<?xml version="1.0" encoding="UTF-8"?><worksheet version="1.0.4" xmlns="http://schemas.mathsoft.com/worksheet10" xmlns:ml="http://schemas.mathsoft.com/math10" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:u="http://schemas.mathsoft.com/units10"><metadata><generator>Mathcad Professional 12.0</generator><userData><title /><description /><author>Mathsoft Engineering &amp; Education, Inc.</author><company>Mathsoft Engineering &amp; Education, Inc.</company><keywords /><revisedBy /></userData><identityInfo><revision>3</revision><documentID>0ddbd43e-a290-497d-9e31-787d2b30d43e</documentID><versionID>0545239a-f641-4397-b58d-1e5fd8d00984</versionID><parentVersionID>00000000-0000-0000-0000-000000000000</parentVersionID><branchID>00000000-0000-0000-0000-000000000000</branchID></identityInfo></metadata><settings><presentation><textRendering><textStyles><textStyle name="Normal"><blockAttr margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit" /><inlineAttr font-family="Verdana" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#000" /></textStyle><textStyle name="Titre 1"><blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="14" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="inherit" /></textStyle><textStyle name="Titre 2"><blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="bold" font-style="italic" underline="false" line-through="false" vertical-align="baseline" color="inherit" /></textStyle><textStyle name="Titre 3"><blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="inherit" /></textStyle><textStyle name="Paragraphe"><blockAttr margin-left="0" margin-right="0" text-indent="21" text-align="left" list-style-type="inherit" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="inherit" /></textStyle><textStyle name="Liste"><blockAttr margin-left="14.25" margin-right="0" text-indent="-14.25" text-align="left" list-style-type="inherit" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="inherit" /></textStyle><textStyle name="Retrait"><blockAttr margin-left="108" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="inherit" /></textStyle><textStyle name="Titre"><blockAttr margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="11" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#808000" /></textStyle><textStyle name="Sous-titre" base-style="Titre"><blockAttr margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit" /><inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#800000" /></textStyle><textStyle name="functions" base-style="Normal"><blockAttr margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit" /><inlineAttr font-family="Times New Roman" font-charset="0" font-size="11" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#800000" /></textStyle><textStyle name="subsubtitle" base-style="Sous-titre"><blockAttr margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit" /><inlineAttr font-family="Times New Roman" font-charset="0" font-size="11" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#800000" /></textStyle></textStyles></textRendering><mathRendering equation-color="#000"><operators multiplication="narrow-dot" derivative="derivative" literal-subscript="large" definition="colon-equal" global-definition="triple-equal" local-definition="left-arrow" equality="bold-equal" /><mathStyles><mathStyle name="Variables" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" color="inherit" /><mathStyle name="Constantes" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" color="inherit" /><mathStyle name="Utilisateur 1" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" color="inherit" /><mathStyle name="Utilisateur 2" font-family="Courier New" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" color="inherit" /><mathStyle name="Utilisateur 3" font-family="Arial" font-charset="0" font-size="10" font-weight="bold" font-style="normal" underline="false" color="inherit" /><mathStyle name="Utilisateur 4" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="italic" underline="false" color="inherit" /><mathStyle name="Utilisateur 5" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" color="inherit" /><mathStyle name="Utilisateur 6" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" color="inherit" /><mathStyle name="Utilisateur 7" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" color="inherit" /><mathStyle name="Math Text Font" font-family="Times New Roman" font-charset="0" font-size="14" font-weight="normal" font-style="normal" underline="false" color="inherit" /></mathStyles><dimensionNames mass="mass" length="length" time="time" current="current" temperature="temperature" luminosity="luminosity" substance="substance" /><symbolics derivation-steps-style="vertical-insert" show-comments="false" evaluate-in-place="false" /><results><general radix="dec" complex-threshold="10" imaginary-value="i" zero-threshold="15" precision="3" show-trailing-zeros="false" exponential-threshold="3" /><matrix display-style="auto" expand-nested-arrays="false" /><unit format-units="true" simplify-units="true" /></results></mathRendering><pageModel show-page-frame="false" show-header-frame="false" show-footer-frame="false" header-footer-start-page="1" paper-code="1" orientation="portrait" page-width="612" page-height="792"><margins left="86.4" right="86.4" top="86.4" bottom="86.4" /><header use-full-page-width="false" /><footer use-full-page-width="false" /></pageModel><colorModel background-color="#fff" default-highlight-color="#ffff80" /></presentation><calculation><builtInVariables array-origin="0" convergence-tolerance="0.001" constraint-tolerance="0.001" random-seed="120733505" prn-precision="4" prn-col-width="8" /><calculationBehavior automatic-recalculation="true" matrix-strict-singularity-check="true" optimize-expressions="false" exact-boolean="false" strings-use-origin="false" /><units unit-system="si" /></calculation><editor protection-level="none" view-annotations="false" view-regions="false"><ruler is-visible="false" ruler-unit="in"><tabs><tab show-guide="true" position="36" /><tab show-guide="true" position="67.5" /><tab show-guide="true" position="144" /><tab show-guide="true" position="225" /></tabs></ruler><plotTemplate><xy item-idref="1" /></plotTemplate><grid granularity-x="6" granularity-y="6" /></editor><fileFormat image-type="image/png" image-quality="75" save-numeric-results="false" save-text-images="false" /><miscellaneous><handbook handbook-region-tag-ub="2120" can-delete-original-handbook-regions="true" can-delete-user-regions="true" can-print="true" can-copy="true" can-save="true" file-permission-mask="4294967295" /></miscellaneous></settings><regions><region region-id="12" left="6" top="6" width="237" height="34.5" align-x="6" align-y="6" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><png item-idref="2" display-width="235.5" display-height="33" /><link href="./dataTOC.xmcd" title="Links to Table of Contents" popup="false" /><rendering item-idref="3" /></region><region region-id="2117" left="96" top="44.25" width="146.25" height="12" align-x="96" align-y="54" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Titre" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit"><f family="Arial" charset="0"><u><inlineAttr line-through="false"><c val="#808000"><link href="./condition.xmcd" title="Links to Section Contents" popup="false"><sp count="6" />PRECONDITIONING DATA</link></c></inlineAttr></u></f></p></text></region><region region-id="114" left="36" top="72.75" width="89.25" height="13.5" align-x="36" align-y="84" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Sous-titre" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">Linear Prediction</p></text><indexes><phrase>linear prediction</phrase><phrase>time-series, linear prediction</phrase><phrase>prediction, linear</phrase></indexes></region><region region-id="583" left="66" top="97.5" width="102" height="12.75" align-x="66" align-y="108" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="functions" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">predict(vx, prev, next)</p></text></region><region region-id="1955" left="36" top="128.25" width="294" height="84" align-x="36" align-y="138" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">The </c><f family="Verdana" charset="0"><b><inlineAttr line-through="false"><c val="#000">predict</c></inlineAttr></b></f><c val="#000"> function takes a time series of data, y, the number of previous points from the end of the series you wish to use for a prediction, and the number of subsequent points to predict. A</c><f family="Verdana" charset="0"><inlineAttr font-weight="normal" line-through="false"><c val="#000"> linear prediction</c></inlineAttr></f> method returns a vector containing next predicted values of the time series. The data <i>are </i><f family="Verdana" charset="0"><inlineAttr font-style="normal" line-through="false"><c val="#000">assumed</c></inlineAttr></f> to be measured at <f family="Verdana" charset="0"><inlineAttr font-style="normal" line-through="false"><c val="#000">equal time intervals</c></inlineAttr></f>. <c val="#000">Given some time-series data,</c></p></text></region><region region-id="1951" left="66" top="225" width="72.75" height="99.75" align-x="78" align-y="234" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><component clsid-buddy="a6cfc4d6-3a88-437d-94de-30d1a591cf29" item-idref="4" disable-calc="false"><inputs hide="false" /><outputs hide="false"><ml:id>y</ml:id></outputs></component><rendering item-idref="5" /></region><region region-id="1952" left="66" top="339" width="54.75" height="12.75" align-x="74.25" align-y="348" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>i</ml:id><ml:range><ml:real>0</ml:real><ml:apply><ml:id>last</ml:id><ml:id>y</ml:id></ml:apply></ml:range></ml:define></math><rendering item-idref="6" /></region><region region-id="1953" left="42" top="360" width="315.75" height="138" align-x="42" align-y="360" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="7" /><rendering item-idref="8" /></region><region region-id="1954" left="36" top="500.25" width="279.75" height="36" align-x="36" align-y="510" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">the</c><b><c val="#000"> predict</c></b><c val="#000"> function is used to predict or forecast as many future values of the time series being observed as we like.</c></p></text></region><region region-id="1764" left="0" top="546" width="6000" height="6" align-x="0" align-y="546" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1765" left="66" top="555" width="104.25" height="12.75" align-x="95.25" align-y="564" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>pre_y</ml:id><ml:apply><ml:id>predict</ml:id><ml:id>y</ml:id><ml:real>15</ml:real><ml:real>10</ml:real></ml:apply></ml:define></math><rendering item-idref="9" /></region><region region-id="1766" left="66" top="579" width="75" height="12.75" align-x="76.5" align-y="588" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>k</ml:id><ml:range><ml:real>0</ml:real><ml:apply><ml:id>last</ml:id><ml:id>pre_y</ml:id></ml:apply></ml:range></ml:define></math><rendering item-idref="10" /></region><region region-id="1767" left="36" top="606" width="352.5" height="194.25" align-x="36" align-y="606" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="11" /><rendering item-idref="12" /></region><region region-id="1769" left="36" top="800.25" width="317.25" height="24" align-x="36" align-y="810" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Most time-series prediction methods assume that an observed piece of data, say,</p></text></region><region region-id="2098" left="66" top="831" width="13.5" height="18" align-x="72" align-y="840" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>N</ml:id></ml:apply></math><rendering item-idref="13" /></region><region region-id="1771" left="36" top="854.25" width="308.25" height="24" align-x="36" align-y="864" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">is a direct function of a specific number of data points prior to this point. For example,</p></text></region><region region-id="1772" left="66" top="891" width="111.75" height="18" align-x="84" align-y="900" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>N</ml:id></ml:apply><ml:apply><ml:id>f</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>2</ml:real></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="14" /></region><region region-id="1773" left="36" top="920.25" width="307.5" height="36" align-x="36" align-y="930" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">says that the value of the observation at time N is a function of the three previous observed values. Therefore the next observation</p></text></region><region region-id="1774" left="66" top="963" width="24" height="18" align-x="77.25" align-y="972" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:plus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></math><rendering item-idref="15" /></region><region region-id="1775" left="36" top="986.25" width="90" height="12" align-x="36" align-y="996" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">would be given by</p></text></region><region region-id="1776" left="66" top="1011" width="112.5" height="18" align-x="94.5" align-y="1020" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:plus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:id>f</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>N</ml:id></ml:apply><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="16" /></region><region region-id="1777" left="0" top="1044" width="6000" height="6" align-x="0" align-y="1044" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1778" left="36" top="1052.25" width="278.25" height="24" align-x="36" align-y="1062" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">If, in fact, this prediction function is a linear function — meaning there are constants a, b, and c such that</p></text></region><region region-id="1779" left="66" top="1089" width="213.75" height="18" align-x="158.25" align-y="1098" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:id>f</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>2</ml:real></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:id>a</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>b</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>c</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="17" /></region><region region-id="1780" left="36" top="1118.25" width="33.75" height="12" align-x="36" align-y="1128" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">. . . so</p></text></region><region region-id="1781" left="66" top="1143" width="139.5" height="18" align-x="84" align-y="1152" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>N</ml:id></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:id>a</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>b</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>c</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="18" /></region><region region-id="1782" left="66" top="1173" width="140.25" height="18" align-x="94.5" align-y="1182" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:plus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:id>a</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>N</ml:id></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>b</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>c</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="19" /></region><region region-id="1783" left="66" top="1203" width="141" height="18" align-x="94.5" align-y="1212" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:plus /><ml:id>N</ml:id><ml:real>2</ml:real></ml:apply></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:id>a</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:plus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>b</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>N</ml:id></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:id>c</ml:id><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:apply><ml:minus /><ml:id>N</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="20" /></region><region region-id="1784" left="36" top="1232.25" width="297" height="24" align-x="36" align-y="1242" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">and so on — we say the prediction method is a </c><f family="Verdana" charset="0"><inlineAttr font-weight="normal" line-through="false"><c val="#000">linear prediction method. </c></inlineAttr></f></p></text></region><region region-id="1813" left="36" top="1268.25" width="278.25" height="36" align-x="36" align-y="1278" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Using greater or fewer numbers of points from the time series will produce different predicted values since we are changing the prediction model.</p></text></region><region region-id="1816" left="36" top="1316.25" width="273" height="36" align-x="36" align-y="1326" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">The following setup allows you to play with these two parameters and study their effect on the randomly generated time-series data:</p></text></region><region region-id="1817" left="66" top="1371" width="42" height="12.75" align-x="91.5" align-y="1380" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Num</ml:id><ml:real>15</ml:real></ml:define></math><rendering item-idref="21" /></region><region region-id="1958" left="144" top="1371" width="47.25" height="12.75" align-x="152.25" align-y="1380" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>i</ml:id><ml:range><ml:real>0</ml:real><ml:id>Num</ml:id></ml:range></ml:define></math><rendering item-idref="22" /></region><region region-id="1957" left="228" top="1364.25" width="109.5" height="27.75" align-x="240" align-y="1380" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:apply><ml:indexer /><ml:id>z</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:div /><ml:id>i</ml:id><ml:real>10</ml:real></ml:apply><ml:apply><ml:div /><ml:real>3</ml:real><ml:apply><ml:plus /><ml:id>i</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:id>rnd</ml:id><ml:real>0.5</ml:real></ml:apply></ml:apply></ml:define></math><rendering item-idref="23" /></region><region region-id="1820" left="66" top="1395" width="43.5" height="12.75" align-x="97.5" align-y="1404" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Nprev</ml:id><ml:real>3</ml:real></ml:define></math><rendering item-idref="24" /></region><region region-id="1959" left="144" top="1395" width="45" height="12.75" align-x="177" align-y="1404" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Nmore</ml:id><ml:real>5</ml:real></ml:define></math><rendering item-idref="25" /></region><region region-id="1822" left="66" top="1419" width="139.5" height="12.75" align-x="97.5" align-y="1428" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>next_z</ml:id><ml:apply><ml:id>predict</ml:id><ml:id>z</ml:id><ml:id>Nprev</ml:id><ml:id>Nmore</ml:id></ml:apply></ml:define></math><rendering item-idref="26" /></region><region region-id="1823" left="66" top="1437" width="57" height="12.75" align-x="76.5" align-y="1446" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>k</ml:id><ml:range><ml:real>1</ml:real><ml:id>Nmore</ml:id></ml:range></ml:define></math><rendering item-idref="27" /></region><region region-id="1825" left="36" top="1458" width="327.75" height="147.75" align-x="36" align-y="1458" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="28" /><rendering item-idref="29" /></region><region region-id="1826" left="0" top="1614" width="6000" height="6" align-x="0" align-y="1614" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1827" left="36" top="1627.5" width="88.5" height="12.75" align-x="36" align-y="1638" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="subsubtitle" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">How predict Works</p></text><indexes><phrase>how predict works</phrase><phrase>predict, how predict works</phrase></indexes></region><region region-id="1828" left="36" top="1652.25" width="249.75" height="12" align-x="36" align-y="1662" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Here we outline what's going on inside of </c><b><c val="#000">predict</c></b><f family="Verdana" charset="0"><inlineAttr font-weight="normal" line-through="false"><c val="#000">.</c></inlineAttr></f></p></text></region><region region-id="1829" left="36" top="1676.25" width="116.25" height="12" align-x="36" align-y="1686" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Let's say we have data</p></text></region><region region-id="1830" left="66" top="1711.5" width="46.5" height="111.75" align-x="79.5" align-y="1770" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>X</ml:id><ml:matrix rows="7" cols="1"><ml:real>2.7</ml:real><ml:real>5.7</ml:real><ml:real>6.8</ml:real><ml:real>2.9</ml:real><ml:real>5.1</ml:real><ml:real>6.2</ml:real><ml:real>2.5</ml:real></ml:matrix></ml:define></math><rendering item-idref="30" /></region><region region-id="1831" left="36" top="1844.25" width="158.25" height="12" align-x="36" align-y="1854" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">and </c><b><c val="#000">predict</c></b><c val="#000"> is told to estimate </c></p></text></region><region region-id="1832" left="66" top="1869" width="53.25" height="12.75" align-x="107.25" align-y="1878" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Npredict</ml:id><ml:real>4</ml:real></ml:define></math><rendering item-idref="31" /></region><region region-id="1833" left="36" top="1892.25" width="170.25" height="12" align-x="36" align-y="1902" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">future values using a model where</p></text></region><region region-id="1834" left="66" top="1917" width="44.25" height="12.75" align-x="98.25" align-y="1926" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Nprior</ml:id><ml:real>3</ml:real></ml:define></math><rendering item-idref="32" /></region><region region-id="1835" left="36" top="1940.25" width="205.5" height="12" align-x="36" align-y="1950" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">previous values are used in the estimate.</p></text></region><region region-id="1836" left="36" top="1964.25" width="285.75" height="36" align-x="36" align-y="1974" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">The number of previous values, and the fact that we are using linear prediction, now tells </c><b><c val="#000">predict</c></b><c val="#000"> that it is looking for three coefficients,</c></p></text></region><region region-id="1837" left="66" top="2007" width="36.75" height="18" align-x="90" align-y="2016" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:sequence><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply></ml:sequence></math><rendering item-idref="33" /></region><region region-id="1838" left="66" top="2037" width="120" height="18" align-x="83.25" align-y="2046" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>3</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="34" /></region><region region-id="1839" left="66" top="2067" width="120" height="18" align-x="83.25" align-y="2076" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>4</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="35" /></region><region region-id="1840" left="66" top="2090.25" width="20.25" height="12" align-x="66" align-y="2100" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">etc.</p></text></region><region region-id="2100" left="36" top="2114.25" width="309" height="12" align-x="36" align-y="2124" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">that can be used to predict new values for the series data.</p></text></region><region region-id="1843" left="36" top="2138.25" width="268.5" height="24" align-x="36" align-y="2148" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Now values for the c's such that the observed data </c><i><c val="#000">exactly</c></i><c val="#000"> satisfies</c></p></text></region><region region-id="1844" left="66" top="2175" width="120" height="18" align-x="83.25" align-y="2184" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>3</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="36" /></region><region region-id="1845" left="66" top="2199" width="120" height="18" align-x="83.25" align-y="2208" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>4</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="37" /></region><region region-id="1846" left="66" top="2222.25" width="20.25" height="12" align-x="66" align-y="2232" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">etc.</p></text></region><region region-id="1847" left="36" top="2246.25" width="279" height="24" align-x="36" align-y="2256" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">most likely do not exist, so we need to find approximate values for such c's. Here's what </c><b><c val="#000">predict</c></b><c val="#000"> does:</c></p></text></region><region region-id="2111" left="0" top="2274" width="6000" height="6" align-x="0" align-y="2274" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1848" left="36" top="2282.25" width="106.5" height="12" align-x="36" align-y="2292" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Using the relationship</p></text></region><region region-id="1849" left="66" top="2307" width="149.25" height="18" align-x="83.25" align-y="2316" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:apply><ml:minus /><ml:id>k</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:apply><ml:minus /><ml:id>k</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:apply><ml:minus /><ml:id>k</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></ml:apply></math><rendering item-idref="38" /></region><region region-id="1850" left="36" top="2336.25" width="273.75" height="27" align-x="36" align-y="2346" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">you can establish the system of three equations in the three unknowns, c</c><f family="Times New Roman" charset="0" size="12"><inlineAttr line-through="false"><c val="#000"><sub>0</sub></c></inlineAttr></f><c val="#000">, c</c><f family="Times New Roman" charset="0" size="12"><inlineAttr line-through="false"><c val="#000"><sub>1</sub></c></inlineAttr></f><c val="#000"><f size="8">,</f></c><c val="#000"> and c</c><f family="Times New Roman" charset="0" size="12"><inlineAttr line-through="false"><c val="#000"><sub>2</sub></c></inlineAttr></f><f size="8"><c val="#000">,</c></f></p></text></region><region region-id="1851" left="66" top="2378.25" width="108" height="18.75" align-x="158.25" align-y="2388" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></math><rendering item-idref="39" /></region><region region-id="1852" left="66" top="2408.25" width="108" height="18.75" align-x="158.25" align-y="2418" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></math><rendering item-idref="40" /></region><region region-id="1853" left="66" top="2438.25" width="108" height="18.75" align-x="158.25" align-y="2448" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:apply><ml:equal /><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>φ</ml:id><ml:real>3</ml:real></ml:apply></ml:apply></math><rendering item-idref="41" /></region><region region-id="2108" left="36" top="2468.25" width="282.75" height="12" align-x="36" align-y="2478" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">where the <f family="Symbol" charset="2" size="10"><inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"><c val="#000">f</c></inlineAttr></f> values are the autocorrelations.</p></text></region><region region-id="1855" left="36" top="2492.25" width="295.5" height="39" align-x="36" align-y="2502" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Using a technique known as </c><f family="Verdana" charset="0"><inlineAttr font-weight="normal" line-through="false"><c val="#000">Burg's method</c></inlineAttr></f><c val="#000">, </c><b><c val="#000">predict</c></b><c val="#000"> computes estimates for the </c><f family="Symbol" charset="2"><c val="#000">f</c></f><f family="Times New Roman" charset="0" size="12"><inlineAttr line-through="false"><c val="#000"><sub>j</sub></c></inlineAttr></f><c val="#000"> values, solves the system of equations above, and yeilds the prediction coefficients: </c></p></text></region><region region-id="1856" left="66" top="2554.5" width="116.25" height="45.75" align-x="76.5" align-y="2580" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedBIUnit"><ml:id>c</ml:id><ml:matrix rows="3" cols="1"><ml:real>0.8814224392928995</ml:real><ml:real>-0.4176384293009959</ml:real><ml:real>0.5225643330159371</ml:real></ml:matrix></ml:define></math><rendering item-idref="42" /></region><region region-id="2110" left="36" top="2618.25" width="293.25" height="24" align-x="36" align-y="2628" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">This is all done internally and you never need to know these values.</p></text></region><region region-id="1859" left="36" top="2654.25" width="292.5" height="36" align-x="36" align-y="2664" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Having the coefficients in hand, </c><b><c val="#000">predict</c></b><c val="#000"> can now estimate future values. The next </c><b><c val="#000">X-</c></b><c val="#000">value is approximated by the combination of previous </c><b><c val="#000">X-</c></b><c val="#000">values</c></p></text></region><region region-id="1960" left="66" top="2703" width="163.5" height="18" align-x="126" align-y="2712" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>4</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>5</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>6</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="43" /></region><region region-id="2102" left="66" top="2733" width="97.5" height="12.75" align-x="120.75" align-y="2742" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>Predicted_X</ml:id></ml:eval></math><rendering item-idref="44" /></region><region region-id="1962" left="36" top="2756.25" width="275.25" height="12" align-x="36" align-y="2766" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">and this is the first value you see returned by </c><b><c val="#000">predict</c></b><c val="#000">:</c></p></text></region><region region-id="1863" left="66" top="2786.25" width="165.75" height="62.25" align-x="184.5" align-y="2820" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:id>X</ml:id><ml:id>Nprior</ml:id><ml:id>Npredict</ml:id></ml:apply></ml:eval></math><rendering item-idref="45" /></region><region region-id="2112" left="0" top="2862" width="6000" height="6" align-x="0" align-y="2862" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1864" left="36" top="2870.25" width="293.25" height="48" align-x="36" align-y="2880" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Now, how does it get the second predicted value?<sp count="2" />We've just used the last three observed data values. The answer is the predicted value just computed is added to our data set,</p></text></region><region region-id="1865" left="66" top="2935.5" width="51" height="111.75" align-x="78.75" align-y="2994" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>X</ml:id></ml:eval></math><rendering item-idref="46" /></region><region region-id="2118" left="132" top="2985" width="92.25" height="18" align-x="191.25" align-y="2994" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply></ml:eval></math><rendering item-idref="47" /></region><region region-id="1868" left="36" top="3062.25" width="288" height="24" align-x="36" align-y="3072" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">and is now treated like just another piece of data. So the second predicted value is</p></text></region><region region-id="1869" left="66" top="3099" width="205.5" height="18" align-x="126" align-y="3108" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>5</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>6</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="48" /></region><region region-id="2104" left="66" top="3138.75" width="102" height="29.25" align-x="120.75" align-y="3156" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>Predicted_X</ml:id></ml:eval></math><rendering item-idref="49" /></region><region region-id="1871" left="66" top="3176.25" width="165.75" height="62.25" align-x="184.5" align-y="3210" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:id>X</ml:id><ml:id>Nprior</ml:id><ml:id>Npredict</ml:id></ml:apply></ml:eval></math><rendering item-idref="50" /></region><region region-id="1872" left="36" top="3254.25" width="288.75" height="24" align-x="36" align-y="3264" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><b><c val="#000">predict</c></b><c val="#000"> continues in this fashion until it has produced the requested number of estimated values:</c></p></text></region><region region-id="1873" left="66" top="3297" width="247.5" height="18" align-x="126" align-y="3306" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>6</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="51" /></region><region region-id="1874" left="66" top="3327" width="289.5" height="18" align-x="126" align-y="3336" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>3</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="52" /></region><region region-id="1875" left="66" top="3362.25" width="102" height="62.25" align-x="120.75" align-y="3396" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>Predicted_X</ml:id></ml:eval></math><rendering item-idref="53" /></region><region region-id="2119" left="0" top="3438" width="6000" height="6" align-x="0" align-y="3438" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="2105" left="66" top="3446.25" width="165.75" height="62.25" align-x="184.5" align-y="3480" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:id>X</ml:id><ml:id>Nprior</ml:id><ml:id>Npredict</ml:id></ml:apply></ml:eval></math><rendering item-idref="54" /></region><region region-id="1878" left="36" top="3518.25" width="293.25" height="24" align-x="36" align-y="3528" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Look at the sequence of calculations </c><b><c val="#000">predict</c></b><c val="#000"> performed to produce the estimates:</c></p></text></region><region region-id="1879" left="66" top="3561" width="163.5" height="18" align-x="126" align-y="3570" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>4</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>5</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>6</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="55" /></region><region region-id="1880" left="66" top="3591" width="205.5" height="18" align-x="126" align-y="3600" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>5</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>6</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="56" /></region><region region-id="1881" left="66" top="3621" width="247.5" height="18" align-x="126" align-y="3630" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>X</ml:id><ml:real>6</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="57" /></region><region region-id="1882" left="66" top="3651" width="289.5" height="18" align-x="126" align-y="3660" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>3</ml:real></ml:apply><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>0</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>0</ml:real></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>1</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:indexer /><ml:id>c</ml:id><ml:real>2</ml:real></ml:apply><ml:apply><ml:indexer /><ml:id>Predicted_X</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="58" /></region><region region-id="1967" left="36" top="3680.25" width="291" height="75" align-x="36" align-y="3690" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">As you keep predicting, you are using only predicted values to compute new predicted values. For Predicted_X, the smallest index that appears on X is 4, since we only use the previous three values to predict. Does this mean the values X<f family="Times New Roman" charset="0" size="12"><sub>0</sub></f>, X<f family="Times New Roman" charset="0" size="12"><sub>1</sub></f>, X<f family="Times New Roman" charset="0" size="12"><sub>2</sub></f>, and X<f family="Times New Roman" charset="0" size="12"><sub>3</sub></f> are irrelevant to <b>predict</b>?</p></text></region><region region-id="1888" left="36" top="3764.25" width="271.5" height="36" align-x="36" align-y="3774" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">The answer is </c><f family="Verdana" charset="0"><inlineAttr font-style="normal" line-through="false"><c val="#000">no!<sp count="2" /></c></inlineAttr></f><c val="#000">All X-values are used. We can convince you of this by throwing away the first X-value and repeating the </c><b><c val="#000">predict</c></b><c val="#000"> process:</c></p></text></region><region region-id="1889" left="66" top="3819" width="162.75" height="12.75" align-x="107.25" align-y="3828" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Xsmaller</ml:id><ml:apply><ml:id>submatrix</ml:id><ml:id>X</ml:id><ml:real>1</ml:real><ml:apply><ml:id>last</ml:id><ml:id>X</ml:id></ml:apply><ml:real>0</ml:real><ml:real>0</ml:real></ml:apply></ml:define></math><rendering item-idref="59" /></region><region region-id="2095" left="144" top="3861.75" width="78.75" height="95.25" align-x="184.5" align-y="3912" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>Xsmaller</ml:id></ml:eval></math><rendering item-idref="60" /></region><region region-id="1890" left="66" top="3859.5" width="51" height="111.75" align-x="78.75" align-y="3918" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>X</ml:id></ml:eval></math><rendering item-idref="61" /></region><region region-id="2120" left="0" top="3978" width="6000" height="6" align-x="0" align-y="3978" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1892" left="66" top="3980.25" width="165.75" height="62.25" align-x="184.5" align-y="4014" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:id>X</ml:id><ml:id>Nprior</ml:id><ml:id>Npredict</ml:id></ml:apply></ml:eval></math><rendering item-idref="62" /></region><region region-id="1893" left="66" top="4052.25" width="193.5" height="62.25" align-x="212.25" align-y="4086" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:id>Xsmaller</ml:id><ml:id>Nprior</ml:id><ml:id>Npredict</ml:id></ml:apply></ml:eval></math><rendering item-idref="63" /></region><region region-id="1894" left="36" top="4124.25" width="300.75" height="36" align-x="36" align-y="4134" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Note the different results. The reason is that </c><i><c val="#000">all</c></i><c val="#000"> data values are used internally to compute the </c><f family="Symbol" charset="2"><c val="#000">f</c></f><c val="#000"> values used internally. Different </c><f family="Symbol" charset="2"><c val="#000">f</c></f><c val="#000">'s produce different c's!</c></p></text></region><region region-id="1896" left="36" top="4183.5" width="60" height="12.75" align-x="36" align-y="4194" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="subsubtitle" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">Using predict</p></text><indexes><phrase>using predict</phrase><phrase>predict, using</phrase></indexes></region><region region-id="1898" left="36" top="4208.25" width="288.75" height="36" align-x="36" align-y="4218" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">The error messages emitted by </c><b><c val="#000">predict</c></b><c val="#000"> are basically ones that warn against grossly wrong types of arguments, b</c>ut one error message related to the algorithm itself is</p></text></region><region region-id="1902" left="66" top="4272" width="101.25" height="78.75" align-x="150" align-y="4314" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math error="must be less than the number of data points" optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:matrix rows="5" cols="1"><ml:real>2.7</ml:real><ml:real>8.2</ml:real><ml:real>1.9</ml:real><ml:real>0.3</ml:real><ml:real>2.7</ml:real></ml:matrix><ml:real>5</ml:real><ml:real>3</ml:real></ml:apply></ml:eval></math><rendering item-idref="64" /></region><region region-id="1903" left="36" top="4370.25" width="291.75" height="36" align-x="36" align-y="4380" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">So you cannot ask that the predicted values be a linear function of all the data points. You can use up to </c><i><c val="#000">one less than</c></i><c val="#000"> the number of data points:</c></p></text></region><region region-id="1904" left="66" top="4428" width="131.25" height="78.75" align-x="150" align-y="4470" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:matrix rows="5" cols="1"><ml:real>2.7</ml:real><ml:real>8.2</ml:real><ml:real>1.9</ml:real><ml:real>0.3</ml:real><ml:real>2.7</ml:real></ml:matrix><ml:real>4</ml:real><ml:real>3</ml:real></ml:apply></ml:eval></math><rendering item-idref="65" /></region><region region-id="1905" left="36" top="4526.25" width="267" height="24" align-x="36" align-y="4536" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">We recommend choosing a value that is not too large relative to the amount of data.</p></text></region><region region-id="1906" left="0" top="4560" width="6000" height="6" align-x="0" align-y="4560" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1907" left="36" top="4568.25" width="308.25" height="48" align-x="36" align-y="4578" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Another interesting point is that </c><b><c val="#000">predict</c></b><c val="#000"> works very well if the data are periodic. This is not surprising, since the autocorrelation tells you a lot if the data are periodic, and </c><b><c val="#000">predict</c></b><c val="#000"> depends on the autocorrelation.</c></p></text></region><region region-id="1908" left="36" top="4628.25" width="240" height="12" align-x="36" align-y="4638" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">For example, look at the following periodic data:</p></text></region><region region-id="1909" left="66" top="4653" width="41.25" height="12.75" align-x="74.25" align-y="4662" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>i</ml:id><ml:range><ml:real>0</ml:real><ml:real>100</ml:real></ml:range></ml:define></math><rendering item-idref="66" /></region><region region-id="1910" left="66" top="4676.25" width="152.25" height="27.75" align-x="79.5" align-y="4692" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:apply><ml:indexer /><ml:id>P</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:plus /><ml:apply><ml:id>sin</ml:id><ml:apply><ml:mult style="default" /><ml:apply><ml:mult style="default" /><ml:real>12</ml:real><ml:id>π</ml:id></ml:apply><ml:apply><ml:div /><ml:id>i</ml:id><ml:real>100</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:id>cos</ml:id><ml:apply><ml:mult style="default" /><ml:apply><ml:mult style="default" /><ml:real>6</ml:real><ml:id>π</ml:id></ml:apply><ml:apply><ml:div /><ml:id>i</ml:id><ml:real>100</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="67" /></region><region region-id="1911" left="36" top="4722" width="300.75" height="135.75" align-x="36" align-y="4722" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="68" /><rendering item-idref="69" /></region><region region-id="1912" left="36" top="4868.25" width="263.25" height="24" align-x="36" align-y="4878" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">We can compare the result of using </c><b><c val="#000">predict </c></b><c val="#000">with our knowledge of how the data were generated:</c></p></text></region><region region-id="1913" left="66" top="4911" width="52.5" height="12.75" align-x="76.5" align-y="4920" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>k</ml:id><ml:range><ml:real>101</ml:real><ml:real>105</ml:real></ml:range></ml:define></math><rendering item-idref="70" /></region><region region-id="2096" left="228" top="4904.25" width="132" height="113.25" align-x="293.25" align-y="4920" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:plus /><ml:apply><ml:id>sin</ml:id><ml:apply><ml:mult style="default" /><ml:apply><ml:mult style="default" /><ml:real>12</ml:real><ml:id>π</ml:id></ml:apply><ml:apply><ml:div /><ml:id>k</ml:id><ml:real>100</ml:real></ml:apply></ml:apply></ml:apply><ml:apply><ml:id>cos</ml:id><ml:apply><ml:mult style="default" /><ml:apply><ml:mult style="default" /><ml:real>6</ml:real><ml:id>π</ml:id></ml:apply><ml:apply><ml:div /><ml:id>k</ml:id><ml:real>100</ml:real></ml:apply></ml:apply></ml:apply></ml:apply></ml:eval><resultFormat><general radix="dec" complex-threshold="10" precision="3" show-trailing-zeros="false" exponential-threshold="3" /><matrix display-style="auto" expand-nested-arrays="false" /><unit format-units="false" simplify-units="false" /><table item-idref="71" /></resultFormat></math><rendering item-idref="72" /></region><region region-id="1915" left="66" top="4944" width="129" height="78.75" align-x="134.25" align-y="4986" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:apply><ml:id>predict</ml:id><ml:id>P</ml:id><ml:real>10</ml:real><ml:real>5</ml:real></ml:apply></ml:eval><resultFormat><general radix="dec" complex-threshold="10" precision="6" show-trailing-zeros="false" exponential-threshold="3" /><matrix display-style="auto" expand-nested-arrays="false" /><unit format-units="false" simplify-units="false" /></resultFormat></math><rendering item-idref="73" /></region><region region-id="1916" left="36" top="5042.25" width="182.25" height="12" align-x="36" align-y="5052" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">We see that </c><b><c val="#000">predict</c></b><c val="#000"> does very well.</c></p></text></region><region region-id="1969" left="0" top="5064" width="6000" height="6" align-x="0" align-y="5064" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="2074" left="36" top="5078.25" width="285.75" height="24" align-x="36" align-y="5088" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">In fact, periodic data are what </c><b><c val="#000">predict</c></b><c val="#000"> handles best. For example, take a look at a case of linear data:</c></p></text></region><region region-id="2075" left="66" top="5115" width="36.75" height="12.75" align-x="74.25" align-y="5124" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>i</ml:id><ml:range><ml:real>0</ml:real><ml:real>25</ml:real></ml:range></ml:define></math><rendering item-idref="74" /></region><region region-id="2076" left="198" top="5115" width="21" height="18" align-x="209.25" align-y="5124" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:apply><ml:indexer /><ml:id>t</ml:id><ml:id>i</ml:id></ml:apply><ml:id>i</ml:id></ml:define></math><rendering item-idref="75" /></region><region region-id="2077" left="66" top="5139" width="85.5" height="12.75" align-x="83.25" align-y="5148" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>PT</ml:id><ml:apply><ml:id>predict</ml:id><ml:id>t</ml:id><ml:real>10</ml:real><ml:real>5</ml:real></ml:apply></ml:define></math><rendering item-idref="76" /></region><region region-id="2078" left="198" top="5139" width="34.5" height="12.75" align-x="208.5" align-y="5148" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>k</ml:id><ml:range><ml:real>1</ml:real><ml:real>5</ml:real></ml:range></ml:define></math><rendering item-idref="77" /></region><region region-id="2079" left="36" top="5160" width="301.5" height="180" align-x="36" align-y="5160" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="78" /><rendering item-idref="79" /></region><region region-id="2080" left="36" top="5348.25" width="256.5" height="12" align-x="36" align-y="5358" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">The </c><b><c val="#000">predict</c></b><c val="#000"> predictions seem to fit the given data.</c></p></text></region><region region-id="2081" left="36" top="5372.25" width="285.75" height="36" align-x="36" align-y="5382" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">However, if you try to predict too far ahead with these data or any data, you will run into problems if you are expecting linearity.</p></text></region><region region-id="2082" left="66" top="5427" width="90" height="12.75" align-x="83.25" align-y="5436" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>PT</ml:id><ml:apply><ml:id>predict</ml:id><ml:id>t</ml:id><ml:real>10</ml:real><ml:real>50</ml:real></ml:apply></ml:define></math><rendering item-idref="80" /></region><region region-id="2083" left="66" top="5451" width="39" height="12.75" align-x="76.5" align-y="5460" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>k</ml:id><ml:range><ml:real>1</ml:real><ml:real>50</ml:real></ml:range></ml:define></math><rendering item-idref="81" /></region><region region-id="2084" left="36" top="5478" width="307.5" height="180" align-x="36" align-y="5478" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="82" /><rendering item-idref="83" /></region><region region-id="2085" left="0" top="5658" width="6000" height="6" align-x="0" align-y="5658" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="2086" left="36" top="5666.25" width="165" height="12" align-x="36" align-y="5676" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Now predict much further ahead:</p></text></region><region region-id="2087" left="66" top="5691" width="94.5" height="12.75" align-x="83.25" align-y="5700" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>PT</ml:id><ml:apply><ml:id>predict</ml:id><ml:id>t</ml:id><ml:real>10</ml:real><ml:real>100</ml:real></ml:apply></ml:define></math><rendering item-idref="84" /></region><region region-id="2088" left="66" top="5715" width="43.5" height="12.75" align-x="76.5" align-y="5724" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>k</ml:id><ml:range><ml:real>1</ml:real><ml:real>100</ml:real></ml:range></ml:define></math><rendering item-idref="85" /></region><region region-id="2089" left="36" top="5736" width="309.75" height="147.75" align-x="36" align-y="5736" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="86" /><rendering item-idref="87" /></region><region region-id="2090" left="36" top="5894.25" width="285" height="36" align-x="36" align-y="5904" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">We see that </c><b><c val="#000">predict</c></b><c val="#000"> essentially forces periodicity. If you are predicting this far ahead, you're asking for trouble anyway!</c></p></text></region><region region-id="1968" left="36" top="5953.5" width="107.25" height="12.75" align-x="36" align-y="5964" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="subsubtitle" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">Estimating prior values</p></text><indexes><phrase>estimating prior values</phrase><phrase>prior values, estimating</phrase></indexes></region><region region-id="1934" left="36" top="5978.25" width="282" height="36" align-x="36" align-y="5988" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">The </c><b><c val="#000">predict</c></b><c val="#000"> function can also be used to estimate </c><i><c val="#000">prior</c></i><c val="#000"> values of a set of time-series data. All you need to do is change your perspective for a while.</c></p></text></region><region region-id="1935" left="36" top="6026.25" width="159" height="12" align-x="36" align-y="6036" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">Consider the following data set:</c></p></text></region><region region-id="1936" left="66" top="6051.75" width="254.25" height="18" align-x="79.5" align-y="6066" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>X</ml:id><ml:apply><ml:transpose /><ml:matrix rows="1" cols="10"><ml:real>0.81</ml:real><ml:real>1.07</ml:real><ml:real>1.83</ml:real><ml:real>2.25</ml:real><ml:real>3.01</ml:real><ml:real>2.91</ml:real><ml:real>1.87</ml:real><ml:real>1.53</ml:real><ml:real>1.40</ml:real><ml:real>1.05</ml:real></ml:matrix></ml:apply></ml:define></math><rendering item-idref="88" /></region><region region-id="1937" left="66" top="6081" width="57" height="12.75" align-x="74.25" align-y="6090" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>i</ml:id><ml:range><ml:real>0</ml:real><ml:apply><ml:id>last</ml:id><ml:id>X</ml:id></ml:apply></ml:range></ml:define></math><rendering item-idref="89" /></region><region region-id="1938" left="36" top="6102" width="268.5" height="141.75" align-x="36" align-y="6102" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="90" /><rendering item-idref="91" /></region><region region-id="2097" left="0" top="6246" width="6000" height="6" align-x="0" align-y="6246" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1939" left="36" top="6254.25" width="253.5" height="12" align-x="36" align-y="6264" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"><c val="#000">We can apply </c><b><c val="#000">predict</c></b><c val="#000"> to the reversed set of data,</c></p></text></region><region region-id="1940" left="66" top="6279" width="42" height="12.75" align-x="96" align-y="6288" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Nvals</ml:id><ml:real>3</ml:real></ml:define></math><rendering item-idref="92" /></region><region region-id="1941" left="138" top="6279" width="47.25" height="12.75" align-x="173.25" align-y="6288" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>Nterms</ml:id><ml:real>7</ml:real></ml:define></math><rendering item-idref="93" /></region><region region-id="1942" left="66" top="6303" width="160.5" height="12.75" align-x="77.25" align-y="6312" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>P</ml:id><ml:apply><ml:id>predict</ml:id><ml:apply><ml:id>reverse</ml:id><ml:id>X</ml:id></ml:apply><ml:id>Nterms</ml:id><ml:id>Nvals</ml:id></ml:apply></ml:define></math><rendering item-idref="94" /></region><region region-id="1943" left="66" top="6334.5" width="57.75" height="45.75" align-x="76.5" align-y="6360" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>P</ml:id></ml:eval></math><rendering item-idref="95" /></region><region region-id="1945" left="36" top="6398.25" width="156" height="12" align-x="36" align-y="6408" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">and then reverse these values:</p></text></region><region region-id="1946" left="66" top="6441" width="72.75" height="12.75" align-x="89.25" align-y="6450" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:id>past</ml:id><ml:apply><ml:id>reverse</ml:id><ml:id>P</ml:id></ml:apply></ml:define></math><rendering item-idref="96" /></region><region region-id="1947" left="180" top="6424.5" width="69.75" height="45.75" align-x="202.5" align-y="6450" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:eval><ml:id>past</ml:id></ml:eval></math><rendering item-idref="97" /></region><region region-id="1948" left="36" top="6488.25" width="267.75" height="24" align-x="36" align-y="6498" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><text use-page-width="false" push-down="false" lock-width="true"><p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">We can add these values to our plot, distinguishing them as follows:</p></text></region><region region-id="1949" left="66" top="6525" width="64.5" height="12.75" align-x="76.5" align-y="6534" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDScalar"><ml:id>k</ml:id><ml:range><ml:apply><ml:neg /><ml:id>Nvals</ml:id></ml:apply><ml:real>-1</ml:real></ml:range></ml:define></math><rendering item-idref="98" /></region><region region-id="1950" left="36" top="6552" width="321.75" height="147.75" align-x="36" align-y="6552" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="99" /><rendering item-idref="100" /></region><region region-id="1956" left="6" top="6726" width="427.5" height="5.25" align-x="6" align-y="6726" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><png item-idref="101" display-width="435" display-height="3.75" /><rendering item-idref="102" /></region></regions><binaryContent><item item-id="1" content-encoding="gzip">H4sIAAAAAAAA/4zMuw6CMBgF4P5tuYoyOf8mLjZh4hmIk4mPQErFW0I0yODb66HgbpvTy9fm
ZEIIQhyS+rPCmp/K+tLb57U+P/rODlqMY4sknis7WE9ih8SdOzb31g0TNdMm5AaLmvvXY6s/
j7fUvm+vva+W/m+BHGj+O40PpqDif/2VR0NvXVtW05PxzjmRkcSSjJKsyGjFmgxpDuABh/CQ
I7jgGE6cwCWncMULuOYMHvASHvIKPpYaTfwFAAD//wMAWrX/UEMBAAA=</item><item item-id="2">iVBORw0KGgoAAAANSUhEUgAAAToAAAAsCAIAAAB62OM5AAAAAXNSR0IArs4c6QAAAARnQU1B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=</item><item item-id="3">iVBORw0KGgoAAAANSUhEUgAAATwAAAAuCAIAAAA6DjJ1AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="4" content-encoding="gzip">H4sIAAAAAAAA/+yYfUzVVRjHz70oIlGCEhVBGtGLbjmspkiOQER7QSC8c5T6R8Cdb7wUMSSn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</item><item item-id="5">iVBORw0KGgoAAAANSUhEUgAAAGEAAACFCAIAAADEsQFgAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
BBJJREFUeF7tmutyqzAQg3ve/6HT9tAxjveiNYQwkdV/oQuDv8jrldp/j8fjSz85gR9G+skJ
fAkQJCBGENFDjMQIE8AV0lGZUTv7+lFguPunBj+PsWJfdo8gwWEnCUYsT2tyGC2rl+jLfto+
G52c0dI6anSko7ER1xvzsuzGo+olINg8cq+jlwCCHe2zzsHf73t7434+Or+GV7E+/ybnn7Az
Ov+sK/T42rc69jQeRnYfDD0x/wqTbUTCKHcIw8bfcETIbJcgYQQ10grcIRkivsSm3tizE40M
pNyP5DqymyiaQiKO0ROko330cZmS96NoyyetgHavRU23yGiVnu0OOEVGuc0g3GvHhunkLjHC
SMXoVkaDXfrsj5jkfMWNc/b8y4I7tNcwUjFaiZGdj4YO2DtY68vc8WpPaDHJ+Yo39yM4KOcW
H95O7mkj61+MBP5G8HmV4DverKMkY5uyr/y+v54f1UW0tI5cfUUXmftR3ffnbYuB0VR+VFcQ
1dkfBUB+qNgNTjsFc/FpnsKn1HzFjefa/MvKr51mJr+GEYrRrYw+O1SLDDFGWq5Qz8aoxEiM
MAFc8X4duSFZ/WIbJu3aSM4114vUL/bTNi0jNz+CWh4K3GiFPBvZEEQrH4x+KybX0YCj/+ia
W6u+5fKjKDCx7blVitGuG7cTuYcgTz+aOsKSXk6uo+IolPswckZ4rj1aQTJDHl1+6T4xwpjE
6FZGQ3f87I+Y5HwF9Erzj7ztDu01jF6MVmKUz5DDIG7DgMiIkHuRIf0oWlk3IWD4n4iTGRtM
CJgZJTuoV9lCjGzG1vTlhiH2t7ZP8fQjd69NXWx01upHU4wixfHoqJ6x1St7aiQ9u5ix/eni
f/UgNLcZ8egID8snKuRFMDwxEiNMAFdcqKPPDtWGt8ck5yuUsWFmYiRGmACueL+O6jNklCuR
z5BTDsMNIfn9Wj1j22gmMl/L9zcc7rKjfkHOyGZsbcHWwVpG5P0I7rUKo+0h5DoaFuk28ggE
f8+eOtesWBKaVPmR5iM8tV5XcaHvv+6l3/xkMcLAxehWRsrYAP73+36sh6MV2muYnBitxKg+
Q+aVtH6t7kUi25EnSiR/709MaW/QXBb5CUPYj3IKbvDWjyl+roS71nzFLWe/G5JVgrdVfP+B
vVbcd4vutSRp07n2FMU2HKvstVfNR+Q9e/5oKd1B2I9K654pEiNMS4xuZaSMTRkbFqAYiVFH
gKdnuzOk6znq02a7nSE/SsyE9f3W/a7iRSLf7wYmK3ravn9aUURh1orZiJVMSx1dp1q/yNCP
XB250UcxihseyMwoCqqLu2yVc63/o8hUq+4VR6Kj4nw0uMhdKd0vhj7FM0OetgbhA8QIsxUj
McIEcMWFOqLJ2H5PRkxy+Ypvb02hu53sCpEAAAAASUVORK5CYII=</item><item item-id="6">iVBORw0KGgoAAAANSUhEUgAAAEkAAAARCAIAAAAezBYRAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
APdJREFUSEvlVcsWhSAIrP//aHtYhEAw6nGRtbq3dHQewJpSWmZ9dm6zPsusxI48/oLbXnTN
PKlgQYSes3KDMA8S79v5EDpHRC6dhQBV0Ez8vcVl8DPeVtZyc4QP3UBEoTWPfnobAiQu2rZF
ZumOOJ9PpoEUAVEUGfDi5uREDz+hbo9vj8Z3SpE3YTkU3Gpzwun1cBPO0F8T30mWdr6YAU7z
EdYN9S2D8xxpJzXzRm5hs+nxDUngFbCTrr4MfTUIc51M9iE3nSvCFD/Ms3goTCjeMMJGyk+s
mDMIydFrHOt0g/kYN3MSvDa20UqPwOcV6FT793zDxdoAmvX6oVQIvPIAAAAASUVORK5C
YII=</item><item item-id="7" content-encoding="gzip">H4sIAAAAAAAA/2yTzW7TQBDHZ9ffTtKkoYWWpN3QJqGGgNK0CHozVoREVQmpZ5DlJG4bRJvg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</item><item item-id="8">iVBORw0KGgoAAAANSUhEUgAAAaUAAAC4CAIAAADMj2QIAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="9">iVBORw0KGgoAAAANSUhEUgAAAIsAAAARCAIAAABihsiNAAAAAXNSR0IArs4c6QAAAARnQU1B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=</item><item item-id="10">iVBORw0KGgoAAAANSUhEUgAAAGQAAAARCAIAAACkb55xAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ATtJREFUWEftVlsOgzAMY/c/NGMqy0KcpO5j2pDKF0KNm7i2y2Pf9209JAMHWeshGdjIdWvZ
y4KLBZ6BD1mHbfmyaKV4n4Qa3HSwvNqkwT8JKkNWi/MFGoFBa93Utj7cMDPvZSgpYMabS1a5
iJmOy5rWw+CRybkcG+KBkyN1DGNKUDvSjDY49lO+mM7lo3CdD2K20GgnlFFWAidYGlSfyQhZ
Utv0olkwhaZJhHXVFI3gkEUqKEn3VlOjsvRUKDT0Y+JQF7wqLneLf1cWeidRh6uIDqVLMrrn
5GdWXzR2NNdkOjeYSE5JG1JkYbZFYVS9OHQuGknjcZkQxTbQSu4dijxisGJjTJ7oERpu7j65
fakqirMp24VZOQX9JyAdrmf6TI7hrsq6uONtOYYL87chbtUv0Z1+b7J4dqasfAIrg0mZ6Bpq
eQAAAABJRU5ErkJggg==</item><item item-id="11" content-encoding="gzip">H4sIAAAAAAAA/5RTy27TQBS941cSp2lC2kILhUkfQNwXbpIW2KBhVCFRVULqGhQ5sWkDbRMc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</item><item item-id="12">iVBORw0KGgoAAAANSUhEUgAAAdYAAAEDCAIAAAAHi7KRAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="13">iVBORw0KGgoAAAANSUhEUgAAABIAAAAYCAIAAAB4NzpmAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AHxJREFUOE/dk1EOwCAIQ/X+h2YaDauAwohf82tZfGlpsRJRSZyGJU5JMN3g/zGuAPs4jP1G
0ki+h98mbFx1GVnAAC5gY2Y0P1MQ1rWU+DMdRTCd1rJc5lRi4EUNe9vpI++vMltIYrxAoYcT
SlIPpqsLqTk7+emZ+0neVHsAUdlI8S84GhUAAAAASUVORK5CYII=</item><item item-id="14">iVBORw0KGgoAAAANSUhEUgAAAJUAAAAYCAIAAAB87igeAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="15">iVBORw0KGgoAAAANSUhEUgAAACAAAAAYCAIAAAAUMWhjAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AKtJREFUSEvtVVsOgCAMw/sfGjEkY+7VEYXERL4MsnbrOjhqrWXpagRLV1mKfsnzEyAFdklE
RuWuRcml/o8KGgdF8O8UjH/IAH0RXdq0Q28l6L3h0okdTxuKki7S6YudTH08iRRB3H+T0q4g
ODoCmGJdnxQBnwMhq4CGGgpWPMkegfmI6EmaIKAh90rk+66LtO2gi1I9COysRwFWcLvZHl41
MBz3AELEB75PcAINHcCjtyOL9wAAAABJRU5ErkJggg==</item><item item-id="16">iVBORw0KGgoAAAANSUhEUgAAAJYAAAAYCAIAAACX2ZMdAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="17">iVBORw0KGgoAAAANSUhEUgAAAR0AAAAYCAIAAABIsr4rAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="18">iVBORw0KGgoAAAANSUhEUgAAALoAAAAYCAIAAADCuHBDAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AhJJREFUaEPtmOGSwyAIhHvv/9C59rwxBBFWlEyT0H93Y8P6sbimP9u2vfKTBEACb7vkJwmA
BF7gulyWBD5BlBSSAE4g7cJZvUMcx/e0lYnm0PFy4XuaCfD9PhGNboivsstXidnvLvU1ir5X
46YDV/Zm95zqVSRil0tIVZoVhHQ/XSjEUVO3L+2tgeozxYfPVAfNitvlQlLLpk5DKthl1Ct4
t6jl2Sb1DuEllJXiD1GKralC0c0ngKKadamtbyKQHu4upUAQBVN9aHXKGgkjZWoroiBQPqni
nCxHatiFRTg7HujxzmZ3NIzEHoDVWS2zixF2cUjtwXTYpbej1i6TOvmbUVuY/cdsBhgHyjQo
vMzqDAdYpTWc+ZxJUGK6IXEjStWZ6+M0NDyQXcC9zd8wxB6MVjctNa9TjOx2cMFCoYKXIK3R
LISRaEbz5gGiGZqDNqEQssiaSbViCXby4zLwlaOyF+r8391unL/pMO8cEXsrc7mqeoTC9pa2
EFSE4OVID3ZBhp5Nz6jTfevZqUb/rETcKeCT1PuWQ6oYamtV4bZmPOUUM8XRr/XOAPMh7gWO
6hHDiui/ilSHTvnuIkJh9E9uhqP6yQppTlGApgx2GiGOXLLGgRS1SxuBJoUlW+rdqMzqSmYv
FCae8EOZ2EvSUJEl+9w6P1+P1pfPvxOBtMuduhm+l7RLOOI7FUi73Kmb4Xv5BRIy+n/JF349
AAAAAElFTkSuQmCC</item><item item-id="19">iVBORw0KGgoAAAANSUhEUgAAALsAAAAYCAIAAAAteht9AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="20">iVBORw0KGgoAAAANSUhEUgAAALwAAAAYCAIAAADPpgAEAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="21">iVBORw0KGgoAAAANSUhEUgAAADgAAAARCAIAAAAHR/i3AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ALlJREFUSEvVldsOgCAMQ/H/Pxo14BjtRhQDyXhCrmddh0fOOYVoF2iIlkJQ3mmPB1qMKtzi
252RaIBybysfzQHreNtSaFBKQGsHQEHUpWR8OCslI51Hy2ibe5ygY4W+ZEdvHGRpHLmZw0pl
HgrEACGzAq07Jgq/1N4yd/s0qKf9H7d4VdGVOcvm+dX0BngfcFcpyrdqOE40o8/p+sqj/HB6
NTj26PQbBwD4ORf6/l0Bf6H7Rfp0YxhFTxYTLVNZqHO7AAAAAElFTkSuQmCC</item><item item-id="22">iVBORw0KGgoAAAANSUhEUgAAAD8AAAARCAIAAADlm+POAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AOhJREFUSEvdVdESwyAIc3/eP3e2MojgkNo96Hq9Xh6wTUKgr5xz2vcq7Pe90r7Uz9T8CfsS
/mklPDjBN9R6Lr57XA4Gv+eUKR7BFyqz5ryb9xud62JfhrU/KBvLmvap80E/nnjPZxF8w7zc
K5CpVU40+szfwBEZFMyfFx7tGKhm4mAwVT06E71DHgoPk1MLyEWXvVVLfRhG1v6If+s95gH1
KNzP2JD9sCFPcu+PvgqM1UNT2924t0JsJ8e6hVz9nYPh5uZwxgQMrV254OP9AasT8MrUaWOm
I5WbUgV4cerC/kSXAHyuz/4NIAJUP1IST20AAAAASUVORK5CYII=</item><item item-id="23">iVBORw0KGgoAAAANSUhEUgAAAJIAAAAlCAIAAAAm1icUAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="24">iVBORw0KGgoAAAANSUhEUgAAADoAAAARCAIAAAADsiiKAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AMdJREFUSEvVVVsOwCAI2+5/aLfFSZCCI2qm+Gd8lbbUM6V0BBo33EDjCIT1MUJUuNnAhJ78
vLwejqRil8PNLbgD1opBDggJXg5XwhNzTqrwBkUeL1fspyOVgmWCb/npyDfrZnjXihmEj2mV
q8GBZhBYLYLDxFcLoIdccK231dZUNfGz2NhZRQFisuhBIq2+FCp1s0tyeZOhrbvl+Lnx8hG0
7VbDYBbBgg7uaDVubtlqbd9M8d/gJd6PYIcvw8vuRh/yoDo/H78AONJ4CMNWXNgAAAAASUVO
RK5CYII=</item><item item-id="25">iVBORw0KGgoAAAANSUhEUgAAADwAAAARCAIAAAAOrFjNAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AMBJREFUSEvtlVEOgCAMQ/H+h0YNZo6WLQNNhET+VMC3roUt55yWGwf0ciMtR3xaY23oYmyp
QXw+T1V39DSThi4BnYdY81RYLPY80JoNoauCarfI4chNYC+VLkmvQA5twi5drm2b9mj+Ul7y
V4CAGjR6E5HvCaeSKnmsMeSybMQE/jS4vLp0NSu0gmj5ewzaZ31HaRDVFziiNG84Jjl62gqT
7i9kEXJmPepVwBo8VTEkYxV/u2qu6yOoxQ8dFOrxtB1y4dGuAogWKAAAAABJRU5ErkJg
gg==</item><item item-id="26">iVBORw0KGgoAAAANSUhEUgAAALoAAAARCAIAAADltyGLAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AhlJREFUaEPtWO1uw0AI697/obNOmS7EgPF9KJWq648p2rjDGGOy/hzH8dqfzYDIwFsu+7MZ
EBl4iXE7bDPwt4g2C5sBnYHvl8t7KZ90tAeFHT34XPrtzvYOoGSZiXkmL/AwKxed1hlqhs8C
p9k9SIppv5J68riSIox5Ju9tGIaxDozsZK6x44qgwR56E/lB771hLP6xvJdDWxc9Hdtr1vr5
+Qz/dpGptZE2TOmiVWRIjQcGS8HuoGxl2PUR1l7ul8aJHyFLl6e6xUMhQDI3RXs262ZWlwLp
1gWL2CfOfhNC9FWVYSA7ryFoVcgjh+0bppQJjefihgvD3jfhhhURWRPjUfK2MStVRWi5wIO+
PE1+tmBMSyPlXHcdz2rOehC2IRSlN4aQmRBt1jabvfQ5z2rJm5JXEYHHmTX99krvCQoRL5RL
6S4i49DFECGxOvInAMDlcgZn/JD5DvHrciF5x+SSLQpVLsT3OKG8E6W1ABGc8ZAa4sa+qF7j
CT0J5DLQMEWjPkYcEh7G1+L1nYSl1Y5I+AzBWdfBPGxYOTpgdR5G1pXWwjOg/cyKsooEVCF+
7x+lo/SCD+Wi522woTQr3F5I/zQq8/3ZGF1YS3Aq6ZSY0HgmEep5JxNly332a7q1sMo3gwfS
lYugq2ddwby6hVd10Xhz8a6TJBh8G5xzOIv11eFLBg4u6c2nwA/US14nbq8QC6/eV309A7+d
K9HHFo/hTwAAAABJRU5ErkJggg==</item><item item-id="27">iVBORw0KGgoAAAANSUhEUgAAAEwAAAARCAIAAAD45d1VAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
APlJREFUWEflVtEOhDAIm3/un0/PLcgVRgTJctntwWDcoKXA3GqtZfl1klx+leUZfkr1v0ie
jZlC2OWnTQOKS8MhBcnttlkQLBwj4AeS4srRQ5xfWXx4xt7mRSnFTIHBnSgkpRou3K7NrYjo
yQ3+Cfbw6oNqB1f9IO8HiAEZlbepmvIYSZUtpZsbxFAi5/IQDKXp35dKFklVXg4dSMoquFX5
HSXl/JOUXCRxuo66IiBsWMlRHdqS2v3cEyeb+M3gUS86Nf328CBVaeSAARvkK6+LnB+AgOAz
j3SSZWd3ycCeCSs31nVH7YVIjuzcqJO9dZKNqvGcDCs33AHYg23Ee5FGfgAAAABJRU5ErkJg
gg==</item><item item-id="28" content-encoding="gzip">H4sIAAAAAAAA/1yTzW7TQBDHZ/0Zp0mTlFBIC9mQBNKUtLRGSHAzVoVEVRWpZ6rIiZc20DTB
caKopxzhDbhx6SMg7ogDT8JjIBjvTtqStcaa+XlmZ/ZvOwMADK2Llpa+jveU+HgkTnqDcwOS
tYfmhIOuYrpkr5O0fvdN573oxpJAR9ZreLdGp0N/MFX4LZqN4Ogq8RhtB+2LDfAbux+nruNk
PbDVVBpoupxu5ar5yziOep1xLFTmPloObi7D26/ejDUw5RZGHAlRlMhHyw6la3l+6QfAzLQ9
/ys6npUix3bISaXJcZY8X8NmE9TJYubTbdd9BhnFviu2u+2+eI6bq4qZs+z5yXhhFsV12uft
cfsML8gpfLmAAfKen/6JhamC5xt/sGd+Re0fFvAk7AJuqfBShj2Aosqb5W9THp7Qts7FNG5f
wKrnf/qFD4t36OFqUvUB7qrwmwzZrhSiRCPba3Ta0jo5a/fo2OsyfQfu05HnMZSpdo2rc3ll
3LCi/FlZvVGKSjKqUoFVox7VOjm1h9SsDqAzzd2BR9StPu/WoOLaBonb+F/FJonbWBR3k8St
PiY5NpWILc//nMi0uUW8pWTaJq0xNPTDcT+Z/In8vHOh2z6JguFp+90g6gex+ktqyV8i8V4Q
B+rr25h/hiaaTv/aFlpB+kmUDqa90Su5jyZTsR8cMMpV6y9ewFoH12SRLuTON7fjKOgKd089
akrO88DQyzCWqw46IxFNRFithDgzQ84yBssPIxH2urEIK5PgbCxGGmvqGtdZ09C5wZrM4CZr
aia3kFvcRg48hZxxB7nG08h1voTc4BnkJs8it/gycuA55IzTuP8AAAD//wMAf3vFs4EE
AAA=</item><item item-id="29">iVBORw0KGgoAAAANSUhEUgAAAbUAAADFCAIAAABcnfv/AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="30">iVBORw0KGgoAAAANSUhEUgAAAD4AAACVCAIAAACPeN4+AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="31">iVBORw0KGgoAAAANSUhEUgAAAEcAAAARCAIAAAAABSaiAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
APZJREFUSEvdVssOwzAI6/7/o7NHJkaMYUQlF3qKIiA2NqiPMcbV73ux6vdd/Si93dec1Rwu
ISmzVkJbKusnSirbIguNuTZ0UBUC6JdHpuS5rxhAA+SqamcGcSYmxjMrcK2oW34Jq0u1wlYT
ufFq6gAq6RbViJUA1VD0k5qhhUtvbFOCRD4qMuifA42Zl+5cCQi9POwZ7CrvSvUgxSLbkgVY
IRJvPYAfIC3AnWzEFqtVJ6IVBgRLz0oBZGKq1MB/HXtHMeJA8A8sejoYVk/AJAHeKHqNo4In
FzIfO5p8v4VJTCVh2T+mhqysOUs6eq5IVqtzCE5UfgLtYWg0HuCFHgAAAABJRU5ErkJg
gg==</item><item item-id="32">iVBORw0KGgoAAAANSUhEUgAAADsAAAARCAIAAADscEO0AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ANFJREFUSEvVVVsOwyAM6+5/aLaKKTVxCm7UTgsfVSEPHGPCq7W21RofxLXGVgvurojCiLuY
rQDTtlgSxoohuhuCGTh2uz4K4hLcgUeMZJr1vL/xHGTQ25x98QdNTjkche0SuXEJebqs+QvP
cdynDB3pN6ststXlcafHB4tPQgj92EtBHPKdXlxyOXGIVWH0sACQubsQ84ObRMyArAD+mas/
Idkz0IsGHF4yhOt0HJrCi+jOaq4TJ3H1zfuT3rzXL96DYoivvtgiCzk3leNc9iei3pEcj/CQ
DU3sAAAAAElFTkSuQmCC</item><item item-id="33">iVBORw0KGgoAAAANSUhEUgAAADEAAAAYCAIAAADcXYK1AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AMdJREFUSEvtllkKwCAMRO39D20tStxiMkorKeiXlCzPyUIv772zdgKTteOsAT11O0yQAkcn
SKa6n2gnYL7J6nWvXLsQOiahC0L2hRfDhKCQzdQDQK+fMJXliL0iKMfWThWv9yqbsspXEpRx
m3uTsuFmn9F/FHINNRgxIUPA6qSKRxM2zYSE7m0Qr3UmZCpxgj6a1L9C7VSsZaboOF07FQhp
ODZIHsZRjs06VQtI3j3qfuIbQttqjRctp3RBarHZ5vw/YYJb1OkG2AoejEsYAXUAAAAASUVO
RK5CYII=</item><item item-id="34">iVBORw0KGgoAAAANSUhEUgAAAKAAAAAYCAIAAADyNGFiAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="35">iVBORw0KGgoAAAANSUhEUgAAAKAAAAAYCAIAAADyNGFiAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="36">iVBORw0KGgoAAAANSUhEUgAAAKAAAAAYCAIAAADyNGFiAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="37">iVBORw0KGgoAAAANSUhEUgAAAKAAAAAYCAIAAADyNGFiAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="38">iVBORw0KGgoAAAANSUhEUgAAAMcAAAAYCAIAAADBD35rAAAAAXNSR0IArs4c6QAAAARnQU1B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=</item><item item-id="39">iVBORw0KGgoAAAANSUhEUgAAAJAAAAAZCAIAAABRmzD/AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="40">iVBORw0KGgoAAAANSUhEUgAAAJAAAAAZCAIAAABRmzD/AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="41">iVBORw0KGgoAAAANSUhEUgAAAJAAAAAZCAIAAABRmzD/AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="42">iVBORw0KGgoAAAANSUhEUgAAAJsAAAA9CAIAAAA1RowoAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
BOpJREFUeF7tm+uS2jAMhbdv3jen0ASh6HoU7MSzI350lqBI9vl0ZFi2fx6Px08/fpMCT6Lz
Hk+h5iXvzKa8ExVvnBf0nBZ5FtHGeQHOrYSQegrRGCedWXrP+iUz2MtP18E8QhHkritj+Fuc
uD+4IFcTPdQ+nrL6JTN409TrBtGzW2Rc1ItB1jMvxvSfx3Uu0dSgtCw5LhinmIRxeChyniIx
A35X0AfUJedikD4gg4LT+zOfwBvwsNNEA5kC9h6D00T56Paar0RUB4NEz9l0/NT9hqg+55Er
JoP0Ro8cQrQUo8GUiJobMQ+dvRBuPiQyxhkfaec8Gp+RYs0ICb7I7czeHjyVKIrE6L2Lu7xa
INGPeggnPOYbonjneoJy6fVKkLvMQZfOfC2612dpKlPAVNXD0YPTQiLT2qCltjBQF33aaS+a
7sTP2qBEgLO6i29w3ubRrbCYY1xu/VJ6RcjNxxdpms40HSDq8pXTS0iM3rJ3lxjsOiw21b5Z
xHl4TOpRPFVHVhVoolXFVo9voqsTqq6viVYVWz2+ia5OqLq+JlpVbPX4Jro6oer6mmhVsdXj
m+jqhKrra6JVxVaPb6KrE6qur4lWFVs9vomuTqi6viWI6u9h9Db4b//Fz943KvxbC10iKEr5
kbvmxTxFEMlxoQb/VUrpuxcPFYeq2dCrAekgJihKtXTMlVe2xSO7E91/v0dTovsS1d8ImjvR
QtgbdrLpWjxhvFRvnef6gAya9m5O1LM27jw8MmhDsRMQPCXktgbNyu8t9QHfxTl+8cyI/ZoQ
5UeIeSYTb+QcQk51pPFx8KK1RXLRat5T5LqXeThRs88Cz8ipW7KXB8xLYnYD0p4pUfy8ES1r
dnAAnm8B9LG5Np0nzUzNao4fOdLMuXS7R715oDumStR0kigXHMlmy+7mgP9PgGhTvFzsOtej
35i1dG/gUd5Scdj3RM1aiIPT8766NtP6Jcu9ekXsJ+jB6tEIxotJEriQnBRsUsSYA03n0WzE
jXrcgZnN9cSam9tErCI9igBIY5DCaZIOOKfAgejP349ZvZ+RMk0UUWlSzIfoEyFR9H4GF9FE
QaFmhB2Ivk7U/zb1/gVX0ERBoWaEvd9yv+dt4FS8fBPFtRoeKYmSQbdK/DTFazdRXKvhkf1e
d7ikNydsojcDGF6+iQ6X9OaETfRmAMPLN9Hhkt6csIneDGB4+SY6XNKbE95GNPjq4/U5+P3Y
PxYfn+oAL4wihcz0idn7ioN/pEZitkK6ynYvuAsqxO8yMwddcw9RoZdenxfw/oUI+0Yh/Isy
83cdpJeucu5K2jeCircLc9eCbjoBViQa7MH0AXcA37CH0/OT52O6XuqnwGfBLmgv6UY8tCsS
NXflXRTq8BFnWkdTqQKO50cw3kVneE/1eszWvJRoOvrTqQu2auxCQZevSixAt0VwpUSUigZ3
VS2bHqKvoul0rgbEbXUNUXBagu5H2Jgq8cbaRnGMMAX8S4ji+/ROO1LTlBXJ72UOjglwhMYt
jk/dT/dULYjEB+tAPGrGBINUyFrVaBJRPb1jH4OHep4EIVSNSQevmD/kAH2d24ve73CrebYz
Rxyfn+JGUUjU4mPcWyQ1lhnANdS1PJzeLgSRQxNXaYHx+LgAE3aYp4AcBvOUaqjztBVv/Q4D
YGrVhnq9vOM/vUzdQydPFfgH5HpY+hu+kJQAAAAASUVORK5CYII=</item><item item-id="43">iVBORw0KGgoAAAANSUhEUgAAANoAAAAYCAIAAAATX3QzAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="44">iVBORw0KGgoAAAANSUhEUgAAAIIAAAARCAIAAACek+NHAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="45">iVBORw0KGgoAAAANSUhEUgAAAN0AAABTCAIAAACUHJXlAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="46">iVBORw0KGgoAAAANSUhEUgAAAEQAAACVCAIAAABuE8tvAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="47">iVBORw0KGgoAAAANSUhEUgAAAHsAAAAYCAIAAABVxRXcAAAAAXNSR0IArs4c6QAAAARnQU1B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=</item><item item-id="48">iVBORw0KGgoAAAANSUhEUgAAARIAAAAYCAIAAAC5ueWmAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="49">iVBORw0KGgoAAAANSUhEUgAAAIgAAAAnCAIAAABbkpc+AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="50">iVBORw0KGgoAAAANSUhEUgAAAN0AAABTCAIAAACUHJXlAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="51">iVBORw0KGgoAAAANSUhEUgAAAUoAAAAYCAIAAAATeiMaAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
A1VJREFUeF7tWotyKjEItX/un1t1FREIj5BdY8TptNsOIXA4cNi59+9yuZzqUwgUAksicG3v
+hQChcCSCJyWzKqSKgQKgdtiXigUAoXAqgg82pu8d3Rke/WwnYKHDidwRHECocJ15C+Ze/vO
ThVSlTJZxA3ADicTtsArDZxSND0nIk63pjfix+m2o2D+I1OFVKX0Fw5brofbmPZ2ira/D03L
sZOyjw3kVCYkM99QhBmaflcpCze9XkZ7b/Bh4nKZ4jZ4xwYp5is0V2lsbBLaFPlW+5met5Sj
/uGUx380tlA8YnuvVErPSxx5d/MUZT3c3tqbcIj/CpDxB2APFzFR1nQz51R2mpEFDPJqNfDx
OqwnEo0HJyiy3AM+BkexJ9O/RQyzuzpKaS4ahZus3uJ0FEnDLRVMyXRsjQ+zbJiyJm86DDxU
E22igk8GjTlulFxE5SFzjW9V0cmbIYYevAdzMX7ColbKrdvXxi3W3spKqQ94MtTFBjaBFisX
ogXsz3xURWnB6RLVCv+NHTkq4fnB909qDzHMBoumacpA1GFrZItDTaH0PLj1tLfYyaA8+uIH
JdHNdBkkZRCXgg7R1jeR7pA8u4ZORF1j9U5u6S1hJ77ii0oZxU2R+tawEFVn5hbAidB/9yaS
QjjNKU6oQODTj5vexBYVr+Bl44UPzXIcm1hgBSgIRpniitwpWZPt1IMPHi58DvIs8PwlICjH
Dy6lIqccWOdwFDtf5BVXl+0v8P2DuG1hvFLOqNzMZ0PNrCditrf/+EDEBiY4MKpRrpKYm2H8
CHpP9T6jLX2J54H1y1ANT3STcyGDgQmG7j3GOIO5J8IfQe++VJxP16/HFjfT823JuMfz+ERi
G1i/DNXInuZhntNmYILOGzNmzxK+/VQcZjD3xPkt6EVxg3exx8PW3qt97/o/wy1aZKiWOfuR
nd/TGwfY7IcbvCQfkMWnrgD0JtXtzE6xaf5AZDNUy5yt9iZaNKSm36LbmWSF9gYBhy0dLsDd
MvkzSmxYh2daNHO22nt4e+9Bj0wf7nT2leZOF3zELXlRGRgDeO7wmTmrvC/gt/qOqOY/Mhy3
/egxFZgYt2ESN1WGFUwhUAjc9vFCoRAoBFZFoNp71cpWXoVAqXdxoBBYF4F/bqneM2+lDdMA
AAAASUVORK5CYII=</item><item item-id="52">iVBORw0KGgoAAAANSUhEUgAAAYIAAAAYCAIAAAB4Em1PAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="53">iVBORw0KGgoAAAANSUhEUgAAAIgAAABTCAIAAADLIdjpAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
BDFJREFUeF7tm+uS2zAIhdv3f+g0M+5qFUBwQMjtJmd/bWKEpPOJi+3J78fj8Yt//6ECTzD8
uxR40jkqRcr/2aUc3Wev85Rq5anxWQjmjliZQYJsCMbOYKPo6OAwLwm5neFgziQYA8ysslZ8
oBqXLgz6+xUDJGg+HUyokWPg8DMhpRIawXgK+NgIptwExQP9gADDpZCy4kiN1/6+FqE6TqF2
ak+Yx5Dbpo9OZWUweiDyjTjhQZ5833iId1YDMzdjq+AwbQgmRuInk3EXosv7fEm0y/qjaKMJ
ZhcMOn7PjqlsqR+SyvbE90YTDMGcO11nPDNizui67ZVgtiU844Bgzui67ZVgtiU844Bgzui6
7ZVgtiU844Bgzui67ZVgtiU844Bgzui67ZVgtiU84yAEYz4eHg+Yx6KQb/QO+Kys+KzMFM58
ETATev6vbcwVEEwFzEo1gjmTvF69+uqv8lgYH4yYXXgOmPl15DwNI2ZXdGQ8nq9ElFwfZ3ii
/o+PTiFhjUnXGCcXgZcEORZ/JE6+bboiRnRiCBVzyEvOzG3lvaz9PCOKv05cA4DwY3YNvI9J
nJ3wBjPhK2/KGpOuMXmRKyMgMHMXUTtHq/6ysmT355CF/me1htpOazuqpzLwtmjVYCCbRGyu
qhicptdfsYJus9J0Aagdi8TvoPyFIuogNtmWBvf5VmCubYuG5KWr+zrdZiqb045IQWZkjFhB
5A4DKzzyyCyhk7IBVGOGTPNudTYXx1kDE8NX9gK2NgMlc8zG4vWBGFKCs5Sl38kxdiozlz5v
VSccHTF62/M3oDdnbx8UMX4WDoV24sMBuToE4HFbnfrPihg/lemCZNojZn40CBjljFQe2JLc
oBpjJuJCudZlSaevOUS0xAKbGcRmISyoXBjSggTpPPkbzEapc66giMm5fBdrRkyRpC7gvVIi
3syWZ1UC59Y0bBoZMctjEYLR4oqSObsexo6NsPduBoqH+S2GBWf29amH3/HPPQvB7J4OgtlV
8NB4B8xcLVb5R9voXh+Zwtwd22VDFr+QiEcVpvHctqxOFYt/uvjrbtB5bgQam3fKLP62AmFX
9hwmbPzCDpZ93vkHtSkFBrlHGTUm5Zk1RiqAyHeo79CxKCZi8T+n/Fa8EgzB/DMF0l3ZPStl
u0ww95y0vllY/Pu0bPVEMK1y9jkjmD4tWz0RTKucfc4Ipk/LVk8E0ypnnzOC6dOy1RPBtMrZ
5ywFZrx3cebPOvRc9W3z53nCdURetFzkcBX4SKb5kYwp6HgTQzC4AgTToNXdLlLJ57k4J1xW
V80thfMmcuLdmt0yXyiQWIW2R8qP3ko4L8EkFFiFy2jY8PpPMHHchRpdLoaZY591xXbZw4Oo
acaEn9Y2b3cSgRyfvR9rgbDp2hw4F8H8FRzUaxMPPgvBfEuNq1bDk/JPMDWRj4/6Ax3YP4Nh
h0o5AAAAAElFTkSuQmCC</item><item item-id="54">iVBORw0KGgoAAAANSUhEUgAAAN0AAABTCAIAAACUHJXlAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="55">iVBORw0KGgoAAAANSUhEUgAAANoAAAAYCAIAAAATX3QzAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="56">iVBORw0KGgoAAAANSUhEUgAAARIAAAAYCAIAAAC5ueWmAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="57">iVBORw0KGgoAAAANSUhEUgAAAUoAAAAYCAIAAAATeiMaAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="58">iVBORw0KGgoAAAANSUhEUgAAAYIAAAAYCAIAAAB4Em1PAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="59">iVBORw0KGgoAAAANSUhEUgAAANkAAAARCAIAAADfZ574AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="60">iVBORw0KGgoAAAANSUhEUgAAAGkAAAB/CAIAAABjb3vrAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="61">iVBORw0KGgoAAAANSUhEUgAAAEQAAACVCAIAAABuE8tvAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="62">iVBORw0KGgoAAAANSUhEUgAAAN0AAABTCAIAAACUHJXlAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="63">iVBORw0KGgoAAAANSUhEUgAAAQIAAABTCAIAAAD7iJ7hAAAAAXNSR0IArs4c6QAAAARnQU1B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=</item><item item-id="64">iVBORw0KGgoAAAANSUhEUgAAAIcAAABpCAIAAACfy6evAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="65">iVBORw0KGgoAAAANSUhEUgAAAK8AAABpCAIAAADDQeSLAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="66">iVBORw0KGgoAAAANSUhEUgAAADcAAAARCAIAAAD2TKM6AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AKtJREFUSEvtltEKwCAIRduf789bYcRFJUSFOVgP0drSk1dlV++91R+Dsv5o9RGn2h+jHMnp
Jt6JbbfA3EkLuOMn20Doz3hVIjhYYG/foaTG4qGUYbAHRvV3Vt9DySRAB7KZMvcOxTNjaSyF
EpR/LJdaUg29xtWGZ6we/BNgCU2Pal3KZnTYSehExvSNfLYo2w3dC9YR04lnJ9xA3JS4TnQT
NLUoiZXNQdOJxx/15At6PX5zlgAAAABJRU5ErkJggg==</item><item item-id="67">iVBORw0KGgoAAAANSUhEUgAAAMsAAAAlCAIAAABj14qWAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="68" content-encoding="gzip">H4sIAAAAAAAA/2ySzW7TQBCAZ9fr3zoJLUlpgGRLCgijCqU9wHWxIiQKCNQzyHIc0wbRJrgu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</item><item item-id="69">iVBORw0KGgoAAAANSUhEUgAAAZEAAAC1CAIAAAARCVWUAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="70">iVBORw0KGgoAAAANSUhEUgAAAEYAAAARCAIAAADvx02cAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ANBJREFUSEvdlYsOgCAIRe3P+3OzIEJ84XTmcK1RKy5HeRzee2dsBSRjyxnjuZPOMlIoqCl4
wg/VKXfeq6X0iVqgBA/jSMIP90l2r5bGJyAgC2FMQeKulbZmH7NbE2GwI/lOprKLXbRNeYE6
BYlyLWoPEEol+nR6ZaP5EQkriCde12mUdncvJIOnxDNSk+K7JR7WDm/iVE7pHFASplNI80b0
LqHV9CA+mDCLlLTLPnvH08m6ecFeFtOg0NO4TxcuzMCCPSiz8ndEArDKfWVMg1oXU554q2FR
5PUAAAAASUVORK5CYII=</item><item item-id="71" content-encoding="gzip">H4sIAAAAAAAA/+JhYGBgBOJkIOYCs5mAJEtJUWqqCAMIOAExR26yf1JWanIJWIQhCYhtgJoc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</item><item item-id="72">iVBORw0KGgoAAAANSUhEUgAAALAAAACXCAIAAAA6fEaHAAAAAXNSR0IArs4c6QAAAARnQU1B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==</item><item item-id="73">iVBORw0KGgoAAAANSUhEUgAAAKwAAABpCAIAAAAodl+IAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="74">iVBORw0KGgoAAAANSUhEUgAAADEAAAARCAIAAAD7UtN9AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ALtJREFUSEvNlYEOgCAIRO3P/XOzUEHARrRU15oV08d50JFSCruNzLTbCLsBXee2NVM2lZuv
GdK4ghqPrjau8hBGk7EkNopv7/3aNMq3TDQ95LhVgk/IJFO0JA2tROWznADbAh4rGsFka8nm
JQN8TGrOKBdL15Li97PzM/2k08gYy3Si1paW7/ykOs5yjqN+U+qo73xMePrD5XVn2XtOTNUp
knom8zkQvJAv6WLIV9GNzJcAlf4EQPK+iukE5mQrTH8sjQsAAAAASUVORK5CYII=</item><item item-id="75">iVBORw0KGgoAAAANSUhEUgAAABwAAAAYCAIAAABm/grVAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AHlJREFUSEvtVMsOABAM4/8/eggRmcfG6iCxk4O1XffwROTgEUHh4eCIqfQ3QFODqtL2bZR/
E7QO1Uyjvo7t8vuBZiLKB7ineKVlP5nS7J3ewb4HXOkCS0nDPTXOZps+WNPMZuEYJ39QsKfi
HRD5TF2eXh+R9uDDO0oD6hYBY8x2PsUAAAAASUVORK5CYII=</item><item item-id="76">iVBORw0KGgoAAAANSUhEUgAAAHIAAAARCAIAAACO32/eAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="77">iVBORw0KGgoAAAANSUhEUgAAAC4AAAARCAIAAAAt9wkYAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AKpJREFUSEvNlEsOwCAIROnNvbm1xSB+41Cb4KLRBIeHA71ijORkJRQni5xwPOZ4REkNcwQL
1SmNyun5/B3FoCN5S/ojKFzVfkkafYDSV4aq76OIJ1Xbcr5F1v7vM0wJcYtC1R8Gm39EcfQq
2jnUb/3U6N3cG3qYpV0av/ftl37SNGVc6+FqgoHBQ2tF4zMKBTXVkz0qjca/AxxIUGZ7VNcQ
n1EYaPE1SKNXbgZLvC8uwhntAAAAAElFTkSuQmCC</item><item item-id="78" content-encoding="gzip">H4sIAAAAAAAA/5STzW7TQBDHZ9eOHaf5LKGQBLqpE0gTQilBHHJBxoqQqJBAFUdQ5NimDdAm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=</item><item item-id="79">iVBORw0KGgoAAAANSUhEUgAAAZIAAADwCAIAAAClq3VIAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
EmtJREFUeF7tne12I6kORfu+/0PPzcSTakJRIFF8SGj3r142CGkLjhHGlf/9888/f/gHAQhA
wBGBL9niHwQgAAFHBP448hVXIQABCPxbIEIBAhCAgC8CyJavfOEtBCDAbos5AAEIeCPAbstb
xvAXAuEJIFvhpwAAIOCNALLlLWP4C4HwBJCt8FMAABDwRgDZ8pYx/IVAeALIVvgpAAAIeCOw
Qba+fvn0oXT9BMobNPyFAAR2ElgtWx+p+mjWFXf6/50wGBsCEPBAYKlspYKFbHmYHvgIAYsE
7MqWo6do4CoEIPCSgEod18lWeqQlKRJdVI4unMxoq+bH4sbwHAvcBc/r4Ege+yLZupeEzSLR
C3E5640tXcBEXofPEBd5Ny1b9z1k/UjeC/HhU22GQRcwka3hqXeRd7uyleZD+E2iF+LDp9oM
gy5gIlvDU+8i755k6zNHKx57IT58qs0w6AImsjU89S7y7kO2hLnxQlwYzt5mLmAiW8MniYu8
I1vD894w6GJaIAfDpwV5H4gU2RoIE1MQgMAiAtqPgUUXIDqi10bSMQRdIAABCwS0ix3ZspA1
fIBAaALIVuj0EzwEPBJAtjxmDZ8hEJoAshU6/QQPAY8EkC2PWcNnCIQmgGyFTj/BQ8AjAWTL
Y9bwGQKhCSBbodNP8BDwSADZ8pg1fIZAaALIVuj0EzwEPBJAtjxmDZ8hEJoAshU6/QQPAY8E
kC2PWcNnCIQmgGyFTj/BQ8AjAWTLY9bwGQKhCSBbodNP8BDwSADZ8pg1fIZAaALIVuj0EzwE
PBJAtjxmDZ8hEJoAshU6/QQPAY8EkC2PWcNnCIQmgGyFTj/BQ8AjAWTLY9bwGQKhCSBbodNP
8BDwReDP9588RLZ8ZQ1vIXA+gY82Vf4hW+dPAiKEgCMCP/spZMtR0nAVAoEJpPusyp6L3Vbg
OULoENhB4EmP7q8/t2yVkb/j0rVeyUQrwCt9YywIQOBDoF4DsttinkAAArYISFSJsy1bOcMb
CEQgMKIGbHDSllYUiREmHjFCoJPAkBqwOTay1UREAwhAQERgVA3YHAzZaiKiAQQg0Cag+R6w
ba3eAtl6S5D+EAhFoHqd6i+J5k33N9CQrTf06AuBWASaX/M1GwzhhWwNwYgRCJxPQHJ09UVh
6j7rQxnZOn+2ESEE2gRaR1Ot99sjDGwxRra0VgYGcJmy4MOMuLAJgUUEWrup1vuL3By227Ig
GRZ8WJc3RoLADAKto6nW+zN8KtjULvbCddMvE59/i1x+GGa7A3vDZ3QIjCHQWsit98d4Ubei
XexlbdJamRGZBR9mxIVNCAwgIDyaMrKbagWsXext2dJabHkofX/XuFL/aAeBvQSaR1PNBnv9
T0bXLva2bO0KTRvJLj8ZFwLbCFQ2U8Lt2DbXfw2sXewN2dp4yKWNxAZ/vIDAawIqxbFwNDUg
Yt1J+qNsXaqxSz52jfs6BRiAwGsCwvrOydFVE4d2sbdFTmux6aKwwa5xhe7RDAJzCTQlSSht
c70cY1272JGtMdyxAgEpAXkNKPyV82dgz9XiYNnaeIdLG4l00tAOAtsJSDZKzd3W9ijGOaBd
7O3d1jjfdJa0keis0xoCewnUVUmia3v9Hzq6drEjW0PxYwwCcgJPZZ28ipSPZbslsmU7P3h3
PAGh6ESqAZs5fydbFeJ9bzX9fW6gjeTFUHSFwFACzRKv2WCoO/aNaRf7rUisAO17q5eZNpLe
cegHgQkETrm/PgFNwaR2sZfOtprEizV5a9N7/1Ky/jWlNpI1fBklOgFhDfiFyfONhMVZ1i72
hyN51YWRK8TnXqlbn//fX8lIaSNZDJrh4hKQlHitT/G49EqRaxf7ut1WIm7IFpPWOQGuLwxN
4GvZ6jvAknz+/MQp321dVWT6n6G4MAaB3wTe14ByC/HYF1f050UVjN+t+74uVOZJLluqSGgM
gTEEJJ/B1IBjWP9n5Z1sDXWlaOzyj7Ot+bAZoZcANWAvub5+pmXrSaqKTmsj6eNFLwiUCXCF
feHM0C52XUn5JpBsn8Vu6w1M+nYSEB5oUAN28u3sZlS2sqO4T3Dc2+pMMt3eEGgeXTUbvBmd
viUCRmWrI1naSDqGoEtQAs0L1SkX5ZdcQZG+C1u72Gt/JzH9YnL9XQRtJO+40ds/AWEB+LPV
9x/wORFoF3v7L/dk9xWKXwXO4KeNZIYP2HRGQFjfcXRlLK/axS6VrStM7QDdfJYN1O0hHS0S
aEqSUNosxnasT9rFjmwdOxXiBqb6RS1HVwYmyjDZevqaTztAN5NlA3V7SMelBIRHV83d1lKn
GUxEQLvY27utbNhsgPTYvuKgsFlqQRuJCA+NXBNo1nfNBq7DP9d57WJ/K1tfJIVDCputP0Q7
dzKcGBl3F87Mqu7e+0jZqgsTsnXifBsXk7AG/P6cHDcqlkwQUIvD3evK5fXiW9kNiScMas+Y
nSZm1EInJCUeR1cLE7JsKLU4vPcs07LsYmp30aeN5H0gWNhPgEcv7M/BBg+0i33Aflt4AVXt
GbutDfNn2pDva0C5hWlBYHgSAbU4vPQj3WpVxq4UnqOKypeB0H06AWrA6Yi9DrBatuZx0kYy
zxMsDyNADTgM5VGGtItdXSQuK920kRyVxoOD4fF7Bye3NzTtYtfJ1sqvcbSR9BKj3wgCwoOn
lRNoRFjYWENAu9gVsiU5mhgYpDaSgUNjqodAc340G/SMSp8TCGgXu1S2hJ+mAxFqIxk4NKY6
CXCFvRNc9G7axS6VrS+uiz8stZFEz/zU+OWfWsvOPqfGi/G1BLSLXSFbl3KtmZnaSNZyjjea
5FOLo6t482JIxNrFrpOtbM81xOMnI9pIpjqD8X8JcH2BeTCHgHaxq2VrjtsFq9pIljl22kDy
ArDyqaUychpB4nlLQLvYka23xE/oLykAm7utE0AQwx4CyNYe7u5HbR5LCaXNPQgC2EAA2doA
/ZAhK1+1UAMekmOjYSBbRhOzzS2h4jR3W9sCYODzCSBb5+dYHWGzvms2UA9JBwgoCCBbCliB
mnJ/PVCy/YWKbPnL2SuPhTXg9T3gq8HoDIEpBJCtKVhNG5WUeBxdmU5hdOeQrZAzgPvrIdN+
TNDI1jGpVAbC4/eUwGhuhwCyZScXIzwRHl1RA46AjY1dBJCtXeSnjds8umo2mOYahiEwhACy
NQSjMSNcXzCWENwZSwDZGstzsjVhDfjlxZqHnE0OF/MQKBJAtrxNDEmJx9GVt6zir4oAsqXC
ZaMx1xds5AEvdhFAtnaRL437vgaUW7AUN75AQEUA2VLhmt+YGnA+Y0bwTgDZspdBakB7OcEj
UwSQLVPp+HGGK+wm04JTRgggW6sSIT914nvAVTlhHKcEkK2FiZOfW32c4u7VwuQwlCMCyNba
ZMnPrVCutZlhNEcEkK1BydLWgIOGxQwEAhJAtsYlXV4DUv2No46lgASQraFJl9eAKNdQ8BgL
RQDZkqX7fQ0otyDziFYQCEsA2RKnnhpQjIqGEJhKANnS4KUG1NCiLQQmEUC2lGC5v64ERnMI
DCeAbH0jFR48cX99+ATEIAT0BJCtH2bNo6tmAz19ekAAAh0EkK0E2vxHsGtxd2R0SBf8HILx
MgLPgTy/YGp5/hk4/FhTtUiENWCxWhzqpRb30MEVxvBTAUvQFJ4CSNImYWQr06P6sfrMi6BM
X+nclLWDp4yTtJULnpFk61IuyVeB05TLxbT4RmV3W50uQfyUCpKsnQuewWSrUgPKq0hZ+p9a
uZgWyNa7JBd6k/eBSJ3J1sfdpxnw6/XiZsHA9QWm78Dpi7yOhemFpyfZShd8cfH/fbGiWZ88
76uAkK2xKw2eAXkeKltjMznUGstsKE7O4Mbi9MHzNNm6qkj+AwEInE1AJdjbvmBqFomqMGgM
AQjEIYBsxck1kULgEALI1iGJJAwIxCGAbMXJNZFC4BAC22Trc6mk40uEQ8ATBgQg0Etgp2z1
+kw/CEAgNAG7suVlL2bcz+xmmVlv7zfgDLp6d8mgk091jDVX38A0KlterkcYv26a1eBmqd7P
CgyCvdOzybPolTWeL2EiW6822/bP5p6WluV5fO0XXuVmZucPPZuylcZ9eWh5onbARLYGzG5r
ElCcuMaXWZGhWbAdK23APNObuB8R6G3M7ZEK6zVSM+/I1pisNEGPGUZvxeluSz6D9UgG9HAq
W9lH1wAQ70yke0DV1hXZegf+pzey9ZLjE0CDYPs2CC/5dHT3soHt+AxAtjrmQ6GLwdX18ZLd
1pgElz6fVBuEsW40rbn7GFDBRLaaE6DdwKxmeZcta2CzfZZqpbWn0bgWxf2gtQoxOwdQwTQq
Wx/Eaek7LqfDLHn00KbPmVfGncxOZKzN0ove5ZhBnqmTqX4JYdqVrWHqgiEIQOAsAsjWWfkk
GggEIIBsBUgyIULgLALI1ln5JBoIBCCAbAVIMiFC4CwCyNZZ+SQaCAQggGwFSDIhQuAsAsjW
WfkkGggEIIBsBUgyIULgLALI1ln5JBoIBCCAbAVIMiFC4CwCyNZZ+SQaCAQggGwFSDIhQuAs
AsjWWfkkGggEIIBsBUgyIULgLALI1ln5JBoIBCCAbAVIMiFC4CwCyNZZ+SQaCAQggGwFSPLu
ECuPhC8+L1jyEGHh03t3h874UwggW1OwYlRCoPhXD+R/9kLeUuIMbRwRQLYcJetkV9M/NlPU
o/uLyNbJE6IaG7IVNvWLAhdWc5kGSUTq00Zof1G0DLOEALK1BHPsQSTboqZOPTWQGI+N/8Do
ka0Dk2otpKayVKrC7K/+3f9GobVg8WcBAWRrAeToQ9Rl6+ldYdnY1MTo9E+MH9k6MavGYqpf
gPg4KxSpNLL0FN9YxLgzlwCyNZcv1rNLWPcqL21QubF117XLFKfy0aYZshUt4/vjpazbnwPn
HiBbzhPozX00y1vGLPqLbFnMCj5BAAIVAsgW0wMCEHBGANlyljDchQAEkC3mAAQg4IwAsuUs
YbgLAQggW8wBCEDAGQFky1nCcBcCEEC2mAMQgIAzAsiWs4ThLgQggGwxByAAAWcEkC1nCcNd
CEAgumxdD/atTIWNP6PLns3S9KTZgBkPgQMIIFv/PY/8KZcbH4qSPePly8Pm8/YkT7Y6YNYS
QnAC5mXr+88c/Pp3vdL3Vm6sIVtNsZg6gQbuttiITc0UxlcSMC9b37Lxl0gmVX1vJYDvReL9
SXVXm8qT6j4m74/ES1/MHmuXSlK2p0tbXpYrAlpsX3wgX9HPlROOsSDwnoAH2bqU67696n7r
gVwqTJmspPKRKki2Icp0oWiw2P16sThuJdP39k9RZCHs3Ui+n7tYCEvAiWxle64sXUU5+1mj
qtTWF/yT3NylqnjSX9HEdJv2pHRPgdTbF1X1MkXlqJoeNDZCwIlsfYRp326ruM7vld1NTnO8
xZq00ksiK001LG6y7rWwkRmJGxBoEvAgW30HWJVez1Tku627kN0l5ukV4f5IWMTdrVEkNuc9
DVwTMC9bfV8XVnpV0yWXrXvxVZePdMtTP/DKzqqaG657+0oUzR2i69mM80EImJet5XkoVk/3
F5vNnqqw4hbsrib3crJC4upelLDLVOXd5ZgZEAL9BJCtfnb0hAAEthBAtrZgZ1AIQKCfALLV
z46eEIDAFgLI1hbsDAoBCPQTQLb62dETAhDYQgDZ2oKdQSEAgX4CyFY/O3pCAAJbCCBbb7Hf
752+tCi/X/pyILpDwCkBZOtV4rJrohVbTTH69G0arDcQjvIqZjpDYDeBE2Sr8gCIBXiFSiFs
9lGuutuVBs2+C4AwBARmE3AvW5VnQ0jY3X/4cglHuq95+nFPqjLZLwEl3e9dKrJV+QVPulO7
/4JHwoE2EHBEwLdsdT3l4W92MjFKJSwTlKtP9lPBVEqKh1z1F1OJrBeJmRhl6nYf5cl/R1MT
VyHwRMCxbPU+5eEXiuLyzkqtS93u+53ibqtYqRX3YnWDqaN1GSqKYya1rAEIHEPAsWx9S0a6
depJilC2MtP1XhLZkhhsylaqp9dmLVWre23bw4g+EDBGwLdsXcrVOsV+pO5XtuTSWZRRY/MQ
dyCgIOBetrI9lyL076bZwfl9z5KWgdf/i8fekrOt7GisbrC427rvsFIP0+O5dNulxUJ7CFgm
cIJsveF7X+fFwur+YnYslTWotL+UMVXMVECfNkf3g7DiodtdUrOB3uCiLwQsEEC2ohOwMAvx
AQIqAtEXLec+qulCYwhYIBBatviizcIUxAcIaAmEli0tLNpDAAIWCCBbFrKADxCAgIIAsqWA
RVMIQMACAWTLQhbwAQIQUBD4P3+9K/RM7K1GAAAAAElFTkSuQmCC</item><item item-id="80">iVBORw0KGgoAAAANSUhEUgAAAHgAAAARCAIAAACZ/f8XAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="81">iVBORw0KGgoAAAANSUhEUgAAADQAAAARCAIAAAAdexg5AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ALVJREFUSEvVlIEOgCAIRO3P/XOzMFNIEnCNXGvUJjzkvC2lFNyuDOd2Bbdkx0j/AZeFtwRU
mqcKHqq3nwUIftnhFHnauii+gZbAQevzTaJm3uFo99J683B1lCgo862JgIDhoG74CCHqhKne
6Uwhl0/hXJ8cHbxUPdILMaW5zl2IrczLCJkWUxtZWiu+KjDBtVeconHLZcKxMbxBbKyk2H7a
Rwz5KSMYxIrU9i0FDhCZt72SIsMOuO2fWjz8AyYAAAAASUVORK5CYII=</item><item item-id="82" content-encoding="gzip">H4sIAAAAAAAA/5RTS2/TQBCetR3HcfNoQkNpkrJpHpA0D9pAFHFBxoqQKEigiiMoSmLTBmgT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</item><item item-id="83">iVBORw0KGgoAAAANSUhEUgAAAZoAAADwCAIAAAC2fDW8AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="84">iVBORw0KGgoAAAANSUhEUgAAAH4AAAARCAIAAACU449QAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="85">iVBORw0KGgoAAAANSUhEUgAAADoAAAARCAIAAAADsiiKAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ALlJREFUSEvVlQEOgCAIRe3m3twszAiShFima405gecXcUkphYlGxp1ohIlYt0KYFTcXsAu6
Ng5ZX+9RhcEzBRGm3uNq45D1mAFsMnMiuuCSBD3754jg1YvLFVLtRLWY64dr4FldIRnvzrfi
fYerzTQYV66836mLa7znlgxT99LbWFPrLxLeNfGJ8Thyl63y1dvv0GvN52BwPJ6JiBpwwzZE
d3fZX44Y8leUb9juiW0BCy5AC39bdHevFYPwiH+quHvbAAAAAElFTkSuQmCC</item><item item-id="86" content-encoding="gzip">H4sIAAAAAAAA/1yTzW7TQBDHZ23HH2k+mtBQmrTdkg+Im6Q0URE9GitColQCVRxBkRObNkCb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</item><item item-id="87">iVBORw0KGgoAAAANSUhEUgAAAZ0AAADFCAIAAAAAF7jbAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="88">iVBORw0KGgoAAAANSUhEUgAAAVMAAAAYCAIAAADIwYk4AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="89">iVBORw0KGgoAAAANSUhEUgAAAEwAAAARCAIAAAD45d1VAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AQxJREFUWEflVe0OhCAMwzf3zVHD3ei+YGjkEs4fhiBu7bqOLeecln9Okss/aXmGV6v+F8nT
mLcJk6mDEZ7kKkOkJBJ5cbYgkvvEKAoijqAvUILlQEpEjBHgoZB/RRhP5p0cJalxtzF045vE
mOCmIGbNXiKphcUmFOsGBrM7yiZrbjOEvkTFsW6ldVhd+6EdD6eXqHpjyCeeK4JBRF1QTFPY
bmd5PmdKNnwyWUkcm4KbV0HPk0Tqx0rGu7StlTdZDE+KUgV7T99XbQVwluC/H0Dfz4ibPulx
oFtd7NTB8/wimRMhXnfjCkk79C2s50AfyhLkyWbbxXhPRBLXQ7lnHu7ylL4rJM33TNyv5joA
XX1fUHgyJcYAAAAASUVORK5CYII=</item><item item-id="90" content-encoding="gzip">H4sIAAAAAAAA/2yTzW7TQBCAZ9f/adKEJoUUkmzTAsJSVaXlAjdjRUhUSEg9cQBZjuO2QbQJ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</item><item item-id="91">iVBORw0KGgoAAAANSUhEUgAAAWYAAAC9CAIAAAAPykAZAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
CvRJREFUeF7tndGW3CgMRLP//9HZzvqs19vjdlOAgEJ3npITEKUrUYNxz+Sv379//+ILAhCA
QCGBl2XwBQEIQKCQwK/CcQyDAAQg8OehBAoQgAAEyglgGeWsGAkBCHDKoAcgAAGFAKcMhRZj
IZCeAJaRvgUAAAGFAJah0GIsBNITwDLStwAAIKAQwDIUWoyFQHoCWEb6FgAABBQC4Zbx+ty6
ooexEIDA0gRi9/Pxcy5LA0AcBCCgEAjcz4dZYBlKORgLgdUJzLGMwp+yZRgEIDCAgORSUZZx
Hi5uTxmORw9HzaanPFBLe7hxsEo7xDKuIrCMxoo2TlcbonG5LtMdNZu6c4XsKMt4O029dZJj
TzhqrmiILnu+MQioGwFK01XaIZZxVcwpQ6pf98FqQ3QXUBHQUbOpO1fIxjJKW5o+LiXVPA7U
zQiFACrtcMu41a6qFACEDXXUXPE9JIyfEBjUAqzmoSptLKMUuUq2NG7wOEfZjppN3blCNpYR
vGUJD4G1CagGjWWsXU/UQSCYAJYRDJjwENiLAJaxVz3JBgLBBLCMYMCEh8BeBLCMvepJNhAI
JoBlBAMmPAT2IoBl7FVPsoFAMAEsIxgw4SGwFwEsY696kg0EgglgGcGACQ+BvQhgGXvVk2wg
EEwAywgGTHgI7EUAy9irnmQDgWACWEYwYMJDYC8CWMZe9SQbCAQTwDKCARMeAnsRwDL2qifZ
QCCYAJYRDJjwENiLAJaxVz3JBgLBBFaxjJeO4+s2X1VlMLQ9w/+EDPY9K92WldoVIb/78yri
VpCqso1J3tlfC5EXDZn/S0DdjCGWcS0HljG3OQ/+alvM1czqIwmovRFrGQ8PJueTy/UPI0nl
WUvtia9keOT5imjBAbc77uH24FMKgZbBXcYKfRN0yuCRZ4XidtGgfkcJtIwjHx5MutS1Lkjo
xg4yo7pMmVVNAMuoRrfbxAGPD2q37YZ4i3zUInLK2KLsM5LglDGDev81l7CMrzeaqsr+nIjY
RiD0kadNGrM1AupmDD9l3MpXVWoMGB1MYMAjT3AGhP+PgLoZsQy6BwKpCWAZqctP8hBQCWAZ
KjHGQyA1ASwjdflJHgIqASxDJcZ4CKQmgGWkLj/JQ0AlgGWoxBgPgdQEsIzU5Sd5CKgEsAyV
GOMhkJoAlpG6/CQPAZUAlqESYzwEUhPAMlKXn+QhoBLAMlRijIdAagJYRurykzwEVAJYhkps
5nh+inwmfdb+hwCWYdYI/K4as4JtJxfL8CspvxHPr2YbKcYyLIupls0ySUQvSUDtPX4r1/wy
csqYX4PECrAMs+Jzl2FWsO3kYhlOJeWNiVO1NtXawTJeIY6vA9HbX7twU1V2WZQgEIDATwLq
Zry/y3iLogb9ajQVASk2BCAQQUDdjB+vP6+nDFXo1+dzVaUqgPEQgEAhAXUzPr0xuT6eFC5f
eOxRVVavzkQIQOCZgLoZv7xkVcOVW8Z5RXL9A9WFAASCCNzuuIpjgXzKUE3kdrwaJAgiYVcj
wCuk8RVRN2PIXcaZ9ic1qsrxHK8r0scj+X+9CBspJsNa6mbU3phIx5gHKarK6ZWjj0eW4KBt
1yQjEXVcS+V8YxnnM88h6+dfS+Q+v3BRVZasGD2GPo4m/HyyG7l6qrXUzSj/jEnJAm8XLYV3
ouvXqST39bNYXyHuPLJGaleHWMbXhFWVXwMOGEAfD4D89jzi2CdjKHVcRYWsWcbbQ0q1blVl
9UK9JnKX0YvkcxxumsdwbnkG1CyjVz5elkEf96o7cRYkoG5GLGPBIiIJAuMIYBnjWLMSBDYg
0GQZDyfwun/6BFRVuUFhSAECaxJQN+P7g8nDPV/dP91iUlWuyRpVXgS4k+qyGe8/yvX2rutc
6eFFo/QOEsvw2mzbqOXN189Sqpux6APjha9kytcuH7lNs5LIIgSk722LaA6VoW5GThmh5SD4
igTUTbJiDv00qTS4y+jHnkgOBCJOGda3JE2WUfdapIKXqtKhFdFoQCDuLiMucjRWdTPyUa7o
ihB/FQIV39sk6RHnF0lA3WAso44bsyDQgYC6/Tos2RxC1cwpoxk5ASDwDwFOGYGNoBpboBRC
Q6AHAe4yelD8HAPLiOVL9LEEom9JQrNRNyMPJqHlIDgEVieAZaxeIfRBYCkCWMZS5UAMBFYn
gGWsXiH0QWApAnktw/oKaqkeQsw6BAZ0dV7LOF+MH/VWQazTJSiBwJVA9OtbdacEvjF5kKKq
LO8h04/TlCfIyIQEQrta3YxRlvHSMcUyOF8k3FEZUlY3djkTNXKUZTxvXVWlmn9c/HIljIRA
LwIpThlfLeM4hrx9NSKOfuprlMd0CFQQ6NXVtzvu+WngVu0+p4wBd8sV9WYKBFoIDOhq9Ui+
j2W0FIa5EEhLwM8yVMVpS0viEIggoG7AyacMVW4EMmJCIDMBdQ9GWcZ513J/g/Lrz7qq1sx1
JXcIBBFQt2GUZTynV3FPG8SLsBBITsDGMjhlJO9U0l+EgJNl4BqLNA0yMhMwswxcI3OzkvsK
BPwsYwVqaIBAWgJYRtrSkzgEaghgGTXUmAOBtASwjLSlJ3EI1BDAMmqoMQcCaQlgGWlLT+IQ
qCGAZdRQYw4E0hLAMtKWnsQhUEMAy6ihxhwIpCWAZaQtPYlDoIYAllFDjTkQSEsAy0hbehKH
QA0BLKOGGnMgkJYAlpG29CQOgRoCWEYNtZI5KtmSmAPGOMp21PwqZRLZ036R34Dd0neJJA3R
F1pdNFDXcaubpdLGMko5q2RL4waPc5TtqJlTRmwjO/aEo2bTPgZ17Pb7f3SVNqeM0uqoZEvj
Bo9zlO2o2dSdK2RHWcar6sfX7Y5w7AlHzRUNEexgReFBXYSp0yCVdohlXEXcClJVdoLTFMZR
M5bRVHJxcpIOwTJK+yJJQ5TiiBwH6ki677FV2tMs43xy4Q8QgMBcApJDzbEMSSKDIQCBdQhg
GevUAiUQMCCAZRgUCYkQWIcAlrFOLVACAQMCIZZxvNt7+FyGARgkQgACdwSiLAPaEIDAlgTm
WIbjGcRR87Vl1dfv09vd7pRq1yFvLVGof4JlfP1s6PRm/SnAUfObX3hZhpfa40n8BG4h/s2R
y/VjGbJBWTTEz/OFkWwjqbc24aL/k00868cysAyZQOiEo1/tnkrOg4aLXzycjLCMzh1u1BPX
tnCRfTULF81ejyRfT0ZYRk/L8Gri8gfUnozaYjlqPjI+z0dtAMbN5sEknLWXX5zH+/Mm3EK/
qWU4ysYyYi3j5Gux8Z5f+sSSaovuuPcc35g43WVcv/u1dde42ddv1FhGNPeTdvRCfeN7yf6p
tlD/hDcmfetENAhAYCQBLGMkbdaCgD0BLMO+hCQAgZEEsIyRtFkLAvYEsAz7EpIABEYSwDJG
0mYtCNgTwDLsS0gCEBhJAMsYSZu1IGBPAMuwLyEJQGAkASxjJG3WgoA9ASzDvoQkAIGRBLCM
kbRZCwL2BLAM+xKSAARGEsAyRtJmLQjYE8Ay7EtIAhAYSQDLGEmbtSBgTwDLsC8hCUBgJAEs
YyRt1oKAPQEsw76EcxMw/bWGc6FZr45lWJcP8RAYTQDLGE2c9SBgTQDLsC7fZPGO/w3iZGT+
y2MZ/jWcmgF3GVPxT1gcy5gAfaclsYydqlmSC5ZRQokxHwlgGdmaA8vIVvHO+WIZnYEuHw7L
WL5ECwss/C/5Fs4AaTIBLENGxgQIZCaAZWSuPrlDQCaAZcjImACBzASwjMzVJ3cIyAT+BjER
sYhfLYmlAAAAAElFTkSuQmCC</item><item item-id="92">iVBORw0KGgoAAAANSUhEUgAAADgAAAARCAIAAAAHR/i3AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AMZJREFUSEvVVVsOwCAIc/c/tNuiIVgeQowf+LFsi0JbCj6991ZifUBLrFYC5V/2ekCHUQk3
+TbFhEdIHVQ3cwyLopAmmxWoHgJF1Xg4KWo2WZZbMP5S6zGn6AkvxAEs4XCzzJMlM1GBouMT
EHPQFhN+UN0D4snZ7dgUu564SqAqen5ZSKBZ5Rwb6KWnlGpvgcctIa0gcqpExosHFMoNgLaF
huIEm8bathmcsnbqH9Vtjk5BS/Cw6NFD3lePF7xCr+pxHryMoi/YWSdZsXpVoQAAAABJRU5E
rkJggg==</item><item item-id="93">iVBORw0KGgoAAAANSUhEUgAAAD8AAAARCAIAAADlm+POAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AMlJREFUSEvdlskWQDAMRev/P7o4JdKXpIMuJKxwqPuGYMs5p7jbQR93S3HRz9b8hL6Un8TQ
LLiShxPK4Th9mWYuxoMM9BfoLWIQ9rmSwlP1/jp1W07EskWyZpQVLQKrFcFWIWfdMemBg54q
W8RBrX3eQG6Hmp788FghL9GDhWrfJCsk9rp+z8rW1KrdkDl0k4Hpp/KseN+nV985jcGwejXV
nME0FHp1NOH1rx7yG+U+N9saWZlPQ0bV0kG5Pi/7y5+CT3fbVLG93wHTHj1R9x0nlQAAAABJ
RU5ErkJggg==</item><item item-id="94">iVBORw0KGgoAAAANSUhEUgAAANYAAAARCAIAAAAubMV1AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="95">iVBORw0KGgoAAAANSUhEUgAAAE0AAAA9CAIAAABnSXMmAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="96">iVBORw0KGgoAAAANSUhEUgAAAGEAAAARCAIAAABCRlU1AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ASlJREFUWEftltsOgzAMQ9n/f3SHBgrBdi4TexiiPFVdmqbuibvXGGOZX67AqtH8cgWWKVCp
wNSolGg8WqPVhdiOtxmv3A80goz1vfxHhC+bx6eZ6wXfUSMkxYFjPx0D08iDdyLt8y7yDIAK
WveFszxbhu0V9mO5kQ+z7pALZdqkWsEUV+MbUojqDmBrIxj5bwcXB6JATtkRUJXtEq3l+KgM
ydcBSNKffLeA1ZWGhX3hwMwIuCzfEyDG8cx4RP2OMPRaeRUddH1OQInVjO6GTUCWGrEs8UyC
5TULjb7qrBLgJlx5N8lTJXV2jOIqRx1Q2S/7u+Z+ZH2ReKoEmd2AnYWVlV7MNqL9qInAfcMi
A5I++1CNfEsmTrSDVvJ2X1jKyvPn7HgiykQz4A1Ceqspdv6bYAAAAABJRU5ErkJggg==</item><item item-id="97">iVBORw0KGgoAAAANSUhEUgAAAF0AAAA9CAIAAABA5/LOAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="98">iVBORw0KGgoAAAANSUhEUgAAAFYAAAARCAIAAADIacx0AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AQJJREFUWEftVosOAiEMwz+/P0cI5xwdG081AS/GEIXRdV13D++9O/wJFBz+uMPzj03wp+BN
QTCED9GRrIaCk/N0XbccHgW8kQHKLnAtmyGB3nyWw+MBv6ECGjpcCC3E8T29rFXjowoSynRM
Uj5/fYrAr+B30b9ZcUT7TLYSkKJSYGQr3x6qTMuyAxecF40jIktbtMMoygpdaiwclFQaHtS/
aA3yRxCFJlIDs1G2n6nAaDSYGnbN5w2y4gUzWiierU4EbUPRPoCdMbRIAZfucjs0+sKYEVLD
RdccM4gs3zEKdzr1GoQX80VlvVPa2WiI/nS58LkVpax3zT+mnyiofm9MwRPfZkMLs/u2ugAA
AABJRU5ErkJggg==</item><item item-id="99" content-encoding="gzip">H4sIAAAAAAAA/1yTzXISQRCAe/YXCARIIgpRBhckWQJI4kVv6xZllTGlVTl50KIWWBM0Cbi7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</item><item item-id="100">iVBORw0KGgoAAAANSUhEUgAAAa0AAADFCAIAAABo5DrjAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
EUVJREFUeF7tnYGS5KYORfP+/6PzJvEuSwCDfA1YwJnaSs10yyAdiWthuzv/+/vvv//iBwIQ
gMDJBH50kB8IQAACJxP46+TgiR0CEIDAP3tiKEAAAhA4nAA6eHgBED4EIEA/SA1AAALHE6Af
PL4EAACB4wmgg8eXAAAgcDwBdPD4EgAABI4ngA4eXwIAgMDxBNDB40sAABA4ngA6eHwJAAAC
xxNYWAd/Pg15fPoAAAEIdCCwqpRcHwnvAIAhIACB4wksKSWXAqKDx1cvACDQh8BuOnjyVwcR
OwQgoO0U19PB0AYW+0GHTSIuWU7ZUIKShYDFRqilxXQwjhAdtNRE0UYoFHku44G4ZAEFpUGU
1tPBpPNPuFAogwrFMuwbGxJnoQelQZQW08GYAv2gpSboB6EkE7AcuIc0o4OWXL+y2aNQXiEw
HAwlAySPz0jskbiFdZBOx7JyoAQlmYDlQHTQQmm2zR5ZGU0NShbCUDqH0in9IM9VnUPAsnqx
2ZiAcAI7SAc3TjyhBQLCGoDeZgSEGkAHN6uB08MR1sDpyLaLX6gBdPDLKhASJrgrzyIfaHRy
xPgjxjSGg5kTAkINoIOzcyck6c093+uy4OwgDfMNcsxnsAYemHQjINSAxxXyhscdguv1n//k
/67ptLcEV4UkvdHBf0NzmuURjo0YU8gyh3xIQKgBpytEhtjUwWTkIBG5Vljeyv0Mt2XDW/Er
ybvB298y/ad3S9ql/MC6wSV/Ydiin7HN75PBry80yw8sxpWPEF6JI8p/L05X9KcZZpbQ3Upa
XgvHHogO3vY+oR8cqoNxAhIpSSQv0al4tSe/Jw1dPk7RIDbLy6Iox0WpjQUrF6/6WSexj08M
ybA5t2ZQd4tcWAPH6sWugQs1sNvJs74ytaavclTejAQ1uevFjGs+F6C6dlTmLTJpuhErkcU4
RlG0F3SwCfOuz911hROXhQA66KgfjJf9nS4Ym7t4s9k85K6Dq+wfi2O+0cFwbBJ4sQ+1NLx3
MNFBiy6cZoMOetRBbQ9o31QmOuJTB+sd5Zt+s6Lvp61/4r3bRTXJHLcvHnq/OF/PeasV75eb
zV2+uU4OuTOIza4urLiFr2h0337QqINxw2iBST/YXOEHGtAPNvrBCTWRb/0SGQp/xhe/8hfj
vXCyLw7aV9x15lvIYlnkg9Q9b8bVlKQ83phMTCAJoanp8dTCGphQFUwxk4BQA2f1gzOT4Xwu
oVacRyTviZaICyftBITaRgfteLeyFGplifh3jWsJ+E6cFGoAHXSSu6luJJvcqXMPnkxYA4M9
YvjZBIQaQAdnJ4n5hhIQ1sBQfxh8PgGhBtDB+WlixoEEhDUw0BuG/oKAUAPo4BeJYs5hBIQ1
MMwXBv6GgFAD6OA3qWLWQQSENTDIE4b9ioBQA+jg8GQJWenlU3PqpkEvT6aNs19E09BtM5FQ
A+jg2OzHjwqPnSkbvTl13UAopskBFqdb1G0P6LbxQaiB9XSw/szHHYLr9fzd8Ir2lqV0hKxY
hrXYNKeuGDSPtTgw36aL25VimB8RMz4lINTAYjoYR1iMtq6DiRQmxpXBm/OGVOUyHSQ4ny7p
yJJj4wPjt3Kz4jhF3b/8DPbxaSAepDnF09KcZi+sgWZf2WvMaRAOn0jI12I6GCdY08GgDpXD
n74Vi2D+e6IvQYnufgk6latbcfDkxUTa8iWRGxRVPjYrBuhzsQlr4C6QcB7yGSle1RP3iM+q
Oljp+0Ivk/zS1NBEmCoKYklAXUdy/3Nv8/7xTmSTbrEuW0XVqyvsmToYTpmPVlS9zDrKtOzV
NgferXQB8pI6mDRKFoHLF//Tps/SHdQbq2RdVdq9O0XOfSjqqaCDcUnls+fvul1LwhoY2g/W
zzpuMS7tmFADS+pgpXGr9InF5X2noZZerN4wGveVRbPJOliX77VWsrAGimu+Y9SWM+jSuuPN
eaEGztLBSvekvZVUgL0fzOXP0rHW+8Hk3dDE3TlZ7PISx5pN6wZrwHIBRFhalm2KN3p7+CMk
6ywdnJDmovrkLzbNmioWb35ztar3IME+Vr1EFuMx47eEIpuAvX4pYKYDd6rqnNu3iPrOLqBe
TAeLLYzlxCug6ZsbRptDwFuii1uEOSiOnUWogcV0sJnaOwQCmuZcGDgk4CrRuTOu3HOYvi4u
CZDRwS7kGcQLAWENeHEdPzoREGoAHezEnmF8EBDWgA/H8aIbAaEG0MFu9BnIAwFhDXhwGx86
EhBq4CAdTG568ueuBDquKIZakQA6ePv/L14xnfgMAQgIBNBBdFAoGw6BwFYE0EF0cKuCJhgI
CATQQXRQKBsOgcBWBL7UQWHuEeyduDEiNMaEAAQsBAQR6Ha/WJjbEtJTGyduPHUbewhAoBcB
QQQKOng9TvHjU/jl8q/4Z2wpTN8r8jCOBx+6B8WAEICAnYAgAuV+MJa8oHRBDS9NjP+MX7G7
O8JSQDDCDcaEAAS+IiCIwK0O5h1WeOw27w0TZWz6kehs0mnm+JoG9INf1RzzQsAbgab+FBSm
GEM8ULEfDM1g3hgae8N8ijpNY2xGM2+Zwx8IQKAXAUEEhuyLLeJYbAmT65JFraz3hgKCXvQZ
BwIQ8EBAEIHavvjuxkjYICc6lchf4k3xz7jZLB5eH7O4g/aQCXyAAAS+ItBZB+Uw4iuJ4WJi
/mKyuc67yKRnLF6XTJwUEMhhcqArAnnqKQZXCZrmjJD39n0SwXvjvjiMHNvfNYDG64kCAiFA
DvFJwFgkPp3Hq14EBBG4fX5QGCvu7+INb3g9Fr6818vbvXgDXtyMsy/uVTrbjJMX3jahEYiR
gKBd3T5PYnRxtJmAYLRLjD+ZADUwGbi36YQCQAe9JRF/XhGgH3yFb4uD0UG+b2aLQlaD4Pqg
Sm6r497q4M+H5fJ/v6/uKW/NpysgmO8kM44gwP3iEVRXHFMQgf/si39/aPhP7OEV7a35EAUE
851kRghAYBwBQQTW08Fw47jIUUAwLh+MDAEIzCcgiMBiOti8ACQgmJ8nZoQABMYREERgMR2M
2RWjFRCMywcjQwAC8wkIIrChDhafvp6fDGaEAASGErj7nEUHHVzofjH94NAiY3BXBLgbbk/H
Wx20z/S55V2oAoLPY8EBCFgINC+OWwY5wUYQgSU/T1KJU0BwQmV4jpFOx54dPi1jYSWIwHo6
GIJkX2ypiSVs6HTsaRIWuX3wPSwFRIvpYHJlNE+bgGCP3K8eBZ2OJYNQslOyWAabxXSwGRs6
2ETk1oDc1VND12wsXaGQ2t8/GFowoxPfmgkIvnWY2S8CdDp2EYyJUT9dNoWm76NeSFwWcpUK
/rMriT6+TgYpjJcEhBK63RfXb0e8dHTc4QKCcc4wsoUA94stlLCxExBEoHZ98NoR26f3YLmc
wx6g4QMEdiIgiEBD5oQRvwW6nMPf4mJ2COxHQBCBx/2gMMdM0M7dm4mCuSBwJgFBBLg+OLBU
Kt/vPXBWhobAPYETrsZ208FkoPieiTDHzLJ05V7lS7xnMmEuCMQEtn8OURCBx88PCnPMrEJX
7vXSQfrKmSV0wlx7P60piMDj28HCHDMLy5V7HXUwYbjabfyZJcBcJgKuVorJY7OREBo6aKb7
3BAdfM6MI2YQ6NIPur3UOFwHrycKhWlm5PbfOVz5hg5OyzsT2Ql0vD7YcSi7/01LQQQe94NN
J741EBCMc7jXdb1eejouUkZehUD3Jq5La9mXniACyf+fJJXF+E5xdonql3F3sm+gCAjeTDfn
WHRwDmdm0Qh4W3SCP7fCl+8xKz2wn/ZYQKDlfuZRvfrKmT4z1yEENuwHr8xVAtPemlkQ73UQ
0ZmZL+ZamoCfBijGKIiA6Xu3jHMI03cvgvc+sAntnhQG3JKAqwtiRo26S0T5Oep8U/ymVZxZ
BOjgTNrMBQGHBAQR4Ppgmkf6QYeVjUsQsBN4q4OVRld7y+56L0sBQTI1OtgrF4wDgU8ICCLA
84P0g5/UKpNCYBQBdLDD50m4X2wpT7fXyC3OY7M3AXSwgw7uXSIdo/P5zETHABlqDoHu51R0
EB2cU7q/ZnH4DO3U+JmsE4G+59RTdLASp4CgUyoPHQbghya+d9gdz6lCTa53n+QnSHSwdxGK
43WsXdEDDtuIgKBfxeiFcdbTwZ/I0UEPxd93L+MhInz4kEDHcyo6+I9EFn8+TPCWU3e/tr0l
JYIyEtDOqXeLHR3kPomx8DCDgBcC3c+p6CA66KW48QMCXxFAB9HBRu11P/d+VevMC4E7Augg
OtheHdq1mPa4WEDAB4EjdDBcHO11y9xH7qZ60fHe3FS/mQwCBgJH6GCdg4DAAHZDE0BtmFRC
+peAUNtLPj9YSbeA4MDioR88MOnnhCyIADp4Tnn8ipTrg8el/LCA0UGlJT6qSLhffFS6zwwW
HUQHz6x8oobAHwLoIDrIeoDA6QTQQXTw9DVA/BBAB9FBVgEETieADqKDp68B4ocAOogOsgog
cDoBdBAdPH0NED8E0EF0kFUAgdMJoIPo4OlrgPghgA6ig6wCCJxOAB1EB09fA8QPAXQQHWQV
QOB0AuggOnj6GiB+CKCD6CCrAAKnE0AH0cHT1wDxQwAdRAdZBRA4nQA66FEHhayMLmRcshCG
0jmU+F5+S65f2bCcLPigBCULAYuNUEvooAXsKxshK6/mMxyMSwZI7C0skDahhA6akv3GCNGx
0IMSlCwELDZCLaGDFrCvbISsvJrPcDAuGSBt0ulYIn1js0ctraeDP9yvn2Ly9sjKm7q0HAsl
KFkIWGz2qKXFdDCGXkzAHlmx1N8bGyhZ6EHpHErooCXXr2xYThZ8UIKShYDFRqilDXUwbJz5
BQIQOJOARS5jm9108Gn82EMAAhBAB6kBCEDgdALo4OkVQPwQgAA6SA1AAAKnE1hMB3/SVX9+
8PR8Ej8EIPCcwHo6+DxGjoAABCBQI7C/DgoPEw0qGT+drB9PAmqHLsW+DSoJbdjK56m0Ad8c
5SpxyWK3+7a5DvqpmOYnYd7U4qNj/XhSFBo/563LPT8lFPx5lO6hxq5qKcnUI9921sELhLd1
9blLj+pj6CoqDu4qX95KyBWcpJI9+HZX203fdtDB5In5pMtoIhix1O9c8nBKRwftGXelg8GZ
T0r6DppDRPkqaxLbQQfrbUUTgX1V9LL81iV00JjHAOrbfCWXUD2cShOATvhU+tOmh3vqoOel
3kyJcZXKZsCxoHNIyaFLQXo+r+p6D9h0b1sdrO9MLSthhE0zHyMmrZy9PfiTXMeYQMAyRVI/
HkA51EFvLh19fbBe1h4qODlZfeuSt9p1BcfzrRuHifPmEjp4K4bfik5ycSd0GZaWZJyNEzd8
wsmxOymhaxN6/Yyrjacj+3Ep98TumyOgTxOAPQQgAIEuBNDBLhgZBAIQWJgAOrhw8nAdAhDo
QgAd7IKRQSAAgYUJoIMLJw/XIQCBLgTQwS4YGQQCEFiYADq4cPJwHQIQ6EIAHeyCkUEgAIGF
CaCDCycP1yEAgS4E0MEuGBkEAhBYmAA6uHDycB0CEOhCAB3sgpFBIACBhQmggwsnD9chAIEu
BNDBLhgZBAIQWJgAOrhw8nAdAhDoQgAd7IKRQSAAgYUJoIMLJw/XIQCBLgTQwS4YGQQCEFiY
ADq4cPJw/YeA8XvqXX2XPYnzRgAd9JYR/HlMAI17jIwD/ksAHaQilieADi6fwq8DQAe/zgDz
vyZw6WBlg2zcO792hAFWJYAOrpo5/A4Egg5WmNAzUjC18oAOBFYnYGn30MHVszzUf/rBoXgZ
fAYB+sEZlLeeAx3cOr1nBBd6vUrTRz94Ri2IUaKDIjgOc0Lg2hTnt0rizXJs48Rt3HBFAB10
lQ6c6UmAHrAnza3HQge3Tu/BwSGCByf/cejo4GNkHAABCGxGAB3cLKGEAwEIPCbwf3VPWrNe
JetRAAAAAElFTkSuQmCC</item><item item-id="101">iVBORw0KGgoAAAANSUhEUgAAAkQAAAAFCAMAAACTpQOIAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AwBQTFRFAAAAgICAgAAAgIAAAIAAAICAAACAgACAgIBAAEBAAID/AECAQAD/gEAA////wMDA
/wAA//8AAP8AAP//AAD//wD///+AAP+AgP//gID//wCA/4BAwNzApsrwbWlzTAB/PwCCMQCG
IgCJEgCMAgCPAA6TACCWADOZAEacAFqgAG+jAIWmAJupAK2nALCWALODALdwALpdAL1IAMAz
AMQdAMcGEMoAKc0AQtEAXNQAdtcAktsArt4Ay+EA5OEA6MkA67AA7pcA8XwA9WIA+EYA+ykA
/wwACwsLFhYWISEhLCwsNzc3QkJCTU1NWFhYY2Njbm5ueXl5hYWFkJCQm5ubpqamsbGxvLy8
x8fH0tLS3d3d6Ojo8/PzKwAAVQAAqgAA1QAAADMAKzMAVTMAgDMAqjMA1TMA/zMAAGYAK2YA
VWYAgGYAqmYA1WYA/2YAAJkAK5kAVZkAgJkAqpkA1ZkA/5kAAMwAK8wAVcwAgMwAqswA1cwA
/8wAK/8AVf8AgP8Aqv8A1f8AAABVKwBVVQBVgABVqgBV1QBV/wBVADNVKzNVVTNVgDNVqjNV
1TNV/zNVAGZVK2ZVVWZVgGZVqmZV1WZV/2ZVAJlVK5lVVZlVgJlVqplV1ZlV/5lVAMxVK8xV
VcxVgMxVqsxV1cxV/8xVAP9VK/9VVf9VgP9Vqv9V1f9V//9VAACqKwCqVQCqgACqqgCq1QCq
/wCqADOqKzOqVTOqgDOqqjOq1TOq/zOqAGaqK2aqVWaqgGaqqmaq1Waq/2aqAJmqK5mqVZmq
gJmqqpmq1Zmq/5mqAMyqK8yqVcyqgMyqqsyq1cyq/8yqAP+qK/+qVf+qgP+qqv+q1f+q//+q
KwD/VQD/gAD/qgD/1QD/ADP/KzP/VTP/gDP/qjP/1TP//zP/AGb/K2b/VWb/gGb/qmb/1Wb/
/2b/AJn/K5n/VZn/gJn/qpn/1Zn//5n/AMz/K8z/Vcz/gMz/qsz/1cz//8z/K///Vf//qv//
1f////vwoKCk2AWPPxGsViNa2xjW4wAAAAlwSFlzAAAOzQAADtYBytAvAAAAADNJREFUSEvt
0jENADAQw8CoAMwf7peDV3vPcsqoBKTA5L55AoxXCRgB/okqASnQiSRgcziJX4ONC7U4ewAA
AABJRU5ErkJggg==</item><item item-id="102">iVBORw0KGgoAAAANSUhEUgAAAkYAAAAHCAIAAABiJBXbAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AGtJREFUaEPt2IENABAMBMDa3OZFJHbwucYCfySP0d1lCBAgQIBAgMCuNEOAAAECBAIEKiCD
CAQIECBA4Hw6XoVZZREgQIAAgR8FXp17pbnZECBAgECIgEoL2UgxCBAgQEClOQMECBAgECKw
AGWDLGOJbqsTAAAAAElFTkSuQmCC</item></binaryContent></worksheet>