<?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>2</revision><documentID>aa8390ae-6a24-47f4-b633-6bf32492bb71</documentID><versionID>b433b17d-7386-4a04-95ba-875efeccc579</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="12" 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="6" 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="3996802201" 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="1941" 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" /><link href="./dataTOC.xmcd" title="Links to Table of Contents" popup="false" /><rendering item-idref="3" /></region><region region-id="1532" left="156" top="44.25" width="87" height="12" align-x="156" 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="./regression.xmcd" title="Links to Section Contents" popup="false"><sp count="6" />REGRESSION</link></c></inlineAttr></u></f></p></text></region><region region-id="114" left="36" top="78.75" width="169.5" height="13.5" align-x="36" align-y="90" 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">Residuals and Applicability of Fit</p></text><indexes><phrase>residuals and applicability of fit</phrase><phrase>regression, residuals and applicability of fit</phrase><phrase>fit, residuals and applicability</phrase></indexes></region><region region-id="1618" left="36" top="122.25" width="318" height="108" align-x="36" align-y="132" 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">Before a regression model can be used for prediction, it's important to see whether the underlying model assumptions have been met. Assumptions for the simple linear model are based on the way in which the population error terms are distributed. Specifically, the errors should be uncorrelated, and, for any given value of X, should be <b><u><link href="./QQplot.xmcd" title="Links to section" popup="false">normally distributed</link></u></b> with a mean of zero and a constant variance. We can check the assumptions, therefore, by investigating the error estimates (residuals).</p></text></region><region region-id="1533" left="36" top="260.25" width="177" height="72" align-x="36" align-y="270" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">It's often easier to interpret the relative magnitude of residuals if they're standardized, that is, if the residuals are divided by an estimate of the error standard deviation <f family="Symbol" charset="2" size="10"><inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"><c val="#000">s</c></inlineAttr></f>.</p></text></region><region region-id="1924" left="222" top="241.5" width="97.5" height="243.75" align-x="250.5" align-y="366" 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>FLEX</ml:id><ml:matrix rows="15" cols="2"><ml:real>0.711</ml:real><ml:real>0.701</ml:real><ml:real>0.706</ml:real><ml:real>0.67</ml:real><ml:real>0.898</ml:real><ml:real>0.821</ml:real><ml:real>0.872</ml:real><ml:real>0.874</ml:real><ml:real>0.848</ml:real><ml:real>0.853</ml:real><ml:real>0.67</ml:real><ml:real>0.905</ml:real><ml:real>0.887</ml:real><ml:real>0.742</ml:real><ml:real>0.79</ml:real><ml:real>0.58</ml:real><ml:real>0.63</ml:real><ml:real>0.603</ml:real><ml:real>0.615</ml:real><ml:real>0.855</ml:real><ml:real>0.810</ml:real><ml:real>0.799</ml:real><ml:real>0.834</ml:real><ml:real>0.791</ml:real><ml:real>0.81</ml:real><ml:real>0.615</ml:real><ml:real>0.916</ml:real><ml:real>0.87</ml:real><ml:real>0.73</ml:real><ml:real>0.696</ml:real></ml:matrix></ml:define></math><rendering item-idref="4" /></region><region region-id="1925" left="66" top="357.75" width="56.25" height="18" align-x="81" align-y="372" 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>vx</ml:id><ml:apply><ml:matcol /><ml:id>FLEX</ml:id><ml:real>0</ml:real></ml:apply></ml:define></math><rendering item-idref="5" /></region><region region-id="1926" left="66" top="387.75" width="57.75" height="18" align-x="82.5" align-y="402" 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>vy</ml:id><ml:apply><ml:matcol /><ml:id>FLEX</ml:id><ml:real>1</ml:real></ml:apply></ml:define></math><rendering item-idref="6" /></region><region region-id="1927" left="66" top="417" width="68.25" height="12.75" align-x="77.25" align-y="426" 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>n</ml:id><ml:apply><ml:id>rows</ml:id><ml:id>FLEX</ml:id></ml:apply></ml:define></math><rendering item-idref="7" /></region><region region-id="1928" left="66" top="441" width="48.75" height="12.75" align-x="74.25" align-y="450" 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:minus /><ml:id>n</ml:id><ml:real>1</ml:real></ml:apply></ml:range></ml:define></math><rendering item-idref="8" /></region><region region-id="1895" left="36" top="470.25" width="183.75" height="24" align-x="36" align-y="480" 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">The residuals are found by fitting the data</p></text></region><region region-id="1896" left="66" top="507" width="195.75" height="12.75" align-x="114.75" align-y="516" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:function><ml:id>FLEXfit</ml:id><ml:boundVars><ml:id>a</ml:id></ml:boundVars></ml:function><ml:apply><ml:plus /><ml:apply><ml:id>intercept</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:id>slope</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:id>a</ml:id></ml:apply></ml:apply></ml:define></math><rendering item-idref="9" /></region><region region-id="1897" left="36" top="530.25" width="283.5" height="12" align-x="36" align-y="540" 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">and subtracting the fit values from the measured values.</p></text></region><region region-id="1898" left="66" top="555" width="114" height="18" align-x="99" 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:apply><ml:indexer /><ml:id>residF</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:minus /><ml:apply><ml:indexer /><ml:id>vy</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:id>FLEXfit</ml:id><ml:apply><ml:indexer /><ml:id>vx</ml:id><ml:id>i</ml:id></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="10" /></region><region region-id="1899" left="0" top="582" width="6000" height="6" align-x="0" align-y="582" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1557" left="36" top="602.25" width="274.5" height="24" align-x="36" align-y="612" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">An estimate of <f family="Symbol" charset="2" size="10"><inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"><c val="#000">s</c></inlineAttr></f> is given by the standard error of estimate: </p></text></region><region region-id="1568" left="66" top="639" width="77.25" height="12.75" align-x="81" align-y="648" 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>se</ml:id><ml:apply><ml:id>stderr</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply></ml:define></math><rendering item-idref="11" /></region><region region-id="1908" left="156" top="639" width="60.75" height="12.75" align-x="170.25" align-y="648" 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>se</ml:id></ml:eval></math><rendering item-idref="12" /></region><region region-id="1559" left="36" top="679.5" width="106.5" height="12.75" align-x="36" align-y="690" 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">Standardized Residuals</p></text><indexes><phrase>standardized residuals</phrase><phrase>residuals, standardized</phrase></indexes></region><region region-id="1564" left="36" top="704.25" width="303.75" height="24" align-x="36" align-y="714" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">Dividing the residuals by the standard error of the estimate (i.e., the parametric fit)</p></text></region><region region-id="1583" left="66" top="758.25" width="65.25" height="27.75" align-x="96" align-y="774" 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>sresid</ml:id><ml:apply><ml:div /><ml:id>residF</ml:id><ml:id>se</ml:id></ml:apply></ml:define></math><rendering item-idref="13" /></region><region region-id="1909" left="156" top="736.5" width="120" height="76.5" align-x="185.25" align-y="774" 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>sresid</ml:id></ml:eval><resultFormat><table item-idref="14" /></resultFormat></math><rendering item-idref="15" /></region><region region-id="1578" left="36" top="824.25" width="259.5" height="12" align-x="36" align-y="834" 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">gives us what are known as standardized residuals. </p></text></region><region region-id="1586" left="36" top="848.25" width="277.5" height="36" align-x="36" align-y="858" 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">Another frequently used estimate for <f family="Symbol" charset="2" size="10"><inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"><c val="#000">s</c></inlineAttr></f> adjusts for the distanc<f family="Verdana" charset="0">e, or leverage,</f> between each value of x and the mean of x:</p></text></region><region region-id="1587" left="66" top="891" width="48.75" height="12.75" align-x="74.25" 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:define warning="WarnRedefinedUDScalar"><ml:id>i</ml:id><ml:range><ml:real>0</ml:real><ml:apply><ml:minus /><ml:id>n</ml:id><ml:real>1</ml:real></ml:apply></ml:range></ml:define></math><rendering item-idref="16" /></region><region region-id="1591" left="66" top="909" width="182.25" height="39" align-x="108" align-y="936" 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>leverage</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:div /><ml:real>1</ml:real><ml:apply><ml:minus /><ml:apply><ml:id>rows</ml:id><ml:id>vx</ml:id></ml:apply><ml:real>1</ml:real></ml:apply></ml:apply><ml:apply><ml:div /><ml:apply><ml:pow /><ml:parens><ml:apply><ml:minus /><ml:apply><ml:indexer /><ml:id>vx</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:id>mean</ml:id><ml:id>vx</ml:id></ml:apply></ml:apply></ml:parens><ml:real>2</ml:real></ml:apply><ml:apply><ml:id>Var</ml:id><ml:id>vx</ml:id></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="17" /></region><region region-id="1593" left="36" top="974.25" width="280.5" height="24" align-x="36" align-y="984" 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">The adjusted standardized residual, called a studentized residual, is defined by</p></text></region><region region-id="1602" left="66" top="1005" width="177.75" height="49.5" align-x="111.75" align-y="1026" 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>stu_resid</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:div /><ml:apply><ml:indexer /><ml:id>sresid</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:sqrt /><ml:apply><ml:minus /><ml:real>1</ml:real><ml:parens><ml:apply><ml:plus /><ml:apply><ml:div /><ml:real>1</ml:real><ml:apply><ml:id>rows</ml:id><ml:id>vx</ml:id></ml:apply></ml:apply><ml:apply><ml:indexer /><ml:id>leverage</ml:id><ml:id>i</ml:id></ml:apply></ml:apply></ml:parens></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="18" /></region><region region-id="1596" left="36" top="1058.25" width="30" height="12" align-x="36" align-y="1068" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">where</p></text></region><region region-id="1603" left="66" top="1082.25" width="167.25" height="44.25" align-x="103.5" 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:define><ml:id>σ_estim</ml:id><ml:apply><ml:div /><ml:id>se</ml:id><ml:apply><ml:sqrt /><ml:apply><ml:minus /><ml:real>1</ml:real><ml:parens><ml:apply><ml:plus /><ml:apply><ml:div /><ml:real>1</ml:real><ml:apply><ml:id>rows</ml:id><ml:id>vx</ml:id></ml:apply></ml:apply><ml:id>leverage</ml:id></ml:apply></ml:parens></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="19" /></region><region region-id="1623" left="258" top="1068" width="124.5" height="61.5" align-x="294.75" 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:eval><ml:id>σ_estim</ml:id></ml:eval><resultFormat><table item-idref="20" /></resultFormat></math><rendering item-idref="21" /></region><region region-id="1598" left="36" top="1148.25" width="304.5" height="24" align-x="36" align-y="1158" 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">is an estimate for the standard deviation of the individual residual. </p></text></region><region region-id="1604" left="0" top="1182" width="6000" height="6" align-x="0" align-y="1182" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1605" left="36" top="1196.25" width="281.25" height="48" align-x="36" align-y="1206" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">Studentized residuals are more precise in the sense that they account for any point-to-point differences in error variance. However, for the most part, standardized and studentized residuals will usually be close in value:</p></text></region><region region-id="1608" left="66" top="1258.5" width="120" height="76.5" align-x="95.25" align-y="1296" 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>sresid</ml:id></ml:eval><resultFormat><table item-idref="22" /></resultFormat></math><rendering item-idref="23" /></region><region region-id="1609" left="222" top="1258.5" width="133.5" height="76.5" align-x="264.75" align-y="1296" 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>stu_resid</ml:id></ml:eval><resultFormat><table item-idref="24" /></resultFormat></math><rendering item-idref="25" /></region><region region-id="1610" left="36" top="1363.5" width="117.75" height="12.75" align-x="36" align-y="1374" 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">Checking the assumption</p></text><indexes><phrase>assumption, checking the</phrase></indexes></region><region region-id="1611" left="36" top="1382.25" width="297.75" height="24" align-x="36" align-y="1392" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">A linear relationship was originally chosen for this data because it looked linear</p></text></region><region region-id="1612" left="48" top="1416" width="261.75" height="137.25" align-x="48" align-y="1416" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="26" /><rendering item-idref="27" /></region><region region-id="1932" left="36" top="1550.25" width="200.25" height="12" align-x="36" align-y="1560" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">and had a high <b><u><link href="./association.xmcd" title="Links to section" popup="false">correlation coefficient</link></u></b>,</p></text></region><region region-id="1931" left="258" top="1551" width="99" height="12.75" align-x="310.5" align-y="1560" 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>corr</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply></ml:eval></math><rendering item-idref="28" /></region><region region-id="1736" left="36" top="1574.25" width="306" height="72" align-x="36" align-y="1584" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">If the data are, in fact, linearly related, and the errors are normally distributed, a scatterplot of the standardized (or studentized) residuals versus either the x values or the predicted y values will have no discernible pattern. The points will be randomly scattered about the hypothesized error mean of zero.</p></text></region><region region-id="1628" left="42" top="1650" width="267.75" height="141.75" align-x="42" align-y="1650" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="29" /><rendering item-idref="30" /></region><region region-id="1629" left="0" top="1800" width="6000" height="6" align-x="0" align-y="1800" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1631" left="42" top="1824" width="265.5" height="141.75" align-x="42" align-y="1824" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="31" /><rendering item-idref="32" /></region><region region-id="1633" left="36" top="1970.25" width="310.5" height="48" align-x="36" align-y="1980" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">If the data are not linear, residual scatterplots will have a distinct pattern. For instance, if we generate a random sample of points having a curvilinear (in this case, quadratic) relationship, </p></text></region><region region-id="1634" left="66" top="2037" width="60.75" height="12.75" align-x="110.25" 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:define><ml:id>n_sample</ml:id><ml:real>75</ml:real></ml:define></math><rendering item-idref="33" /></region><region region-id="1644" left="228" top="2037" width="45.75" height="12.75" align-x="257.25" 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:define><ml:id>endpt</ml:id><ml:real>20</ml:real></ml:define></math><rendering item-idref="34" /></region><region region-id="1636" left="66" top="2061" width="84" height="12.75" align-x="76.5" align-y="2070" 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:minus /><ml:id>n_sample</ml:id><ml:real>1</ml:real></ml:apply></ml:range></ml:define></math><rendering item-idref="35" /></region><region region-id="1645" left="228" top="2061" width="66" height="18" align-x="242.25" align-y="2070" 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>x</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:id>rnd</ml:id><ml:id>endpt</ml:id></ml:apply></ml:define></math><rendering item-idref="36" /></region><region region-id="1638" left="66" top="2084.25" width="28.5" height="13.5" align-x="78" align-y="2094" 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>σ</ml:id><ml:real>10</ml:real></ml:define></math><rendering item-idref="37" /></region><region region-id="1646" left="228" top="2084.25" width="106.5" height="13.5" align-x="238.5" align-y="2094" 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 font="4">ε</ml:id><ml:apply><ml:id>rnorm</ml:id><ml:id>n_sample</ml:id><ml:real>0</ml:real><ml:id>σ</ml:id></ml:apply></ml:define></math><rendering item-idref="38" /></region><region region-id="1647" left="66" top="2102.25" width="93" height="39.75" align-x="77.25" align-y="2130" 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>y</ml:id><ml:apply><ml:plus /><ml:apply><ml:vectorize /><ml:apply><ml:pow /><ml:parens><ml:apply><ml:minus /><ml:id>x</ml:id><ml:apply><ml:div /><ml:id>endpt</ml:id><ml:real>2</ml:real></ml:apply></ml:apply></ml:parens><ml:real>2</ml:real></ml:apply></ml:apply><ml:id font="4">ε</ml:id></ml:apply></ml:define></math><rendering item-idref="39" /></region><region region-id="1652" left="66" top="2157" width="152.25" height="12.75" align-x="92.25" align-y="2166" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:function><ml:id>fit</ml:id><ml:boundVars><ml:id>a</ml:id></ml:boundVars></ml:function><ml:apply><ml:plus /><ml:apply><ml:id>intercept</ml:id><ml:id>x</ml:id><ml:id>y</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:id>slope</ml:id><ml:id>x</ml:id><ml:id>y</ml:id></ml:apply><ml:id>a</ml:id></ml:apply></ml:apply></ml:define></math><rendering item-idref="40" /></region><region region-id="1432" left="36" top="2192.25" width="47.25" height="12" align-x="36" align-y="2202" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">residuals:</p></text></region><region region-id="1911" left="66" top="2217" width="82.5" height="18" align-x="96" align-y="2226" 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>resid</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:minus /><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:id>fit</ml:id><ml:apply><ml:indexer /><ml:id>x</ml:id><ml:id>k</ml:id></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="41" /></region><region region-id="1654" left="36" top="2252.25" width="324" height="24" align-x="36" align-y="2262" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">The quadratic pattern in the data will be reflected in the residual scatterplot.</p></text></region><region region-id="1656" left="24" top="2286" width="288" height="135.75" align-x="24" align-y="2286" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="42" /><rendering item-idref="43" /></region><region region-id="1436" left="0" top="2430" width="6000" height="6" align-x="0" align-y="2430" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1659" left="36" top="2443.5" width="118.5" height="12.75" align-x="36" align-y="2454" 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">Constant Error Variances</p></text><indexes><phrase>constant error variances</phrase><phrase>error variances, constant</phrase></indexes></region><region region-id="1660" left="36" top="2462.25" width="296.25" height="84" align-x="36" align-y="2472" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">If the variance of the error terms is not constant (they are not normally distributed), a scatterplot of the residuals will show either an increasing or decreasing spread from left to right. Even though the data generated below appear linear, the error variances are not normally distributed. The points in the residual plot are increasingly scattered from left to right:</p></text></region><region region-id="1670" left="66" top="2559" width="60.75" height="12.75" align-x="110.25" align-y="2568" 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>n_sample</ml:id><ml:real>75</ml:real></ml:define></math><rendering item-idref="44" /></region><region region-id="1707" left="222" top="2559" width="45.75" height="12.75" align-x="251.25" align-y="2568" 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>endpt</ml:id><ml:real>20</ml:real></ml:define></math><rendering item-idref="45" /></region><region region-id="1672" left="66" top="2583" width="84" height="12.75" align-x="76.5" align-y="2592" 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>0</ml:real><ml:apply><ml:minus /><ml:id>n_sample</ml:id><ml:real>1</ml:real></ml:apply></ml:range></ml:define></math><rendering item-idref="46" /></region><region region-id="1708" left="222" top="2583" width="66" height="18" align-x="236.25" align-y="2592" 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>x</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:id>rnd</ml:id><ml:id>endpt</ml:id></ml:apply></ml:define></math><rendering item-idref="47" /></region><region region-id="1674" left="66" top="2606.25" width="38.25" height="17.25" align-x="93" align-y="2616" 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 subscript="incr">σ</ml:id><ml:id>x</ml:id></ml:define></math><rendering item-idref="48" /></region><region region-id="1709" left="222" top="2606.25" width="154.5" height="21.75" align-x="252" align-y="2616" 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 subscript="incr">ε</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:indexer /><ml:apply><ml:id>rnorm</ml:id><ml:id>n_sample</ml:id><ml:real>0</ml:real><ml:apply><ml:indexer /><ml:id subscript="incr">σ</ml:id><ml:id>k</ml:id></ml:apply></ml:apply><ml:id>k</ml:id></ml:apply></ml:define></math><rendering item-idref="49" /></region><region region-id="1676" left="66" top="2637" width="24.75" height="12.75" align-x="78.75" align-y="2646" 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>m</ml:id><ml:real>5</ml:real></ml:define></math><rendering item-idref="50" /></region><region region-id="1677" left="114" top="2637" width="23.25" height="12.75" align-x="125.25" align-y="2646" 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>b</ml:id><ml:real>2</ml:real></ml:define></math><rendering item-idref="51" /></region><region region-id="1710" left="222" top="2636.25" width="81" height="17.25" align-x="233.25" align-y="2646" 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>y</ml:id><ml:apply><ml:plus /><ml:apply><ml:plus /><ml:apply><ml:mult style="default" /><ml:id>m</ml:id><ml:id>x</ml:id></ml:apply><ml:id>b</ml:id></ml:apply><ml:id subscript="incr">ε</ml:id></ml:apply></ml:define></math><rendering item-idref="52" /></region><region region-id="1714" left="66" top="2661" width="152.25" height="12.75" align-x="92.25" align-y="2670" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDFunction"><ml:function><ml:id>fit</ml:id><ml:boundVars><ml:id>a</ml:id></ml:boundVars></ml:function><ml:apply><ml:plus /><ml:apply><ml:id>intercept</ml:id><ml:id>x</ml:id><ml:id>y</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:id>slope</ml:id><ml:id>x</ml:id><ml:id>y</ml:id></ml:apply><ml:id>a</ml:id></ml:apply></ml:apply></ml:define></math><rendering item-idref="53" /></region><region region-id="1715" left="36" top="2690.25" width="47.25" height="12" align-x="36" align-y="2700" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">residuals:</p></text></region><region region-id="1914" left="66" top="2715" width="82.5" height="18" align-x="96" align-y="2724" 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>resid</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:minus /><ml:apply><ml:indexer /><ml:id>y</ml:id><ml:id>k</ml:id></ml:apply><ml:apply><ml:id>fit</ml:id><ml:apply><ml:indexer /><ml:id>x</ml:id><ml:id>k</ml:id></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="54" /></region><region region-id="1917" left="222" top="2715" width="88.5" height="12.75" align-x="264" align-y="2724" 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>corr</ml:id><ml:id>x</ml:id><ml:id>y</ml:id></ml:apply></ml:eval></math><rendering item-idref="55" /></region><region region-id="1721" left="24" top="2742" width="288" height="135.75" align-x="24" align-y="2742" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="56" /><rendering item-idref="57" /></region><region region-id="1905" left="36" top="2882.25" width="335.25" height="36" align-x="36" align-y="2892" 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">It may be possible to correct this sort of situation by transforming the dependent variable, as described in <b><u><link href="./errorvar.xmcd" title="Links to Application" popup="false">Stabilizing the Error Variance</link></u></b>.</p></text></region><region region-id="1913" left="0" top="2940" width="6000" height="6" align-x="0" align-y="2940" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1724" left="36" top="2947.5" width="97.5" height="12.75" align-x="36" align-y="2958" 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">Correlation of Errors</p></text><indexes><phrase>correlation of errors</phrase><phrase>errors, correlation of</phrase></indexes></region><region region-id="1723" left="36" top="2972.25" width="306" height="36" align-x="36" align-y="2982" 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">Whether adjacent error terms in the linear regression model are correlated can be determined by the Durbin-Watson statistic (DW).</p></text></region><region region-id="1740" left="66" top="3015.75" width="51" height="18" align-x="75.75" align-y="3030" 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:matcol /><ml:id>FLEX</ml:id><ml:real>0</ml:real></ml:apply></ml:define></math><rendering item-idref="58" /></region><region region-id="1741" left="144" top="3015.75" width="52.5" height="18" align-x="155.25" align-y="3030" 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>y</ml:id><ml:apply><ml:matcol /><ml:id>FLEX</ml:id><ml:real>1</ml:real></ml:apply></ml:define></math><rendering item-idref="59" /></region><region region-id="1742" left="36" top="3044.25" width="309.75" height="24" align-x="36" align-y="3054" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">Values for the statistic range from 0 to 4. If adjacent terms are uncorrelated, the Durbin-Watson value will be close to 2. </p></text></region><region region-id="1743" left="66" top="3085.5" width="138" height="89.25" align-x="100.5" align-y="3132" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:function><ml:id>DW</ml:id><ml:boundVars><ml:id>e</ml:id></ml:boundVars></ml:function><ml:apply><ml:div /><ml:apply><ml:summation /><ml:lambda><ml:boundVars><ml:id>d</ml:id></ml:boundVars><ml:apply><ml:pow /><ml:parens><ml:apply><ml:minus /><ml:apply><ml:indexer /><ml:id>e</ml:id><ml:id>d</ml:id></ml:apply><ml:apply><ml:indexer /><ml:id>e</ml:id><ml:apply><ml:minus /><ml:id>d</ml:id><ml:real>1</ml:real></ml:apply></ml:apply></ml:apply></ml:parens><ml:real>2</ml:real></ml:apply></ml:lambda><ml:bounds><ml:real>1</ml:real><ml:apply><ml:minus /><ml:apply><ml:id>rows</ml:id><ml:id>e</ml:id></ml:apply><ml:real>1</ml:real></ml:apply></ml:bounds></ml:apply><ml:apply><ml:summation /><ml:lambda><ml:boundVars><ml:id>d</ml:id></ml:boundVars><ml:apply><ml:pow /><ml:parens><ml:apply><ml:indexer /><ml:id>e</ml:id><ml:id>d</ml:id></ml:apply></ml:parens><ml:real>2</ml:real></ml:apply></ml:lambda><ml:bounds><ml:real>0</ml:real><ml:apply><ml:minus /><ml:apply><ml:id>rows</ml:id><ml:id>e</ml:id></ml:apply><ml:real>1</ml:real></ml:apply></ml:bounds></ml:apply></ml:apply></ml:define></math><rendering item-idref="60" /></region><region region-id="1746" left="222" top="3123" width="100.5" height="12.75" align-x="276" align-y="3132" 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>DW</ml:id><ml:id>residF</ml:id></ml:apply></ml:eval></math><rendering item-idref="61" /></region><region region-id="1747" left="36" top="3194.25" width="304.5" height="48" align-x="36" align-y="3204" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">Durbin-Watson values less than 2 indicate positive adjacent correlations, and values greater than 2 indicate negative correlations. Note that this statistic is used in the calculation of <b><u><link href="./statspline.xmcd" title="Links to section" popup="false">least-squares B-splines</link></u></b>.</p></text></region><region region-id="1752" left="36" top="3254.25" width="288" height="48" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">Unfortunately, the Durbin-Watson statistic cannot detect higher-order (non adjacent) correlations. These types of correlations do not commonly occur without a correlation between adjacent errors.</p></text></region><region region-id="1933" left="36" top="3325.5" width="45.75" height="12.75" align-x="36" align-y="3336" 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">Normality</p></text><indexes><phrase>normality</phrase></indexes></region><region region-id="1754" left="36" top="3344.25" width="325.5" height="36" align-x="36" align-y="3354" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">If a normal plot of the standardized residuals resembles a straight line, as it does for the FLEX data, then the errors are approximately normally distributed. </p></text></region><region region-id="1757" left="66" top="3392.25" width="168.75" height="27.75" align-x="89.25" align-y="3408" 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>axes</ml:id><ml:apply><ml:id>qqplot</ml:id><ml:apply><ml:div /><ml:id>residF</ml:id><ml:apply><ml:id>stderr</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply></ml:apply><ml:str>normal</ml:str></ml:apply></ml:define></math><rendering item-idref="62" /></region><region region-id="1756" left="24" top="3426" width="315.75" height="154.5" align-x="24" align-y="3426" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="63" /><rendering item-idref="64" /></region><region region-id="1918" left="0" top="3582" width="6000" height="6" align-x="0" align-y="3582" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1759" left="36" top="3596.25" width="279.75" height="36" align-x="36" align-y="3606" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">Since normal plots can also be sensitive to other assumption violations, such as when the error variances are not equal, it's best to check for normality last.</p></text></region><region region-id="1764" left="36" top="3655.5" width="102" height="12.75" align-x="36" align-y="3666" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag="outlier"><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">Residuals and outliers</p></text><indexes><phrase>residuals and outliers</phrase><phrase>outliers, residuals and</phrase></indexes></region><region region-id="1768" left="36" top="3674.25" width="279.75" height="48" align-x="36" align-y="3684" 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">As a general rule, a data point can be classified as an outlier if the absolute value of its standardized residual is greater than 3. Let's make an outlier in the FLEX data to demonstrate.</p></text></region><region region-id="1783" left="66" top="3741" width="74.25" height="18" align-x="112.5" align-y="3750" 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>FLEX</ml:id><ml:sequence><ml:real>12</ml:real><ml:real>1</ml:real></ml:sequence></ml:apply><ml:real>1.151</ml:real></ml:define></math><rendering item-idref="65" /></region><region region-id="1803" left="144" top="3735.75" width="56.25" height="18" align-x="159" align-y="3750" 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>vx</ml:id><ml:apply><ml:matcol /><ml:id>FLEX</ml:id><ml:real>0</ml:real></ml:apply></ml:define></math><rendering item-idref="66" /></region><region region-id="1804" left="222" top="3735.75" width="57.75" height="18" align-x="238.5" align-y="3750" 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>vy</ml:id><ml:apply><ml:matcol /><ml:id>FLEX</ml:id><ml:real>1</ml:real></ml:apply></ml:define></math><rendering item-idref="67" /></region><region region-id="1805" left="66" top="3765" width="68.25" height="12.75" align-x="77.25" align-y="3774" 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>n</ml:id><ml:apply><ml:id>rows</ml:id><ml:id>FLEX</ml:id></ml:apply></ml:define></math><rendering item-idref="68" /></region><region region-id="1806" left="144" top="3765" width="195.75" height="12.75" align-x="192.75" align-y="3774" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define warning="WarnRedefinedUDFunction"><ml:function><ml:id>FLEXfit</ml:id><ml:boundVars><ml:id>a</ml:id></ml:boundVars></ml:function><ml:apply><ml:plus /><ml:apply><ml:id>intercept</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:id>slope</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:id>a</ml:id></ml:apply></ml:apply></ml:define></math><rendering item-idref="69" /></region><region region-id="1807" left="66" top="3789" width="48.75" height="12.75" align-x="74.25" align-y="3798" 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:minus /><ml:id>n</ml:id><ml:real>1</ml:real></ml:apply></ml:range></ml:define></math><rendering item-idref="70" /></region><region region-id="1808" left="144" top="3789" width="114" height="18" align-x="177" align-y="3798" 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>residF</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:minus /><ml:apply><ml:indexer /><ml:id>vy</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:id>FLEXfit</ml:id><ml:apply><ml:indexer /><ml:id>vx</ml:id><ml:id>i</ml:id></ml:apply></ml:apply></ml:apply></ml:define></math><rendering item-idref="71" /></region><region region-id="1823" left="222" top="3860.25" width="132" height="24" align-x="222" align-y="3870" 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">We can remove the outlier using the<f family="Verdana" charset="0"><b><inlineAttr line-through="false"><c val="#000"> trim </c></inlineAttr></b></f>function</p></text></region><region region-id="1820" left="222" top="3897" width="52.5" height="18" align-x="258" align-y="3906" 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>outlier</ml:id><ml:real>0</ml:real></ml:apply><ml:real>12</ml:real></ml:define></math><rendering item-idref="72" /></region><region region-id="1812" left="60" top="3822" width="147" height="241.5" align-x="147.75" align-y="3942" 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:greaterThan /><ml:apply><ml:vectorize /><ml:apply><ml:absval /><ml:apply><ml:div /><ml:id>residF</ml:id><ml:apply><ml:id>stderr</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply></ml:apply></ml:apply></ml:apply><ml:real>3</ml:real></ml:apply></ml:eval><resultFormat><table item-idref="73" /></resultFormat></math><rendering item-idref="74" /></region><region region-id="1821" left="222" top="3933" width="127.5" height="12.75" align-x="267" align-y="3942" 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>FLEXnew</ml:id><ml:apply><ml:id>trim</ml:id><ml:id>FLEX</ml:id><ml:id>outlier</ml:id></ml:apply></ml:define></math><rendering item-idref="75" /></region><region region-id="1825" left="222" top="3957.75" width="77.25" height="18" align-x="241.5" align-y="3972" 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>vx1</ml:id><ml:apply><ml:matcol /><ml:id>FLEXnew</ml:id><ml:real>0</ml:real></ml:apply></ml:define></math><rendering item-idref="76" /></region><region region-id="1824" left="222" top="3987.75" width="78.75" height="18" align-x="243" align-y="4002" 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>vy1</ml:id><ml:apply><ml:matcol /><ml:id>FLEXnew</ml:id><ml:real>1</ml:real></ml:apply></ml:define></math><rendering item-idref="77" /></region><region region-id="1828" left="222" top="4029" width="218.25" height="12.75" align-x="275.25" align-y="4038" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:function><ml:id>FLEXfit1</ml:id><ml:boundVars><ml:id>a</ml:id></ml:boundVars></ml:function><ml:apply><ml:plus /><ml:apply><ml:id>intercept</ml:id><ml:id>vx1</ml:id><ml:id>vy1</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:id>slope</ml:id><ml:id>vx1</ml:id><ml:id>vy1</ml:id></ml:apply><ml:id>a</ml:id></ml:apply></ml:apply></ml:define></math><rendering item-idref="78" /></region><region region-id="1902" left="0" top="4074" width="6000" height="6" align-x="0" align-y="4074" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1827" left="48" top="4080" width="299.25" height="192.75" align-x="48" align-y="4080" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><plot disable-calc="false" item-idref="79" /><rendering item-idref="80" /></region><region region-id="1934" left="36" top="4285.5" width="94.5" height="12.75" align-x="36" align-y="4296" 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">Influence of outliers</p></text><indexes><phrase>influence of outliers</phrase><phrase>outliers, influence of</phrase></indexes></region><region region-id="1834" left="36" top="4304.25" width="284.25" height="48" align-x="36" align-y="4314" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">Outliers that are the result of experimental errors should, of course, be discarded from a regression. Removing an outlier that's not due to an error depends in part on its effect on the regression.</p></text></region><region region-id="1835" left="36" top="4364.25" width="300" height="36" align-x="36" align-y="4374" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit"><c val="#000">For instance, when the outlier</c> detected above is removed from the original data the coefficient of determination improves from</p></text></region><region region-id="1940" left="66" top="4407" width="103.5" height="18.75" align-x="123" align-y="4422" 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:pow /><ml:apply><ml:id>corr</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:real>2</ml:real></ml:apply></ml:eval></math><rendering item-idref="81" /></region><region region-id="1941" left="192" top="4412.25" width="14.25" height="12" align-x="195.75" align-y="4422" 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">to </p></text></region><region region-id="1938" left="222" top="4407" width="112.5" height="18.75" align-x="288" align-y="4422" 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:pow /><ml:apply><ml:id>corr</ml:id><ml:id>vx1</ml:id><ml:id>vy1</ml:id></ml:apply><ml:real>2</ml:real></ml:apply></ml:eval></math><rendering item-idref="82" /></region><region region-id="1903" left="0" top="4440" width="6000" height="6" align-x="0" align-y="4440" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><pageBreak /></region><region region-id="1855" left="36" top="4453.5" width="75" height="12.75" align-x="36" align-y="4464" 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">Cook's Distance</p></text><indexes><phrase>Cook's distance</phrase></indexes></region><region region-id="1856" left="36" top="4472.25" width="250.5" height="24" align-x="36" align-y="4482" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">The overall influence of a deleted point on a linear regression can be measured by <f family="Verdana" charset="0"><inlineAttr font-weight="normal" line-through="false"><c val="#000">Cook's distance:</c></inlineAttr></f></p></text></region><region region-id="1859" left="66" top="4515.75" width="289.5" height="195.75" align-x="158.25" align-y="4530" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><math optimize="false" export="false" disable-calc="false"><ml:define><ml:function><ml:id>Cooks</ml:id><ml:boundVars><ml:id>DATA</ml:id><ml:id>index</ml:id></ml:boundVars></ml:function><ml:program><ml:localDefine><ml:id>vx</ml:id><ml:apply><ml:matcol /><ml:id>DATA</ml:id><ml:real>0</ml:real></ml:apply></ml:localDefine><ml:localDefine><ml:id>vy</ml:id><ml:apply><ml:matcol /><ml:id>DATA</ml:id><ml:real>1</ml:real></ml:apply></ml:localDefine><ml:localDefine><ml:id>yfit</ml:id><ml:apply><ml:plus /><ml:apply><ml:id>intercept</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:id>slope</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:id>vx</ml:id></ml:apply></ml:apply></ml:localDefine><ml:localDefine><ml:apply><ml:indexer /><ml:id>ind</ml:id><ml:real>0</ml:real></ml:apply><ml:id>index</ml:id></ml:localDefine><ml:localDefine><ml:id>x0</ml:id><ml:apply><ml:id>trim</ml:id><ml:id>vx</ml:id><ml:id>ind</ml:id></ml:apply></ml:localDefine><ml:localDefine><ml:id>y0</ml:id><ml:apply><ml:id>trim</ml:id><ml:id>vy</ml:id><ml:id>ind</ml:id></ml:apply></ml:localDefine><ml:localDefine><ml:id>y0fit</ml:id><ml:apply><ml:plus /><ml:apply><ml:id>intercept</ml:id><ml:id>x0</ml:id><ml:id>y0</ml:id></ml:apply><ml:apply><ml:mult style="default" /><ml:apply><ml:id>slope</ml:id><ml:id>x0</ml:id><ml:id>y0</ml:id></ml:apply><ml:id>vx</ml:id></ml:apply></ml:apply></ml:localDefine><ml:localDefine><ml:id>Cooks</ml:id><ml:apply><ml:div /><ml:apply><ml:summation /><ml:lambda><ml:boundVars><ml:id>i</ml:id></ml:boundVars><ml:apply><ml:pow /><ml:parens><ml:apply><ml:minus /><ml:apply><ml:indexer /><ml:id>y0fit</ml:id><ml:id>i</ml:id></ml:apply><ml:apply><ml:indexer /><ml:id>yfit</ml:id><ml:id>i</ml:id></ml:apply></ml:apply></ml:parens><ml:real>2</ml:real></ml:apply></ml:lambda><ml:bounds><ml:real>0</ml:real><ml:apply><ml:id>last</ml:id><ml:id>vx</ml:id></ml:apply></ml:bounds></ml:apply><ml:apply><ml:mult style="default" /><ml:real>2</ml:real><ml:apply><ml:pow /><ml:apply><ml:id>stderr</ml:id><ml:id>vx</ml:id><ml:id>vy</ml:id></ml:apply><ml:real>2</ml:real></ml:apply></ml:apply></ml:apply></ml:localDefine></ml:program></ml:define></math><rendering item-idref="83" /></region><region region-id="1861" left="66" top="4737" width="120.75" height="12.75" align-x="140.25" align-y="4746" 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>Cooks</ml:id><ml:id>FLEX</ml:id><ml:real>12</ml:real></ml:apply></ml:eval></math><rendering item-idref="84" /></region><region region-id="1862" left="36" top="4760.25" width="292.5" height="36" align-x="36" align-y="4770" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">If Cook's distance for an outlier is less than 1, then the outlier doesn't usually have a major effect on the regression and perhaps should not be removed.</p></text></region><region region-id="1863" left="36" top="4802.25" width="297" height="84" align-x="36" align-y="4812" 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="inherit" text-align="left" list-style-type="inherit" tabs="inherit">This outlier does affect the correlation, but should we delete it?<sp count="2" />If, for some reason, we believe that the outlier came from a source other than the rest of the data, it should be removed from the analysis. The caution is that what appear to be outliers may, in fact, represent a physical phenomena which must be explained, and perhaps a different regression model is a better choice.</p></text></region><region region-id="1919" left="6" top="4902" width="427.5" height="8.25" align-x="6" align-y="4902" show-border="false" show-highlight="false" is-protected="true" background-color="inherit" tag=""><png item-idref="85" /><rendering item-idref="86" /></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
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AAlwSFlzAAALEQAACxEBf2RfkQAADRtJREFUeF7tnP1vHkcRx8M/ixBqcNIXAUlp07zYde0Q
N24ipyl9iSlpYhoai0TEtHHTJKpdHhQsKFIoKkFQoEhAJJDCb+FjvtVo2N3bZ++e3mM/ZKOV
dbc3Mzs7N9+d2bl98pWHDx/uqf+qBaoFJsICwLX+qxaoFpgIC+yZCC2rktUC1QLbiXC1QrVA
tcCkWKDCdVLeVNWzWqBG1+oD1QKTY4EaXSfnXVVNH3kL7Dl6+Ght1QLVAjtigXNvnLt44eLm
xub9f9wvWYsqXOtqVS2wKyxw9uWzW7/YyoO2wnVXvKodWdrroLvQAosnF+/du9cE2grXCtdq
gV1ngdXLqw8ePIhBW+G6617VLlzyq0rjtwC5cYzYCYPr7xf3Pjzz1eXnnx6/+VqNOIqeb50/
MRic4W+rER8p4r5NhP1pO27SGLEVrr1E1wrXXn39EYErNgwQu+vgunjs0AfffWp17tvJ9z0K
DHp1oED4KHqO6IuvvTqPhONzM+Oc75jHGtFEQ7U9fWqWNpRsPATsY20Tu+vgSqJLuou7V7h2
84a1tZdI5HaPt3WbRZ6rb7j2ofMoMq1WXOH6/5YMV7iOAozdycvXHQXYUrgS9NTi+ZC+6hEX
Q2drcriYP3I4pm8VXc9OP9Nt6LyeJrbtpGxG5cnwyYXnlXpZ+poJHdCImAZjchaFcJ2dOWai
OoRiz4uooe/dD5enL1FslOhaIr9pOqPwDjVRnkAnKNJwNczgf1sL+8lOfaNHfrkye/D+6a/7
R9zSGQ8MAP5y6rFADrfQs1MVvQZtaiIzGGyeeOLfS18LhjZRXgGGFlfQ0CdQlUndPTkVUzKQ
TdlLZnmK6RFrI+Yr2PjcxsaSipBqN2+e/v7y8aQvxsRiufzOi+b9Amqy8cg0P3tmjoFiMmia
lgDjhSA5CgIR640DnhlCMmOW5FjlinkTYTEGunrlZOx1rG6apkwEV3Li69dPBQtWXBku5x0R
lk3s1JyGw1UYwylBAg2vFUgAANUgLiAAOXoKmZ7GiFU/fyG2ALs2/02hnU60lJdrRCi59k3F
JwFPNMHQghkaBljV0AxkenJha4dX1bCHEPqlJ+PaoF4y2JbyyPf2oZMePcrAVQ5Hw1e4VuNa
PcGHHKpHBk6uFdm4EAygl2Jv/3CBHi0BdHJtjUeiMQ/mEY4uUeAE2MMFb75GJXfnL9pagEW4
BvWIFVxRg0c04KQ5QmxCvD1bKebhGmDSy5TdZB+zIRM3g3OBYrKtR2wA11a8PcEVsZwrzkVX
eTkoSgKAp3H9FmJxeRaFTZw4zn4JUHpk9CXJMPSBVkK7EOsfKTUATrERBU7/SOxJjAl+XjJr
jTSPNTHYZ+Aq/zYUmXrmGf67q2CZ/BIr1/dRMZ8My9F9sLWhhVgw3ORwQiCax9ms1PYhTsRC
S0DPrdT2CGmlWJCACHLoEGiuGcnIoqEnnh0EgfIBXFvx9gfXzc3NIXBNOhzRScEtqZkCl9/H
yrObir0BEkrgqmgcN4GT4exRU7SHQAPZymJoL5QsTCY10RqUia7mysldnFw5hmtyhxljrASu
SeTHkAtMYfltbCKFOJBsj2yOyXAthHg1MtvRWLGAWATxGuQXhcySpzzFL50BXFvx9gfXlQsr
Obj6oOeVIO9t8lTIyqsskhnQl8A1uT1GlBSzHazBJlkD0wfe5HY3tnggeTsz+W+8bfo+nE+G
5V44U/LVCoFJRMX0sZcXlppiURk0lrhg4OKWDCd5W9WKYsUCdlY9ZSt+abAkWQrILMnoGmuY
hGshb4mtutEsv7E8pNSUlBv7ridrgivwwLmFEB2EUBrZAa5NSWagmNWuyq1Dus5aYEoSqDVW
POV8/MyvWZnEj7GSrqyapN90Kdh2gyu8qmmpseeU/KY82RuQxFsnMdS4FkiScE1m3U1zpL9Q
sXjWcZYRhFxVpEB1nDMPhWsr3nJn60A5DriCgWRtVhmykmdDYEl07QmuSvLjRqhU6utD8Tjh
iocFNWRhg/isbVVh5ox/ALZkdRT58vgmgMELpJuKz0puaeaCefDHeGulWMzOiqN9sikQABjl
VcMLGp3YMF8ZbsXbAYTlLOOAq0JT/I0H1MVVmZ2Cq4Viwr4viZECSP+dgiuxS1jFR32yh3/L
a1vBVS6Lg/rqFGJtRcjAVZhEgq8A48rcGhI6w7WVYskEJMiH4yIcuqEqvCDZaua2ePlJBUuP
JlXIW469DpTjgGt+m6dq0I5HV5Wpgu9AZtAdjK75jW6r6GrbuWT5RylfBq7y7GQyCfhHia5t
FUvCVauJKttx6SuDDdnQR+YkXJMSYt4OICxnGQdcW+WNyegqCfFGN5hnq1ITvF4xpetNlad4
76oc3lehvTLBGhToqczN11E9QYDAthvduNRkPVatTfrH0L2rnLjpCNQocG2rWBKuWjJUwIuL
ydpvJ8+BxKMHc2nFW469DpQ7D9eSvWs3uBogmyrJ5XDVttaDWfE2GY2HfsixWJSMcsGHnDxc
4y8ineGqRSQTXTNwJSXuD64b7xz46+CgV6ypsGwJsDauPhHIfIwZCtcm3tPHD/3q6pOfDF7w
m/YOICxnGQdc7WhUrJY+yfpkWN8//ZkK/4E0X3GNY6BABVd8QkOnsuxjlQBJ3h5TooCipYer
scfHJOzYZuaYhBwr/jYQH5MQimgxtvEz29aabeP0WGPBbqCKE1qeaveYgasIYp1fP/Xsez/+
Qslue9e8Yr+9dagQrsrn7eCUt1imkKYlz39XC5aeJt5XXnzm77en7g22T32UQ24UynHA1ao4
4Na+keDT2tPGnyitH2I7GxycGU7OOYarDa1zvza6lbjswykozRxXlEoertDbSUY7hIi2hYcQ
5VjaMuEu+ijSdAhR/Sre2hcUdcZnKvyJRYgVGQyEVsK1U4HQCOESlYGrLRz+4CQq/fNn+z//
6IAWjm5whauVYk3RVVvW5CFHf8rKbMigViTz1fVkpqCX5XkHN+b/NdgPXD3vKGgcyjsOuKIE
sImP+OuQfRww40P5dniobXTV0MnPSHQG0S95ZF8/Qkh+ak7+/gGxhUf8QWzwTYVbbZPwDO8B
BB9QEXzL4VauExB715fbgUB/fAqW+FsO8n0yfO6l71x57Vu0S68cWJh9DpkvTB/m9ubbT3+w
vojMXw9O4KZ3B/P8vb+x79O1fVzQoJHPvf7y9KeDmY9vbHMhzRyR2wtLBy+vzEH80dVn/aNr
l2Y++fCI5MC7NfjiYMO1tw7RY+sILDCKnXRUkgl0SEbVO+vTYr/yo/BMIhO8vb7AU9JXhGsg
rmVJhMDOfJEjCZq42puvznx8c5tes74z2F7aMAJLFWqYfeDFUENR15mg9Ad0nQfwjPi3ne9P
/nrOE9vv8oZSFupmQ2dyVIkyypKfBHr6Dqrab+KG/hqGgeJf22XmriP4TUf2M+P+/NLjn737
2C9X93Pxu2vf4BowMNCN809xjUeemDvyx+tTn7+/9wffOypiyLhQE3isEzlcq5/G9d0rUxKr
9tPl7UOjiBXL1uUn2BAi/A/vTsn1NYTYde2bFgixaywgRND7bH2fx5uhGgIpr/bn63ulG2NJ
K5s4t5LAomAjius3P3kc2y4vPSeDQMMs7LrQITuQjRWuHfSrLGO2AA6K23148UmNq1uAag6N
Eweu6dEoLgHbQp9u5frCpPAPQoCuoCgcWrAlAitaerhKGUMRxAK/wZWBlhaniXvExj+9t9dC
vdlQ64gpI4G6RVt00ExpEu6hqGUFSunMhaQpfYiN0MeLq3Dt5X+T6ONVjUemXBDHVTKsqGWx
UX4pkFjWF3uqsGcSdCuIBsQWOQVpGxe4Wmw0GtPNTCFlDK4QaA98a202GetiCV43S4ZJiT1c
PY2CLXKYvkljBaH1mgZryhWuFa7/YwG5IGg0sGm36ZNJCMgYPWYMz+qUf4NAL8Siqyc2KOLr
0HOrhFZNCC+HKzVq7fBXzx9rC1fUUzKslF7Xiq4BXINYrWCL2hWuFUvjtoD2aR5RBlfLXS14
ynGNXlDnL7m0gQ0CqwNloquFLMn0+XYAV1sp0IdRwBX0768c/NutqTu3t+vt1G8tTQ1SEluM
rF9gY3TlEcp4g2RY07FEndiLSlJYkKaHC9tB9JcH1eg6bjz09y6/LMkKLERU3FGOqB2dIMRf
q8ooYIqefgVG/Fhc+DQSaOrPR1dBAiHQAwyxKKr7UpPGUtxWv0BCeZbyEqValcHzcBUXEpSB
C/CMpSApnaWPoqumYxmHdPBwhUb0tvX9st5FIKfCtcI1tIChRa6MC9o+zXZofk+LoytG8VT+
rczWyr9gwEJTELoNiqr08FQNgRboPFy9bkKdClprbx6gYEup1iftTaUmBGrp0UCmG53SmU4r
g8fTEW6VNRikmbJfYipcK66qBUa1QFxq6glXPYmt0XVUD+jpxVSxfVigwrW6e7XAxFigwnVi
XlUfq3WVOVkW0Cbczm9MlvJoW5PhutxUC0yMBf4D+GpKfTpd4gcAAAAASUVORK5CYII=</item><item item-id="3">iVBORw0KGgoAAAANSUhEUgAAATwAAAAuCAIAAAA6DjJ1AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
DTpJREFUeF7tnf2PVkcVx+s/a4wpLvQlVqgt5WUpXRDKFrKU2hfWSmEtLRshsrZQCqS79TG4
0ZpgTcVo1ZqoJJrQ3/CzfsnJyczceebeh/u8yJDJ5t6555w5c+75zjlz7jztN+7fv/9Y/Vct
UC0wQxYAtPVftUC1wAxZ4LEZ0rWqWi1QLbCVGlcrVAtUC8yWBSpoZ+t9VW2rBWqkrT5QLTBr
FqiRdtbeWNX3kbdABe0j7wLVALNmgS3Q7tuzr7ZqgWqBiVjg9Funz509t7G+cfdfdwtXjwra
umBVC0yLBU69emrzV5tDoVtBOy0vbCLLfB10Ci2weGzxzp07GehW0FbQVgtMowVWL6zeu3cv
Cd0K2ml8YVO4/FeVxm8BsuUkbmcPtH9c3Hb/5DeXX3x2/EZsNeIoer5z5shgcJK/rUZ8pIj7
NhH2p03cpEncVtD2FWkraHv1+EcEtNgwxu00gnZx/+6Pf/D06sL3km99FDD06kaB8FH0HNEj
33j9EBIOLxwY53zHPNaIJhqq7YnjB2lDycZDwP7Wb26nEbSkviTAOH0FbTefWFt7hdRuenyu
2yzyXH2Dtg+dR5Hp68kVtP+H6XEF7SjwmE5evgNZsG0BWgKgWjwrElo94mLonE0OF4f27onp
W0XaU/PPdRs6r6eJbTspm1F5enzs6ItKxiyhzYQRaERMgzE5i0LQHjyw30R1CMueF1FD37sf
Lk9fotgokbZEftN0RuEdaqI8gZ27aAStIQcv3Dy6g3zVN3rknSsHd9098W3/iFs64+GBwd+O
Px7I4RZ6drCi16BNTWQGho0jT3699K1gaBPlFWBocQUNfQJVmdTtY3MxJQPZlL1kFqmYHrE2
Yr7Kjeetry+pUKl2/fqJHy0fTnpkTCyWC++/bBgQXJONR6b5qZMLDBSTQdO0EBgvBMlREIhY
bxxQzRCSGbMkxypXzJsIizHQpYvHYq9jjdM0ZSK4khO/euV4sGzF1eNy3hHB2cRORUrBdjho
hTRcEzzQ8F1BBRhQK+ICAvCjp5DpaYxb9fMXYgu2a4e+K8zTia7ydY0IJde+qTQl+IkmGFpg
Q8MAsRqagUxPLmwF8aoaAhFCv/RkXBvUSwbhUh753j500qNHGdDK7Wh4DNdqXKsn+ORDbckg
yrWiHBcCA/RS7N2fHKVHCwGdXFvjkWjMj3mEu0sUaAH8cMGbr2DJ6fmLthZsEa5BPW4FWtTg
EQ1QaY4QmxBvz1aKedAGyPQyZTfZx2zIxM3gXKCYbOtxG4C2FW9PoEWszicPAa18HSwlYcDT
uMYLsbg8i0IorhznwwQrPTL6kvQY+kArYV649Y+UJgCq2JSCqH8k9iTSBEIvmRVHmseaGPgz
oJWXG5ZMPfMP/51W4Ex+uRUAfITMp8dydx94bWjhFiQ3uZ1wiOZxfiu1fbgTsTAT0HMrtT1O
WikWJCMCHjoEmmtGMrJo6IlnB0GgfADaVrz9gXZjY6MItEm3I1Ip0CX1UxDz+1v5d1NBOMBD
CWgVmeMmiDKcPWqK/BBoIFtfDPOFkoXMpCZaiTKR1hw6ubuTQ8egTe48Y6SVgDaJ/xh4gSks
441NpHAHnu2RzTEZuoUTr0ZmmxorFhCLIF6J/NKQWfiUs/gFNABtK97+QLtydmU4aH0A9KqQ
CTf5K2TlNRjJDOhLQJvcNiNKitnO1sCTrJDpg3ByGxzbPZC8lav8L/Y2fU/Op8dyMlwq+YKF
wySuYvrY1wsLUbGoDCZLHDFwdEuPk7ytKkmxYgE7a58yF79AWNosBWSWZKSNNUyCtpC3xFbd
aJbfWh4O2qbYGHuwV6IJtIAEFxdOdHxCiWUH0DalnYFiVtkqtxEJPCuCKUnQ1ljxlPOxNL9y
ZVJBxko6tOqWfjOmwNsNtPCq4qXGXlTymzJnb0BScZ3fUONaUEmCNpmHN82R/kLF4lnHGUcQ
flWvAttxFj0UtK14y52tA+X4QAsSkvVb5cxKpw2HJZG2J9Aq7Y8bYVPJsA/L4wQtfhbUmYUQ
YrW2W4W5NF4C5JIVVOTL75tgBi/AbipQK92lmSPml4AYda0Ui9lZd7R/NgUCGKO8KnxBoxMb
5qvHrXg7QLGcZXygVZiKvwaBvbhmMynQWlgmBfAFM9IB6T8p0BLHhFg81ad/eLl8txVo5bi4
qa9dIdbWhQxohUwk+CoxDs2t4aEzaFsplkxGggw5LtGhG6rCC56trm5LmJ9UsABpUoW85Qjs
QDk+0Oa3f6oVTTzSqogVfDEys04w0uY3wK0irW3zksUhJYEZ0Mq/k+klS8AokbatYknQak1R
9TsujGUQIhv6KJ0EbVJCzNsBiuUs4wNtq0wyGWklId4AB7NtVYiC1yumBL6pLhXvaZXV+0q1
VyZYiQI9lcv5WqsnCHDYdgMcF6Ksxyq6SS8ZuqeVKzcdnxoFtG0VS4JWC4fKe3HBWfvw5OmR
ePRgLq14yxHYgXJaQFuyp+0GWoNlU7W5HLTa7npIK/YmI/PQTz4Wl5IRL/jkkwdt/O2kM2i1
lGQibQa0JMn9gXb9/Z1/H+zyijUVny0l1obWJwWZzzZDQdvEe+Lw7t9ceurzwUt+M98BiuUs
4wOtHauKldMnXJ8e63upP4nhP6jmq7JxPBS04IrPdehEl33WEizJ5GNKFFDk9KA19vhwhR38
zByukHvFXxHiwxXCEi1GON5m212zbZwwayzYDVpxistT7SozoBVBrPObx5//8KcPlOy2p80r
9vsbuwtBqwzfDl15i2XKbFr4/Be4YAFq4n3t5ef+eXPuzmDrrEg58EahHB9orcYDeu1rCp6t
vW78SdP6IbYzxsHZ4+TMY9Da0Do/bKNbAcw+tILVzIFHqeRBC72dhbRjjGhbeIxR7qWtFE6j
zydNxxjVrwKvfWtRZ3wSw595hFhRwqBoZV47VwiNcC5RGdDa8uGPXqLSv3+x46tPd2r56AZa
uFop1hRptZVNHpP0J7TMhgxqJTRfgU9mDXpZnndw7dB/BjsArecdBZNDeccHWlQBPPEPBnRk
Pw6e8RF/O3jUNtJq6OQHJzqDSJj8AYB+0pD8NJ38NQViC38wAG6Dry/cavuEf3g/IBCBjeCr
D7dyoIDYA0DOBw790StY4q8+yPfp8elXvn/xjWdo51/befTgC8h8aX4Pt9ffffbjq4vI/O3g
CM56e3CIv3fXt3+xtp0LGjTyvDdfnf9icOCza1tcSDN35Pbs0q4LKwsQf3rpef/o8vkDn3+y
V3Lg3Rw8OA5x+Z3d9NhqAguMYidBlWSCHpJR9dbVebFffC881cgEb149ylMSWoRrIK5lSYTA
znyRIwmauNrbrx/47PoWvWZ9a7C1wGEEFizUMPvAi6GGYq8zQQ60nYXmGfFy+7VA8nd5nt1+
8TeUslBbGzqTtUqUUZb82NDTd1DVfm039Bc2DBT/ji8zdx3ob/oBQGbcX55/4ssPHv/16g4u
/nD5O1wDCQa6duZprvHLIwt7/3xl7quPtv34h/tEDBkXaoKQdSKHa/XTuL59cU5i1X6+vHXs
FLFi2bzwJBtFhP/pgzkBQEOIXde+aZkQu8YCSATAL69u96gzbEMg5dX+emWbdGMsaWUT51YS
WBpsRHH97mdPYNvlpRdkEGiYhV0XOmQHsgmAtoOWlWXMFsBNcb5Pzj2lcXULXM2tceXAQT0m
xSV4WxjUrQAgZGoVACcAWIAUGi3wEo0VOT1opYxhCWItAQZaBlpanCcGEif/8uE2C/tmQ60m
powE6hZt0UEzpUm4B6QWFyilMxeSplQiNkIfL66Ctq//bEUfb2tsMuWIuK/SY0Uwi5PyTkHF
8sDYX4VAk6BbATUgtigqYNu4gNbipNGYbmYNKWOghUB74xtrB5NxL5bgdbP0mCTZg9bTKPAi
h+mbNNYRWq+JsaZcQVtBm7CAHBFMGuS0C/XpJQTkkB45hmp1ysvBoRdikdYTGyDxeOi5VYqr
JpyXg5Y6tnb+q2f2twUt6ik9VpKva0XaALRB3FbgRe0K2oqoyVhA+zePKwOtZbMWSOW+Ri/A
85fs2iAHgVWJMpHWwpdk+gw8AK2tF+jDKKAL+o9Wdv3jxtytm1s1eWq8lrgGGYotSdYvyDG6
cgrlwEF6rOlY6k4cRiUpLGDTw4XtKfpLi2qknQwq+nujD0uyggzRFaeUO2qnJyDx12o2Cp6i
p19BEm8WF56NBJr685FWwEAI9MBDLIrwvhClsRTD1S+oUMKl+EQ5V6XyPGjFhQTl5II9Yylg
Smfpo0ir6Vj2IR08aKERvW2JH9a7CORU0FbQpi1gmJFD44i2f7Odm9/r4u6KVzyVlyvXtRIx
SLAwFYRxA6TqQDxVQ6AFPQ9ar5uwp3LX2ts7KepSzvVpfFMhCoFagDSQ6UandKbTSuXxdIRe
ZRAGbKbsF5oK2oquaoGHY4G4ENUTunoSWyPtw/GDnl5PFduHBSpoq9NXC8yYBSpoZ+yF9bFy
V5mzZQFtzu3Ux2wpj7Y1Pa6LTrXAjFngAWgz/6P4+qhaoFpg2izwX3+trIvTsxfsAAAAAElF
TkSuQmCC</item><item item-id="4">iVBORw0KGgoAAAANSUhEUgAAAIIAAAFFCAIAAACv+6ISAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="5">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAYCAIAAAA9NpfkAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ASVJREFUWEftVu0OwiAM1Pd/6DkD4tFrS8VgVuJ+GdbF3lfp/TiO297PiTDvc0qjNo/nekUK
zBa80nx7+0d4VTWFgG2SYL+lJquGXdIgjXyeEiEL2KRTtL2qDQd9ba4hjsrh75QulfeBlsOt
bgvVsT9CyOsgLolisotirsQClFGZn6jq6knjE8zXl+NAETmVIIazPIfqbhU/7PYvYwv1RaoI
cSew3GLxPfiDV1vWfOerjPGXlubs9v7MsZOzwjMdog8Oj2U2tdK6yuNoO2LqItezNU1eZKar
vlilYaPW2YkcfSwjxSPnOEWQFRfwCYqbZl6/kTGC0PFOnG4LtoewycL6BDHjAMNEWcNMRFF8
7gc1ivAjA6QonhzBKbDV9Cbqda7V/TV8AA89GOa9jal+AAAAAElFTkSuQmCC</item><item item-id="6">iVBORw0KGgoAAAANSUhEUgAAAE0AAAAYCAIAAAAwKOejAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ASdJREFUWEftV9ESgzAI2/7/o9U7tSKEUNs5h85H7NWmgSS+h2F4PeGZcGZ/JpogBFnHKxIh
90DOEMrbP84MnFoyvUpuPhWqWU8VQXMlMU44mV4xMU4pM4XDG/L5IJwWqp3YxWAyyCo7owfs
Av+0sVKGTQmiZqVco8Aoei/IQ7tPrtIfCuaWZvZuQYIetJbv+UoIiQim7kAnzfIBXHylNAP8
fSFtVjnekBkOHjoET7PkMJt/hq0Vdou8LBtK7FvX09el9twfwFmGOARcyaESGK4Ztm/P4hPi
9IqhSMKQ2VyUQtpGKU69loq23bm6QOurLx5qqxinMiXYjYHWoXnjVgl1sXQ+EQLvJFU5vpPM
Qxd/0uIY5w1ABjmhoT1OYqN/25jP/m/8wg4jS3qoVnO4bs4AAAAASUVORK5CYII=</item><item item-id="7">iVBORw0KGgoAAAANSUhEUgAAAFsAAAARCAIAAAA9l0fEAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ARZJREFUWEftluEOgyAQg937PzQzgVyOtsDNidMFfhnEg/toG18ppW0NT2AnsoYnsC0cQGAR
QUk8g8huc5l3frJSPiUjr2xFxwOIZBx5yGc/+dFK+7Aqe/McgW6HzUsiIB+G61kj/qwlxiQn
JU1TuG3sa9qzidb0zOt5U32r3QPLIp0eS/P+fCDOoT/lgojIuT2YaTUD19ZqD5qSSuHJgUUP
eCpue1ATiEVezHUaaYUWEKEIH/irE4TePnKXPpHghXfs37LkDzRSxVgdARHXDDOOpWc79nO6
fCjzjPM1mKxsXWlmKX5zTfAtnJyj2itaFpfpeff/keBNHMg7tmpB/H2t2RUugFLl3ex+Tqk/
FQqGyykn/qcib+NolTG+9ztjAAAAAElFTkSuQmCC</item><item item-id="8">iVBORw0KGgoAAAANSUhEUgAAAEEAAAARCAIAAAANG1blAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ALBJREFUSEvtldEOgCAIRe3/P9osGhEyBXQzWj75IMi5F3TLOafoqzBEXyk6wNFHn2IoQ+Hm
wYFyZ3AEYsH+uvFWCj8ihAkDVIOQqAzwnHKGWkKlqA4fIIRqaTLhKp0xNNLVnwe7z8rAhkep
VA2p8kGpjZWBtgHbo8BMOLESA8N0H7oMVu3umR73VBS1LaE+pN9L4gOvp2qHszz0sPtjeSRR
GvfmYxP+h+V4P8NyC84CdoxGL22+tBo4AAAAAElFTkSuQmCC</item><item item-id="9">iVBORw0KGgoAAAANSUhEUgAAAQUAAAARCAIAAABbxC7/AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="10">iVBORw0KGgoAAAANSUhEUgAAAJgAAAAYCAIAAACJEKOuAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="11">iVBORw0KGgoAAAANSUhEUgAAAGcAAAARCAIAAABPWCVyAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AU9JREFUWEftVtESgzAIc///0c5dFTFAROrO7a4+7GEWAiGhvuZ5nsZzlYGFtfFcZWC6GjDO
f9w5WCgw8EOsLbul0EDby7XAfBRAfB3PVuY22dZxvg05WYvqBKoUWoDUIVGfhf4LIT3FC9yB
Nbl/9SQjaeThdVoCoYFAeu0V/KJrttT2W0r+cYUvWPbzQ6dqsWsNVuo7o5tliDoAifjRptUr
SdMEJyPSbVXA+6HP2P4kSmcIWYum12MEUA0s76hikORpYzB72wh3hlYxMOWmwr3mzjyCdCXt
Ho7SJulIHgMX528MyxpPtbNGjNmpNXd6UCjYkAzcdTq5bVxGyCbJyPbAmusml7I8j+A1u5gF
FASl2dFn3PMRaxAow7D1J9tcZ8AN/+Nv88ODRpKB0X584Hvt3kkk+ycWJls7lPC9PTySrUDc
6ZUKywpF90if/w76BgZKxB5u3NbFAAAAAElFTkSuQmCC</item><item item-id="12">iVBORw0KGgoAAAANSUhEUgAAAFEAAAARCAIAAAAqtdcNAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
APBJREFUWEftVlsOhDAI1PsfurubRhxhoNRH0k3tl7G0MA/QtZSyzLa+mGdby2yAf75+Md/J
gEwJe6ndioPrDV5Mfa+y2DcS8JTOmFKlt1txcN31Yig2ysJYmFEijxGK2QOsONIWUPkUQzFh
QSd06bwrcLQoKhxwQX2U8rYEBQ9YnPqoB+3U9Da2q8XveRv1yNRMvG0lRVS9w+2czuLJTM9n
YmjZZNxZzoLBK7xc11nUxhFNeY+Vb0q1Y+7ydlP2izrbqdararafPXs3EXILbYTTFlXd6PkF
J7CNyZwiJj2H569PPfVPMjIpL+aR1bmvtg/kVh+ZgGyl3QAAAABJRU5ErkJggg==</item><item item-id="13">iVBORw0KGgoAAAANSUhEUgAAAFcAAAAlCAIAAAC4QOLxAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="14" content-encoding="gzip">H4sIAAAAAAAA/+xWPUsDQRCdzZdJTNBIULRK4R/QLhBEDSmCoKJiK8l5xUkuwXiglvZaaS0G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</item><item item-id="15">iVBORw0KGgoAAAANSUhEUgAAAKAAAABmCAIAAADDHA3TAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="16">iVBORw0KGgoAAAANSUhEUgAAAEEAAAARCAIAAAANG1blAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AMNJREFUSEvdldEOgCAIRe3P/XPTYUrgFLAHybXmgyDngnCllIL3lRm8r+AdoNTRrxjyozDz
tAdl9mAwbAHb4263YvgdIVQYoBqYeGWAdkoZuIRCUQ15ABOspSoJNXTCMHHHhwe5T8tAHo9Q
KQ4pyoNQGy0DLgOybwIT4YaRKBg+z8OSQatdf9P7OR2KOpdQbrKupWGDl1PNzYkffNg8WF5O
hIk7+djTYiMqKrQ/OfQ+ZEtFxpC/2hPQ3gVAndMAwP9eGG6NHRaLKAp2UAAAAABJRU5ErkJg
gg==</item><item item-id="17">iVBORw0KGgoAAAANSUhEUgAAAPMAAAA0CAIAAADQeZlkAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="18">iVBORw0KGgoAAAANSUhEUgAAAO0AAABCCAIAAAA0ZcbjAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
BQlJREFUeF7tmtGi4yAIRO/+/0d3bzddS0GRRKhopk9towjDCcEkfx6Pxw8+UGB1BX45xgcK
rK7Az+oBwH8o8OwpoAIU2EABcLxBErUQfvteebj659JCgOOl03fF+WNHd2Vm4jm7xZNY6kSu
geNEybiJK+WG2BHvgWABURbX6vgyl03fRkPU49SppLwe+FJw2VHGdxlPTwD2Z+rgzzgHjs+o
9fWx1XJbvKDPLljFpWMYxxT3rwcUtSA4jlLW0W61Bis46jUbHDumBqZMCiidg+wi7P8U0LfZ
8KEem3iaNUg2xKzTqP5ku0C5KdwG33f7NCtDWBcKOCqAeuwo5mKmdnogAo4Xg8/X3W0aDHDs
C8Zi1sDxYgmDu1UFwDHA2EEBcLxDFm8eA7upt7Qa8/vjaknYpk4sDcdCzmscz4Jpp/tBC6Gw
tKsZOd7yBYClKcnv/AfHtGGS349gusXyqOLyWej7EWLj5UNqXLkUzLpK5M/lnT18c2x8JUUv
lvJ9gEJ/9UvrzawurGWhnTYrdwZxMPYPjhk99Gfru1yejZRkt1axL2GMWbKOf7ZU4FlbGRPV
AslqsF4sLTi2yjDtPXRYUY+NJ/NNhvX7imq/a+xflV5FP1RA7zYYN8kTwuzUtdb2S27pLGzJ
bpWW3mKTQsxqMztt9L4F2YUCr30XhIACGygw/3meIiKr5V5yb7nXuW1Qo/W4KpwXavYN34UV
I3ruCJsXQtt+Skvn1PWY3SfxSpI7c+4GvSLd0k5VbXA8mus4iOMsj8Y8e75UBhyP5iSItqC9
wWi0OeYvxrG8i+cioyN5jqZwh/FUcpny2evxqdgsg33J87XG/A81btEq8xhw7HnqhqIWajwz
oxbfwLEbx9GcRdu34JJ5DNXHLamZA6a+OcLhaKqqXrT9VVLW8hMc+2QwmrNo+z4qzLMCjn20
D+Us6F6NT+Q5rIDjdx5GWByZm4OEvheZYwTHr/wNPmvInOM+oYYRg/oYVhgaAo5Rj60KZD5X
wbE1i3q5yJxje6HTo/hg5V99PizT9p218qyKH1PYRHYHqVhQhsmIwDE4tipAwSoEsy8MU3pU
In7BSOucBMf9LNIK0WoTW5VMzp3+T7WYnYpRlsyCr6zZVDGmUotsWbO73fl9OZbkjfQGI3Pt
1/3okca+ojqM9QyUxeI2o7xay6utSDdwcNyvx10RqzmzzMo25izHsr6yZne8c1Camepaz1xk
kzXUH/0ad3bp7eux7AEsFzTWVMhaS82y3oZW8a68qMdnia2P7wrts8zWVvTqrocOjn3QAMfj
Olo4bm34wPG4/k8L3+fYuKJxmI8Kw1Zoa9EypuwyX+34sBsrGfBNsK+1ro6nljs1uLv09AHg
+CMFvtn1tWbvBY1UfdM9o0uXh4HjaRwf0pcEtPb+cvP+ulD+PyAbGH2/T7vJbVAGx4EcKy2y
pJbeUSqkGr/ILH5sccg7D8ylPThmYjLRbtEfs62ue14Vg1XUWIWmKWHjW9PZ+cAc0H9evqwn
nMgjTeiir0tdIEaWG+e4nGnVosuaE+pq9RA4Hslm9rkKDeOut1Du1uNqba52GpYlirXQ83Zc
LhcLlXPexW5yI9/nWLZ0epMn6yjtiCwc07pexrv3UUkSfVOOj1oVl9Q4y3J3aCfpC17ZnXEc
WY3rLu8JhXKs3LiIzl/L/q0gfurvKHRyU9GpjbZvP1u+4MmUXCtxgeMpGcGizgr8BfE9RPaX
cp9DAAAAAElFTkSuQmCC</item><item item-id="19">iVBORw0KGgoAAAANSUhEUgAAAN8AAAA7CAIAAAB0TsjvAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="20" content-encoding="gzip">H4sIAAAAAAAA/+JhYGBgBOJkIOYCs5mAJEtJUWqqCAMIOAExR26yf1JWanIJWIQhCYhtgJoc
gDQzWD2nc1iwa6FnSWouI1gFDxCzIQvwgkkWsGIeoGLn/NykfJA0xEQBIGYHCiNE+EF2AB00
Gah/IisDQzSQHwDE//+DVAOV+uekOudkpuaVgPUAVbFBfQIlgXovnBd8sHCj1EMGNGAHdPW/
/5xgHTDACNcNcQ4TlP/vP8hGCPg/CoYU+AvE/5DwKBhZIIghHwhLGBQYXBnygHQRQyV6UYAX
iDGwwvM8qCzYxZ4VYGTD5jpPsvHt+08ML5DVfnBasWTK5COMzEC2A7QgcQbaDrI3FUwWk2Q3
CAgxMDEi+4dYfZaMhNUQC8ixn5qAEvtBwQAqx0FxwsIAKctHwcgBDAzcDBmcDAyuHJD0wIWS
MuBsRmgScQdiEYisCJSZkgmS4IfqhbUXMhGaQVIVQPzlP9bkCbbHsSgzMQdJ8IelJC+yom9E
6mUEpmUOAVRFNiAipOekYfIFwWW9DAtUzrNehDVlONA8imIwnAPKIOAmHwPU98MHgD0KAAAA
//8DAAFvxQLhCgAA</item><item item-id="21">iVBORw0KGgoAAAANSUhEUgAAAKYAAABSCAIAAABR6TgvAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="22" content-encoding="gzip">H4sIAAAAAAAA/+JhYGBgBOJkIOYCs5mAJEtJUWqqCAMIOAExR26yf1JWanIJWIQhCYhtgJoc
gDQzWD2nc1iwa6FnSWouI1gFDxCzIQvwgkkWsGIeoGLn/NykfJA0xEQBIGYHCiNE+EF2AB3U
CaT72RgY4oF0ChD//w9SDVTqn5PqnJOZmlcC1sMIMhziEygJ1HvhvOCDhRulHjKgATugq//9
52RgQxJjhOuGOIcJyv/3H2QjBPwfBUMK/AXif0h4FIwsEMSQD4QlDAoMrgx5QLqIoRK9KMAL
xBhY4XkeVBbsYs8KMLJhc50n2fj2/SeGF8hqPzitWDJl8hFGZiDbAVqQOANtB9mbCiaLSbIb
BIQYmBiR/UOsPktGwmqIBeTYT01Aif2gYACV46A4AdUOo2XAyAIMDNwMlzgZGMK4IOmBCyVl
wNmM0OThDcQiEFkRKDMlE5R2+KF6Ye2FTIRmkFQFEH/5jzV5gu1xLMpMzEES/GEpyYus6BuR
ehmBaZlDAFWRDYgI6TlpmHxBcFkvwwKV86wXYU0ZDjSPohgM54AyCLjJxwD1/fABYI8CAAAA
//8DAFs/8crhCgAA</item><item item-id="23">iVBORw0KGgoAAAANSUhEUgAAAKAAAABmCAIAAADDHA3TAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="24" content-encoding="gzip">H4sIAAAAAAAA/+JhYGBgBOJkIOYCs5mAJEtJUWqqCAMIOAExR26yf1JWanIJWIQhCYhtgJoc
gDQzWD2nc1iwa6FnSWouI1gFDxCzIQvwgkkWsGIeoGLn/NykfJA0xEQBIGYHCiNE+EF2AB2U
DdTfz8bAEA/kpwDx//8g1UCl/jmpzjmZqXklYD1AVaxQn0BJoN4L5wUfLNwo9ZABDdgBXf3v
PycDG5IYI1w3xDlMUP6//yAbIeD/KBhS4C8Q/0PCo2BkgSCGfCAsYVBgcGXIA9JFDJXoRQFe
IAYsU2BmgcqCXexZAUY2bK7zJBvfvv/E8AJZ7YfNq5ZMmXyEkRnIdoAWJM5A20H2poLJYpLs
BgEhBiZGZP8Qq8+SkbAaYgE59lMTUGI/KBhA5TgoTlgYIGX5KBg5gIGBm+ESJwNDGBckPXCh
pAw4mxGaPLyBWAQiKwJlpmSC0g4/VC+svZCJ0AySqgDiL/+xJk+wPY5FmYk5SII/LCV5kRV9
I1IvIzAtcwigKrIBESE9Jw2TLwgu62VYoHKe9SKsKcOB5lEUg+EcUAYBN/kYoL4fPgDsUQAA
AAD//wMAjT3MveEKAAA=</item><item item-id="25">iVBORw0KGgoAAAANSUhEUgAAALIAAABmCAIAAADgR1wGAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="26" content-encoding="gzip">H4sIAAAAAAAA/4ySzW7TQBDHZz9s59tNWlKahE5Ji4SlqgqVWuBmWRESEhJSzyDjOm4b1DYh
NVW45UUQlz4Ddx4MwXh3DKInbM165rezO+O/pgEAgiwlqxlf0VrJPp1k59PZtYbiGZNVJ7PU
MmXY6yLtKn17+jFLc0Pg1JyXtLo3F/NotrT4HZlH4ORP4nuykOyDB/CVqm9W/sbF88blrkAq
89X5Iss2zF5E1pwbV4fR1g+AlXLC6Bs5oXbZcTx23Ao7XjWMpA9wS6Vc4YwOXj47hppl34k5
Qo8Ojl4A1O2BldcII6DNSZ2EqcbX8ef4kl5oWnx3DwO0wkj/pIOub2+dtACUvP0Caza+o1iK
KWW2uYTT4eba6+x0NrjL9SJXjOABd1jG0OWznU3bR9ilCx9af9U1smxx1DZRjw/oPtfoDdjp
P+Jig1KS0RFsc8EBS3L8HAD5iv4OS4L//vtjlgTvSzJkSXq7LMnQSLKEPZZkWEryxIyNPzmM
zxfJ/CI+my2uktxO324xfQaPkzyxE/LUfkDu0KJ4WopE3/hFVEuW05tX5h5pcveLyRLlZJnn
F70g9v+flpd7+SJJs8Ox3QoMxzUQAYhtIUQgBUoRKIlKBFqhFoHQ6BB30CXuokccsEJcYJW4
xBpxhXXiGhvEHWwSd7FFHNAnLpBb+Q0AAP//AwBwDeaetQMAAA==</item><item item-id="27">iVBORw0KGgoAAAANSUhEUgAAAV0AAAC3CAIAAAA+QhqwAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="28">iVBORw0KGgoAAAANSUhEUgAAAIQAAAARCAIAAACTjZMAAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="29" content-encoding="gzip">H4sIAAAAAAAA/2yS32vTUBTHz01ufq5daluts11v3XwwMErt5ra+hVAEURAGvk1ClsSt4taa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</item><item item-id="30">iVBORw0KGgoAAAANSUhEUgAAAWUAAAC9CAIAAADk/fsaAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="31" content-encoding="gzip">H4sIAAAAAAAA/2xSTW8SURS9b76ZQkFAsfLxsFiVpGkoBQsLk8kEmxibNOnKRc1kCtMWYwvC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==</item><item item-id="32">iVBORw0KGgoAAAANSUhEUgAAAWIAAAC9CAIAAAAGIeBjAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="33">iVBORw0KGgoAAAANSUhEUgAAAFEAAAARCAIAAAAqtdcNAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
APNJREFUWEftlt0ShCAIhdv3f2hrR6MTP8oGO15YV9nQke8A1qeUsq12HcyrXdtqwN++fpmn
OXAcKVl7s9OJZK/nWTtFdGo2EQV8F6XU+7SdghknMmNhWZHr8mKuuzotpz5RRasOqkm/2UYs
QLXgV19kfMuqoZ8QtOzUjbSsG3SE2NCFoQK5JtOQPxNWqpZxvM5OZtVCLJ2HCsEwXjbRg8Gx
muJ2clij33HRA9aJsZj7hM46JzMHUR+0Q1adzXketpYaIO3HAZbnGSOXS3WSnfCsyCzhCd8q
q+ucPPGwl/nuodWuEaeHgxMRd747oc7OzP4XtgMUviiQqFp6NgAAAABJRU5ErkJggg==</item><item item-id="34">iVBORw0KGgoAAAANSUhEUgAAAD0AAAARCAIAAADhbjPzAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ANpJREFUSEvllUsOwzAIRNP7H9pN5QhNBkzo2F1Y9Sqy+DzwQF6ttWPHc3LveI4doT8a+Ufu
cy5+V7YNnaXAGz1xj5Jwz1SFvv2bbnRuiuULmOHGaM/c1EJzIAIzs3tv6V+5owjFPHCHEHSJ
iZEeC/Yu4VPQP2OktzDaTaAYyPdm5F+0lMe3xE3R/XD4+dC4qdkj8YQA1w8etwxB5Nyo4EQn
gqBDjGyfUPpEwTQr4QRjNN+dXDmh+m+dkpXnV9WSUMUgU/v72y4WmSpmC7hHq7qSXrZZwC3n
nnF8A6aoy7QOJK5hAAAAAElFTkSuQmCC</item><item item-id="35">iVBORw0KGgoAAAANSUhEUgAAAHAAAAARCAIAAACKKr/jAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AS9JREFUWEftltkSwyAIRdP//2ibjikSFgWhSybmMSNXOVzQRyllW18igR3o+hIJbIlaS+rV
7otCLoEGdB8jcWmYRXGpLyikpFzPCVIHxAoimANWiKsFDzMMT0kZaFKgmPHwKNqCawFNSRlQ
9IDy0hntNgG0hhjNwucJCSRq/Dzieu6yCUupQDvs+FuNbOwFSgD1y9ZO/B5N5A9WA3AY8VAh
4lkZqNGJiS1vr4HoYq0kHXanCwTVhtsfOxebSUz/LxxKHGEpJ8bqcpxYOa4w0ezyLS/Wzatu
txuf5cN2m8OHo1wK07mfnk144mBFi3dIg4jhRIdPvc5GYkviTtRmKCRFbj8+Luxp8sujTW1v
KS66fhqWN9/oY96736/W3xeo1sWRSmg3eERTfep8QvTOmk981ZNrtacvswAAAABJRU5ErkJg
gg==</item><item item-id="36">iVBORw0KGgoAAAANSUhEUgAAAFgAAAAYCAIAAADxr60PAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AV5JREFUWEftmNsSwyAIRNv//+gkHR2DsFxSMuqDeeqkCniyC2m/x3F89nURuEDs66eGTaEQ
2CCqEhYCwXyasW1kL0+3iDVKv27FRE6iVc5CyWUteJdxERBleFW7plu4zRESv3OXr12ctGIX
YovZdsn47Q5dzCJDvbihYNL2qsDUx9UY16R8+YDVU77ymdM7Ggi2i50ExgwmpQLsmmWcgqsF
KXIsSNEXZA2UuBE2GF8qulKjR3oEwlUESxksFIKQWpNtNRjfB8H8/AiK0cDhIND0DDuUYSjY
Yql3aEBpMWCNtoF+iBzP8IgsSLMuewZw4LFuEgmldWiGG1jjb5sEW0Zw2StiDE7im6/92KFW
g+fJLHuLhR2naysuiMx5MnvzLKg3od06Bxgg8qVkQAzeq/7oog1scE1T0jkg5NSdUuWApBiE
NooHFDQrxUL/R8xCUAft3PTrZN+KqM9ig6ggTuirMPjjPHDFAAAAAElFTkSuQmCC</item><item item-id="37">iVBORw0KGgoAAAANSUhEUgAAACYAAAASCAIAAAC4tDtCAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AHNJREFUSEtj/P//PwOdAdBKOgMGOtsHCtRRKykIAWCKRNYNT6C0CliIBXAr0di0ikuirMTM
nGjhihZW+EOdsJW4VGCaS9BlEC2kWYmmgbw0RE0rh7Iv0Qp3rIFJfPJBzoXwqIXnHFplEjwp
YNRK8rIHQV0A58POh7xRDEoAAAAASUVORK5CYII=</item><item item-id="38">iVBORw0KGgoAAAANSUhEUgAAAI4AAAASCAIAAAACO3FnAAAAAXNSR0IArs4c6QAAAARnQU1B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=</item><item item-id="39">iVBORw0KGgoAAAANSUhEUgAAAHwAAAA1CAIAAAAMKxhNAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AqFJREFUeF7tmdF2wyAMQ7f//+iuWTrGcQALLENp3KcuNSAuwjjs+/F4fMVnMoEn9PioBJ6L
osbgAcy+8FF3jCRyD+gdBmBxJ0OfnBuXDNexSpVQMnS7oHfu4U2d/s7IjNpYxI9y0SjlJs2J
xAP6Gs+E0xdwD+gBfQGBBUOG0yHo6YUAitaCArpG6PdCMAVRypiArkPPIwJ6Hy9KdECnYOzr
ZA/oFJV9YEajValqADiyb05nqQQnYw9rCCbOxRE6UaWdJt5DUXZ6SJmUF3SKOJxULXJMhmgl
bu0JquxdFHsYmy1XzAmr0WftV2/xLk73Fo2vzRj04/aV+p9oIXgz6MK8J5qro9MTkYvzyJQ0
5u/UnaAXCV6PuPxJviqNyCv3zZzuJzc/0E5M+VjFAgN/OJM73+mu0GVyLF1Fja1EQC+flO38
UPw1T+615vNrmJ2cLs7M/CQUpyJykBZP4Hy1HbcsXn6BkX5aQQGsasRvIps5fZj7AMGBJqC8
W0Bvl+SsKwR8kV7QG/nxWpy117M4dvFAA32xKgyHqCLKi93je5pS7bs4W0R7vNI6+++dySri
A1IbUxNsC9CNXMCx88VGFlKahfq35ewtCil68X+7X4ukNjUEECtLLrS5n9OPnrugIxRqaxbp
JaV+Wb20kQ1Dv/lBml7EXs4THI0JfWBXIgu5JMaOoppmr+nFOEM/rUZhvc39JlKo03vF0feK
RUD+wmHpx3XLftQbKfKqga+Eu9NxKWqkn1Z1aG6q9JvIRzn9vtBdsyFodrtJ7T00pPKdHtBV
Z3wgdIpJKZ1Adbq6RHiAq+jWzv27N7UIsLRFELk4fVWGUS/jECITxHtBnyAdJNgb5m3zg0yv
pq74CRPo0qMGzxHsC30vv88h7u501Vn3DPgBxGjEFQDUCHQAAAAASUVORK5CYII=</item><item item-id="40">iVBORw0KGgoAAAANSUhEUgAAAMsAAAARCAIAAAD8PM8tAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AjhJREFUaEPtWNuWAjEIc///o1099SByCaHTzlN92nUokJCGOf49n8/H+RwG9jHwUtj5HAb2
MfDYl/pkPgy8N+Rh4TCwlYEfheldnFV9xfhH4ZcTfa/KM1G6hZfJvwnLwrTTqVoHv3KRY/q8
yQVSt6oyE9qnY1Cdx8tAWM7JsACmdBlzMQ9/PFCYbg6o7X4F8KhKfhnHWlKuTFIGmFa78SHS
O5N8FGb24+hAvhx/h5JiLj2Px/iovrK6mdGJudDSs35qboi/MFkJj0tzklHhecuokwwlOQa4
R4RBea7ugSbExlvSTDqkycdgNrWIjTgMCxmnnkqpWB4JtQtg+sEbBWMLD1udsBOyQ6xsjz30
RaD1DE52RFeMtySjMGNyopLpDaUFCsRkLIoUuta3b5XHC9CFnWD7N7eutSUYikKuQlkAeWVz
MdBCa3wDDO2aYZwcrc7PEMqMH3tq+ZQpMTEGbahaK+TwsHbxRPAs/OyxAQOvZVIZRVIKG8oI
rRULuetnDFMjpst4GJ8lAXgZuZBjYFwfwMw8rDySjXIhNN2DfdM3u0+4zu602BLgC7eOTU7n
N4PX/5o2BIXxEj+VLKdXiSj7h7784oXLoWsent4SdYn9HmjSeePHlVIoZUDX0rbGl92WAcw6
ZpIshEmWY8IyqyNvzjesBQ90xjTdqrU7mGmYWRzglYspsRYmX/E2aA0Pu74H17J5JZvfqlk2
cmbhmr7S4cRZHhTzCujfXrru9akygeQcOQzwDPwD1/DF/bTnQa4AAAAASUVORK5CYII=</item><item item-id="41">iVBORw0KGgoAAAANSUhEUgAAAG4AAAAYCAIAAACUQl9wAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="42" content-encoding="gzip">H4sIAAAAAAAA/4xSzW7TQBCe9X+cpG7aQhII3SRtoJaqKHHaprlZVoSEQELqGRS5sWmDaBMc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</item><item item-id="43">iVBORw0KGgoAAAANSUhEUgAAAYAAAAC1CAIAAADZZb9CAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="44">iVBORw0KGgoAAAANSUhEUgAAAFEAAAARCAIAAAAqtdcNAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AQpJREFUWEftltsOwjAIhueb++a1W5Eih5YNki2puzCtQeD7OcxXKWVb7anMqz3basB7X/+Z
b1OgrpSs2Gw7odv+fVakiJ+WTcQD/S11pZ7TIgUzTmSmhWVFbtfO3KI6Jcc+UZ02P9Sb1JsF
YgaqBGd1kfaQFaB/IfA6qBv6sg5UEWSjKkw9oGoyDflnwkrVEo7X2cmsSkhL56GiYNReNtGF
wbGa4mdzWKM/UNEDNrCxmMeEzjonMwdRL7RDVp3NeZ62lmog5acDLPcZI5dXdZKd8KzILOEb
3lVW1zl54mbAvL3JMnvwOQ4M7+cKjMzyDI1x2EC7Du1h88987rFP+kkB7swtg4d/ZjF/AE3h
jz5pFh4pAAAAAElFTkSuQmCC</item><item item-id="45">iVBORw0KGgoAAAANSUhEUgAAAD0AAAARCAIAAADhbjPzAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AOZJREFUSEvlleEOgCAIhO3Ne3PL6YgOREbW5upHc07PDzxwyzmnFb+Te8UvrQhdPPJH7rMu
3gubio6O4DPxg6uKwf0kKr63jmEmzg1aMoAn3FxtzA0ppA1AQMtoXq6Ut1xRAsEMuFUImOQH
c3oesNyiXgW8GT2/qWo3g3IhmZvefufKcPm6uEFdFoesjxg3JLtnHhWgPfC8ywCEzc0dbPgk
YGgVw+oncLzhYKgVtYK5msyO7RzV/bdMhZ0nW9UUKadI699pvxp5YHxVj0PHSTa4kGLzPRFr
bNxu0KEzBbowV+7P/rO4D346XrjLagl3AAAAAElFTkSuQmCC</item><item item-id="46">iVBORw0KGgoAAAANSUhEUgAAAHAAAAARCAIAAACKKr/jAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AUdJREFUWEftl4sOwyAIRd2f989dn5byUBDWtIvNsjSLXuVwQffJOafxBBKYgY4nkEAK1BpS
S7kPCrEETqBzG/FLl17kl7pBISTkbZ9Faoe4gXDGABX8as7NNKeHhFxoYqCQcXMr0oB3AQ0J
uaCoAaWpU9qtA+g2RWkW2k/QRKRG98OOpy7rsJQItMKO3tXQwlagCFA9beeOj9aEfoFqBRxE
3FTweJYHqnRiYMnrc8C6WEpJhd3lAAG5ofaHzoVmYsN/hEORIzTphFhNjmMzRxU6ip0/5dm8
WdX1dqO9vFluffjgLJNCd+yXaxPsOFBR4x1UIOx0pEO7XmUhtiRhJUo9tASFTj/aLvRh0sPj
7NrWVLx0fDcsa7yHQyfwl0l4t0o/avytQNOU5s9es8L7bXSkKvZsQDrBPZriVWfpLCvN+vcv
1v5LzS/bA24YxwPXLAAAAABJRU5ErkJggg==</item><item item-id="47">iVBORw0KGgoAAAANSUhEUgAAAFgAAAAYCAIAAADxr60PAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="48">iVBORw0KGgoAAAANSUhEUgAAADMAAAAXCAIAAAAp/uBdAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AL1JREFUSEvtleEOgCAIhO39H9psOiJEPbSVbvpb4OM49PDeuzlPIJvzuDmxrkluMrMCWzOz
ZEv4LH/U7H0WI0JyJFtkSE9sDOCRYBakkriDNF8j+wy01FuCyzUTZB3aVEoK2dSbP5AhTcpp
in+dpyDb3TFPO5MMXH5hVtC7ygbU9efcYvoqN8hRl7C9zKJ2iYzKvIIF/ejLk6kNIJYvGqkx
7OxxVD3HscjFI1jQNAcLdIe3N6A79WDgJrMLeAI9PACq1s5cgwAAAABJRU5ErkJggg==</item><item item-id="49">iVBORw0KGgoAAAANSUhEUgAAAM4AAAAdCAIAAABt18QSAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="50">iVBORw0KGgoAAAANSUhEUgAAACEAAAARCAIAAADc/FKVAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AItJREFUOE/VlI0OgCAIhOnNfXOiQDDybzrWcq0xa3zenXogIkQPYkQPiAZcPv2bYSnH6SAG
N4/yikV4Bk/pN/2jFFqd7DghPQWVzRtiquepjzGvTJpqzMVOZmYZr041ucIx3tfClzqm8nAh
T2auQmP37mM3CioV4Tfq5eTvM5GAHoW16l0Gk4bvNcwJjudhbwFPHk0AAAAASUVORK5C
YII=</item><item item-id="51">iVBORw0KGgoAAAANSUhEUgAAAB8AAAARCAIAAACqxuAeAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AHNJREFUOE/Vk9EOABAIRfn/jw6zVSIzlU2PNqfbiQwAKa4qPa5SHLpZeUSv8l064RIpez+y
0zlkILrQeb4FXRviorekI0JjzR9Dk9kJ5FooM+7ghn6YnRxgRq/sXO/wCvlTFWYOtyqHM/rd
X3f4QZsGP9MLI+04EHUk4skAAAAASUVORK5CYII=</item><item item-id="52">iVBORw0KGgoAAAANSUhEUgAAAGwAAAAXCAIAAABh4T2YAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="53">iVBORw0KGgoAAAANSUhEUgAAAMsAAAARCAIAAAD8PM8tAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="54">iVBORw0KGgoAAAANSUhEUgAAAG4AAAAYCAIAAACUQl9wAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="55">iVBORw0KGgoAAAANSUhEUgAAAHYAAAARCAIAAACHNM+kAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="56" content-encoding="gzip">H4sIAAAAAAAA/4xSTW/TQBCd9XfcJG7aQhII3SRtoJaqKHHTKrlZVoRAICH1XBS5sWmDaBMc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</item><item item-id="57">iVBORw0KGgoAAAANSUhEUgAAAYAAAAC1CAIAAADZZb9CAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="58">iVBORw0KGgoAAAANSUhEUgAAAEQAAAAYCAIAAADMPcxpAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ATlJREFUWEftVgEOgzAIdD/35123KlI4aFfFqJkxi+ko5e6A8kopTY95MphbPJlwGCdfxxZX
g2chKXHSv38w5wonZKEC51EUmxsoU1UFqxy9fnUwWhYSBCh2bsqMnPYcZXizan5fPc1k80U1
c9fWDFPueDB6JOKDkmijwlhbcgMuDuhgXKuRkjT2+LTpa8FJIVEekAsdxZE1A4eO/sVqMDEm
MZ/6BUyRleS2WGz4WiOwmqm+IjRUimQgZTZlCAz0AktCWOpEt7IFWlq3YT+qKs384bTpdOsq
xtBxnjJOzfFuA+c8UcrNLLVYg3Q0Sdw4EmztEaen1mEyO4v9SD6ME6k7GwBUzL98+Bax3S8q
C+GRrfknFiOMV2Vm1tbYd8SRcT6/d8s85XfJt/o77uAIzwuYAgn+Rpwa5PMNs+hDpc6elswA
AAAASUVORK5CYII=</item><item item-id="59">iVBORw0KGgoAAAANSUhEUgAAAEYAAAAYCAIAAADIyBxUAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AS5JREFUWEftV1sSwyAIbG/em1sbHyGwoLFx1Ez86FjqWJaFBd/OudfNloe00PLBh95SOz4x
J0gNT/A2//pAGkefpEizLMMSAxAUjUU4WNaABKtIM64BiVZ/ZmZtlu4JSaKS1RXVfJyGnf5n
DUPfviRHKzpwURA1J+kZ5jcjre/0cLg96WxRsvbef5RmYwKCOt5FxIveG5LFU0iZ6OyUjSKe
KTbypDL3YbxtnFCO7YnOcGbvSzBhZHtmNSCvlgUAJZjGrp7YmrACSM3hMdJmDEs5lgYkKFNw
0Go2Hp4J/9QSDfBVLDHJll9hn7Gbz7nEk8Jv+KRdTWWGKpuswLa+dCWkmrsmOZNE/LO9NMJ7
Y9tH0sh+Eo+LbkQMHkZcfpNg/IzrQsrkBAzysxibeQ58ARmKzptmqfQqAAAAAElFTkSuQmCC</item><item item-id="60">iVBORw0KGgoAAAANSUhEUgAAALgAAAB3CAIAAAA/7x3xAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="61">iVBORw0KGgoAAAANSUhEUgAAAIYAAAARCAIAAACXeEM9AAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AZJJREFUWEftV1sOwyAM6+5/6K4VXRblYRJgCE30ayqPGDs23es8z2M/SzFwSbKfpRg4lkKz
wdyhtVlYjYFHEp2lBJSGxK1TJvBRetN2yGsrvbC8NKO+rQpfZVb0DlUwgOWaCr2VOUcc5FsD
1KtC4fr1MyW6QStNOvXU0hRHChEVYjmnKPs7KklR2Gx8TyGv6TqJA+brrAh8yYtmKc7K1iuJ
6FBPNlLUw2eaWvtD748tm5L/15KIZvKkbZFEOybCFLc2mE8am3OEojptPFoptb2A4ofybgid
kFnHRNzWJYlgDcsuRokjQhkJwIj2KWekrnfcEBq/OKO+YnmjeLDd6100kZlXKUlMBNhJ4D7D
HxTCIqaTPJeAnSOZWQ3Del0Pgd4aA8Lzq8Flss+bro1WbCBvT5O1KiGRJKzqcU/gMYJtlZJE
W1hkupljIij4N4JYjl0SiTITYaloJpJM/M96kMPmkavYRv57B01XxdE2YX7FNpypVSMlibgy
Ba45dgZWmb/VYEmmqfKX/ngycH4X7IqYgTeBzn6qMLJOegAAAABJRU5ErkJggg==</item><item item-id="62">iVBORw0KGgoAAAANSUhEUgAAAOEAAAAlCAIAAAA7qBmPAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="63" content-encoding="gzip">H4sIAAAAAAAA/4xSy4oTQRS91e9k8phJonFMYnWc+AgMIU+DG2maIIiCMGsl9KR7ZiKZdOzu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</item><item item-id="64">iVBORw0KGgoAAAANSUhEUgAAAaUAAADOCAIAAAAR9IrUAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
DKFJREFUeF7t3duW4yYQBdDk/z964owTx7HdMqIAAbX7aZKlS7ELHSOpL3/++vXrD18ECBDI
IHDLO18ECBDIIPBHhkEaIwECBP6+l6VAgACBJALyLkmjDZMAAes7c4AAgTQC1ndpWm2gBNIL
yLv0UwAAgTQC8i5Nqw2UQHoBeZd+CgAgkEZA3qVptYESSC8g79JPAQAE0gg0zrvbT+CloTNQ
AgQWE2gZT/cfN14MQLkECKQRaBZP96STd2lmjoESWE9gRN5l+DUzxkiAwBiBSMq2ybvHsu7j
+m6SRd8kZUy1CmbyfvEw2dikQd49zw95V/Lh44ra+IoqmQCF25gnzedJm7x7Wce+VDlJ2yYp
w/ru49WuO82v7cJULdlsm+40yLtnL+u7VLOnZLCF22xzRRWOt2QzJs0/A+RdycRrvI153Hwe
N+yQ7mzcncZ5N/Otinm88TyWdw0FNp4n8q7rPPl8cMm78RXVcD6ZJ83nSaK8azgRHYoAgUsE
gp8B8u6SrjkpAQI1AvKuRs0+BAicFnj/6fjhPy8v7053zQ4ECFQKPAfc8LC71SzvKhtnNwIE
agTuMXdF2Mm7mn7ZhwCBkMBFYSfvQl2zMwECpwWs747Jgrfcp/thBwIEOgl4fvcVVt59JbIB
gQUEvJ8taZK8K1GyDQECXwWCYeL7jb8K24AAgVkE5N0snVAHAQK9BeRdb2HHJ0BgFgF5N0sn
1EGAQG8Beddb2PEJEJhFQN7N0gl1ECDQW0De9RZ2fAIEZhGQd7N0Qh0ECPQWkHe9hR2fAIFZ
BOTdLJ1QBwECvQXkXW9hxydAYBYBeTdLJ9RBgEBvAXnXW9jxCRCYRUDezdIJdRAg0FtA3vUW
dnwCBGYRkHezdEIdBAj0Fpgi725F3L8+jjZYYm9BxydAYBWBYJg0+H2fzxV8rCZY4iqdUCcB
Ar0FgmHSIO+eRyjvevfb8QlkFlgj7x43vM//yNw2YydA4EDgY2IcPDQrxLS+K4SyGQEC1wtM
tL7zvuL66aACAlsLzJJ3B3UES9y6fQZHgMAJgWCYtLmffRThfcWJ1tmUAIGTAtfn3cuTxff6
gyWeBLE5AQLbCgTDpM367lg3WOK2rTMwAgROCgTDRN6d9LY5gTwC7z8x9cPPUA0jkXfDqJ2I
QD6B54C7Ouxu+vIu3xQ0YgIjBe4xN0HYybuRbXcuAlkF5gg7eZd1/hk3gWEC1nenqIO33KfO
ZWMCBFoKeH53VlPenRWzPYEpBLyfrWiDvKtAswsBAu8CwTDx/XcmFYGVBeZbgnXVlHddeR2c
wPQCkz1i6+ol77ryOjiBFQRmeoXa1UvedeV1cAKLCEzzLXJdveRdV14HJ7CCgPVdWZe8ryhz
shWBfgLBdw6e3xW3Rt4VU9mQQD+B6swKZmW/EfU5svvZPq6OSmCwQJp70oirvIvo2ZfATAI5
3jlExOVdRM++BFoLVN9gWt8VtELeFSDZhMBIgYqHcRW7jBzRNOeSd9O0QiEEHgKnFmvVS8J8
4PIuX8+NeAkBD+M6tEnedUB1SAI3gciy69T6jnaxgLwrprIhgbMCdY/V6vY6W1vK7eVdyrYb
9DCBsyu1yKpw2KCWPZG8W7Z1Cl9FwJO4aTol76ZphUK6Cly1bjq7vuuKkP7g8i79FMgDMP65
2Pgz5ulm1UjlXRWbnRYVGLnaumpFuWhrhpQ9Rd7dirh/fRxysMQhjE4ySiAeIp6mjerVhOcJ
hkmD3wf1XMHHaoIlToiupJBA5CZx5PouNEg7dxEIhom869IVB/0iUBdbkaDUki0E1si7xw3v
8z+28DeInwWOb13P3pbGb4T1ah2Bj4lx8NCscGTWd4VQNqsS+GlFVre+qyrBTjsJrLG+20nc
WM4JvEeb29Jzgrb+T0DemQ3TCxwH3Nkb2+mHq8B+AvKun23uI7d6XubWNfc8ajv66/PuNh7f
f9e2qbMcLX7jGT/CLBbqmEJgirw7lgiWOAVz2iIiq7NWK8S0+Ab+JhAMkwbvZ782JVji1+Pb
oK+A52t9fR39hEAwTOTdCeuVNm21toqs71byUusaAvJujT5dUGX82Vn8CBcM2yl3FpB3O3c3
OrbI6qzVCjE6BvsT+E9A3pkNhwKevpkgGwnIu9WaOXLdFFnfreaq3gwC8m7BLo95LjbmLAvy
K3ldAXm3Zu96r7xGriLX7ICqVxSQdyt27XfNnqwt2zqFXyUg766Sj5239/ouVp29CcwpIO+m
6Uv5LaQna9M0TSFrCci7mfpVEmTlsTjTyNRCYAYBeTdDF55qcKM6WUOUs5OAvJuvm15EzNcT
Fe0hIO8m66P13WQNUc5OAvJupm6WPL+bqV61EFhLQN5N0y8vIqZphUJ2FUifd1Jm16ltXATe
BNLn3U3EXaQLg0AOAXn3u8/eEuSY7kaZXEDe/TsBfBdI8kvB8BMIyDvruwTT3BAJ/HOhh/7k
TmjnwhYEI/n7WTy/+25kCwI7CATDZP288352h2lsDASKBNLnXZGSjQgQ2EFA3u3QRWMgQKBE
QN6VKNmGAIEdBOTdDl00BgIESgSuz7tbBfevn8oNlliiYBsCBDIIBMMk+n72+fQ/lRIsMUMX
jZEAgRKBYJhE8+65RHlX0jDbECBQLTA07x63rh9vYA/y7mXH4/vfags7EiCwh8DHxIjnhvXd
HtPDKAikEBi6vjsQ9b4ixXQzSAKXCkyRd8dFBEu8lNfJCRCYSCAYJg3uZx8VeF8x0bxQCoEd
BS7Ou+M3GHfwYIk7ds2YCBCoEQiGSYP13deqgyV+Pb4NCBBIIhAME3mXZJ4YJoEdBOTdDl00
BgIESgTkXYmSbQgQ2EFA3u3QRWMgQKBEIEHe+Y3tJRPBNgQSCCTIu1sX/UWeBFPZEAl8FciR
d4/I80dmv84IGxDYVyBN3r2s8vbtqJERIPCTQJq8u6/srO9cCgQSC+TIO8/vEk9xQyfwEEiQ
d97Pmu8ECPwWSJB3Ok2AAAF5Zw4QIJBKwPouVbsNlkBqAXmXuv0GTyCVgLxL1W6DJZBaQN6l
br/BE0glIO9StdtgCaQWkHep22/wBFIJyLtU7TZYAqkF5F3q9hs8gVQC8i5Vuw2WQGoBeZe6
/QZPIJWAvCttd1Cq9DQF26nkHYkJk4JLx+8LKEH6vY0ryhVVMlnMk43nSaK/t20ebzyPS4Ks
cBvzZON5Iu8Kr4KWm7miNr6iGk4U86T5PJF3Dedn6aHM4+bzuJS+YDvd2bg7LfPup4kyyQSa
pAxPEj9mju5snDIFnzKlmwTnSbO8u9Uh7wqbFuxZ4VlKNlOJlEk1T9rk3f2ykXclU8f6zvrO
PCkUaP5pdC7v7ou4x9ejmq9597Kj/yRAgECdQHVW/r3UiOx83/exrJvn5ig+KEcgQGA/gWje
PWecvNtvfhgRgZ0EGuTdTze5OzEZCwECGwhE8+6ZwPpugwlhCAQ2FpB3GzfX0AgQ+J9Ay7xD
S4AAgZkFhubd+zeyDKM5uNe+sKrnt9vDKO4nOh71eJPxZ3x5FHMv4GMXLqzt2mdEE141wV6M
y7sL3+QeT+XBQfN+mY0v4LgX46+xC+fGy0fO+9jHazzmw8G8HTBnJrxq4vNkXN5d+2bj+KN7
wOz56RSXXE5f825wVfF5HOngbBrXXiklZ782iB83KBVN75V3j2XnR5p+l9NP5z0+Y796Xj6r
31fjl5y6JF8GFPaM8/HfFRO6YpfZNEoSp2KYFbtcftUc1Fw3P3vl3TFuXa0VDSu/isaXFPmY
ilB8vYMrRwuWsVDevaC1GvjX41w1LQsn54Xl1Z36gryrK/TrzAgm7CVVzXAVHQx8mEnJCis4
AQpXCjNorLK+u2r2Rs47Ou+GXUIv8/vreb9u0Ol6u+S8hfkyrLbCega0QN6dStthM+RUVUef
bZ3m0MfDPnTGM037JGI8ReH97MjC5s+7kRqtru34pT3hVRPMkHHru1uhz1/xZpQf4XHelwdG
7/+//JhNtrywgNlMLqS4fwDcv6aaIUze79KCGTIu75oEhIMQIECgWkDeVdPZkQCBxQTk3WIN
Uy4BAtUC8q6azo4ECCwmIO8Wa5hyCRCoFpB31XR2JEBgMQF5t1jDlEuAQLWAvKumsyMBAosJ
yLvFGqZcAgSqBeRdNZ0dCRBYTEDeLdYw5RIgUC0g76rp7EiAwGIC8m6xhimXAIFqAXlXTWdH
AgQWE5B3izVMuQQIVAvIu2o6OxIgsJiAvFusYcolQKBaQN5V09mRAIHFBOTdYg1TLgEC1QLy
rprOjgQILCYg7xZrmHIJEKgWkHfVdHYkQGAxAXm3WMOUS4BAtYC8q6azIwECiwn8BVAph7ik
wAhzAAAAAElFTkSuQmCC</item><item item-id="65">iVBORw0KGgoAAAANSUhEUgAAAGMAAAAYCAIAAABhvNTAAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="66">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAYCAIAAAA9NpfkAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ATZJREFUWEftVgEOgzAIdD/3511Na0c5oOjSRZqZZekcRo67A14ppW3tKyOMe2VqxOTpfTki
BGYNXkm+/ftH+FQ2GYGtk9B8S0xUDjunETfi/ZAIkcBGncDtU2U4yGtxDmmrHJ5DqpTPA8mH
S00LUbE/QojrIF0SWWdnwRhJAyiNQv+krM7uNHaBcXwZCmSWEwuEcKb7UNyt/De7/UvZQm2S
KkK6E2hq0eo9eMGZltbfcZQh/pLSPbl9HjPkZKzwWA6WB5pHE5sYqY1yP9quMHWR66t1u3ie
ni7qYhaHrbTGTmTwownJbzlDKaxYfgIPUJg01vUbGj0IDe34y63BthA2WpAfJ2bawKijtGbG
rMget43qRXhJACGCz2mxk6bqOIfAVt17eHHf8qf+9p3jISw4L31HAfkGVqXiGXUN04UAAAAA
SUVORK5CYII=</item><item item-id="67">iVBORw0KGgoAAAANSUhEUgAAAE0AAAAYCAIAAAAwKOejAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AUFJREFUWEftVwEOgzAIdD/3586stSIcULt2humyLAZJx3lwh69lWaY7fFac0T8rTRACjeOM
QMg1kAlCufvgjMCpJFOLxOaToUp6yghKkcA44WRqwcA4qcwUDv+QzxvhlFDlxGaDiSCrVo0a
sAv8U66VdNmkIGoyaQ4Dw+i9YB86/OUm/a5g7tvM0S2MRQ9ay+98xYVkCCbvQGWbtQcw+0pp
Bvj6YrRZ5XhDZmzw0CHsbdYoZvdPt7XcbqEPSy4l8q7q6VuqrLsDzjLELuBKDpnA2Joh+3YU
nxCnFnRFEi6ZzUEqpG2U4q1XUtF2uq0u0Prqg6faysfJTAl2o6N1aN5sq4S6WDrfEAKtkqo9
/ksyTz34Qcmbr8xEeI/XuyUoOYMq63vs5x10ntZv7gpxnXBqOX2rGXdaxpCQNPyOq6zvyW+N
CnAb87tINQAAAABJRU5ErkJggg==</item><item item-id="68">iVBORw0KGgoAAAANSUhEUgAAAFsAAAARCAIAAAA9l0fEAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
ASpJREFUWEftloEOgyAQQ92f++fOBXI52gInc04TyLIRhsA92sbXtm3LbJ7ATmQ2T2CZOIDA
JIKSeAaR3eYy7/xgoXxKRp5Zi44HEEk4UpN9P3hopj1YLHvzHIFqu8VLIiAfhutZI/6kJcYk
ByVNU7ht7Ne0vonW9MzzeVN9q80Dy0UaNebi/flAnF1/ygkRkXN5MFIrBq6tVh4UJZXCgx2L
DngqbntQE4hFXsx1GqmFFhChCO/4qxGE3j5ylzaR4IU37F+z5B80UsRYGQER13QzjqVnO7Zz
Oj8o84zzNZisbF1pZil+c03wXzg5R7VXtFxcpufd30eCNzGQd2zVjDj/rM4+lf73u46tcAGU
Iu8+clqX/WNoZH+smLOe+ikUDJdEpPt9Vm33X+cNyyZv3sJazx8AAAAASUVORK5CYII=</item><item item-id="69">iVBORw0KGgoAAAANSUhEUgAAAQUAAAARCAIAAABbxC7/AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="70">iVBORw0KGgoAAAANSUhEUgAAAEEAAAARCAIAAAANG1blAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AMNJREFUSEvdldEOgCAIRe3P/XPTYUrgFLAHybXmgyDngnCllIL3lRm8r+AdoNTRrxjyozDz
tAdl9mAwbAHb4263YvgdIVQYoBqYeGWAdkoZuIRCUQ15ABOspSoJNXTCMHHHhwe5T8tAHo9Q
KQ4pyoNQGy0DLgOybwIT4YaRKBg+z8OSQatdf9P7OR2KOpdQbrKupWGDl1PNzYkffNg8WF5O
hIk7+djTYiMqKrQ/OfQ+ZEtFxpC/2hPQ3gVAndMAwP9eGG6NHRaLKAp2UAAAAABJRU5ErkJg
gg==</item><item item-id="71">iVBORw0KGgoAAAANSUhEUgAAAJgAAAAYCAIAAACJEKOuAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="72">iVBORw0KGgoAAAANSUhEUgAAAEYAAAAYCAIAAADIyBxUAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AQRJREFUWEftVUkOwyAMTP//aJqICtzxAjjErRCcUESwZ/HwSikdi60T0mLrWAzPZboNCRk4
xzB/KpsYjqBcSQOnShRGMJJCH60Le4/xtOti9LFNcclF+6DylXAHYvgZXiOfKTfnfVPP5gFo
VWRQKAz9iYKI7fKhKkgAoS0mfyO11vn3T0Xg0geJikB7ei45REkfhAT8jToKhOpUqdqkU6Wv
lCSzofkNdKNVhnyouZSnQJ15Ix54MGhR0cwVnhBcun4xxXJV218lbz+A0Q4979JoDdvb7ttU
T06/0bhQfBKmN7BVukfpVsnJX6jx8mM15V2yJtZJxR//Fq1SABUbUgDJt0u8AdiYCOhb4J7c
AAAAAElFTkSuQmCC</item><item item-id="73" content-encoding="gzip">H4sIAAAAAAAA/+xWzS8DQRR/2y9tVagi4UTiHyAhRCOlenAQDeIq2xpS2basleBUd06cBRF3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</item><item item-id="74">iVBORw0KGgoAAAANSUhEUgAAAMQAAAFCCAIAAAAhWuVNAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="75">iVBORw0KGgoAAAANSUhEUgAAAKoAAAARCAIAAADCGaBjAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AeBJREFUaEPtWO1ywjAMY+//0IxddkaV5Y9mNBtr+cUFN5ElS+nxcb/fb9fntAw85L8+p2Xg
dtrOr8a/gv9i4cwMfMvv7z58J0CCOpVY89fIfWBrQhqVnX7Hhp3Kg5ixpsruqODJBf6Qb9ep
LHE0NZgu+yEA2ePQ2MT22FYyg0h2NbsBaT3ILfqLJTXTQs49uIsROoItogLj15mJBrRD13N0
vPzlvokDRrhJNv1PuGLfLR4tTunqabZHm9t0Eng8N7K17DeXX5LQnEhkgEiQLEXGI8LH6dw+
yk8HY3V0/ecBiDcikes5TVb8PBlUOXDUJ7FGqGgCIuUkmwmwkhk5x9J7zUU/8Tj0ehqWud/L
78cca5peicJAUiaN4vMguguiPXMOralOR02lyZnSORG9G5I99DJsozZkSJZACQB5MbJI3/0l
gL78L2cmTwjJZ3NRZpjHr9/8o5mg+yMx64T5pDUjc5frCDW31Evkn2Nmr/xoaNmgjAHKXeF+
CgrMK/TZUMg7z+dMv0xaqgyYjvxGECZKBB5bI0j2OLoiIYFEwseJTDu0eSI5hBqUKpCUnLUl
jycsiIbvICoWH7eZoYNaevdtl0my7CB5oXf/AX13OSfwrxdmAuTeR6ipS/69BP6r+k+Gj8im
lL/BawAAAABJRU5ErkJggg==</item><item item-id="76">iVBORw0KGgoAAAANSUhEUgAAAGcAAAAYCAIAAABoV3S6AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="77">iVBORw0KGgoAAAANSUhEUgAAAGkAAAAYCAIAAAB2nkQJAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="78">iVBORw0KGgoAAAANSUhEUgAAASMAAAARCAIAAAAZh11oAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="79" content-encoding="gzip">H4sIAAAAAAAA/4xSzW7TQBCe9X9+Gidt05AUuiEppElDSCpC4WasUAmpUqWeOIAqN3Ybo7YJ
jhMCpzwBr8CFZ+COuMMjwXh3QsWNtWY98+1+MzufJgsADG2Ilha+irsVvD8NLsPxjQbJGqCl
/PFQYqrAXiXXrocn5++CYSwQOBd8BXdjOpq444WE36CZCJz+vfgWzUmsCPALq7/Yuo2TVTfp
VaCo4q/FURBsijMXbW0iXM1xy98BlqruuF/QcTSDHN0kx7DIMVOOq9gAcwtfx/Rep9fvQVpi
3xCzmNnt9A+fPekDZCRnaWYdF/DcySQ1pb/MiNI5x03/wMiwpePk8jKXj6Yq849QcNz1n3jD
XqeDAhYxj45fvr4IY9hw3M/YxLIAm/L464bgCcmKRM1tERVlSVlE7UGJuEW4Q9wSaqHOFz3x
tDK9Xq9Q6+Vtcip3SYNtFJaxLtyj/lcx7BC3wqnzHUxYpc53RPr7FJVFVCOCVqcatV1y6g+o
2K4QvNt53u3DQyqImM60bufpIUCDUtT3qGYD8zapSkNUaZHatX1Su9UmZfZJtUekxCqGDgGt
W2kei9G0/YOzy8ibjM4uxtG1F8sJrycTLuCBF3tyCvfkD1gy8CpNJBaAkvCTKO0twumRyKOI
u220Y7aaXrF+4wes/f8o7J7M4qswiKqzaXhzWZ3G3o3vRX74KfCrUTAN/Zl3NZUPMOPIGwYH
A0lvimQ8Dwy9LGMFH5upfgjjUXUsUzLWZGDncY7+gRXWVJm9sYIRXZ2orKkxriFN4zprKjo3
8K7BTcSBW4gznkJc4WnEVZ5BXONZxHW+hrjBc4gDt0UeavMPAAAA//8DAJQSfUB1BAAA</item><item item-id="80">iVBORw0KGgoAAAANSUhEUgAAAY8AAAEBCAIAAAAPQr4YAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="81">iVBORw0KGgoAAAANSUhEUgAAAIoAAAAZCAIAAABhFyHeAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
Ab1JREFUaEPtWO2OxCAI3H3/h+7txQuxDAzUuD1q7K/GKh8zDmjfx3G89lMWgQ89+ymLwKts
ZDuw38K2UaiMwCL0SO+ojPVAbCvQ8+FGMu/fB+CotmQFenpMNz3Vdtgpnk3Ppuc+BJYqbotJ
Z6mD9XrcrEOPcLMYSZOL2w3ooAv1w2ygM3jXJs+yigGXqxEeIQFtJj03cNOgn+uIXJvMTw1r
86bVxvErd3EHPXMhCxUw0V3mVotyMSP06PFuzThfmT2pJxSpbI3ertpNbY4Moq5xvmjCWyXu
+rWzGJpOj0KJ3Jov0CNR4kvvDxE0q42ZcxyNVTR4JcnoDLeIB5lHeUY9V+eYOLvqwcgIxKoH
mFmZZIRbHld5aYSmQuawk5FEwl9HCBchLCPZ08G6LD1erUjSo6STTJOTkTdCpM81/bd1sGuR
4mYeS8hRytw+Iaxe8pnqkVTMpeLGyejTJ9LPo3SKTQWKEDSSSfdOOhY7aFAX3K4DeWce4nSM
oT4qRBxRQkw8oLCEZvLV6hnIKizHGZuhmMz0kqsyAVSe88/X0iTKXyprlYmZrJ5QwmNYSP8k
VW7M8iNWzVTPIxJ+VpA/MpxjilE96bwAAAAASUVORK5CYII=</item><item item-id="82">iVBORw0KGgoAAAANSUhEUgAAAJYAAAAZCAIAAABchUC4AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="83">iVBORw0KGgoAAAANSUhEUgAAAYIAAAEFCAIAAABhLXe+AAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="84">iVBORw0KGgoAAAANSUhEUgAAAKEAAAARCAIAAAA6+VuUAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="85">iVBORw0KGgoAAAANSUhEUgAAAkQAAAAFCAMAAACTpQOIAAAAAXNSR0IArs4c6QAAAARnQU1B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</item><item item-id="86">iVBORw0KGgoAAAANSUhEUgAAAeEAAAALCAIAAADHgKIJAAAAAXNSR0IArs4c6QAAAARnQU1B
AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA
AIdJREFUaEPt2MENwCAMBEGn83TuJA9Swz4GUcBpjA7EtbtjESBAgEBT4O1oiwABAgSaAtOM
JRUBAgQIfP8cFAgQIEAgK6Cjs6MRjAABAucdfc/YBAgQIBAR+G8n72gXNQECBLoCOro7G8kI
ECCgo50BAgQIdAV0dHc2khEgQEBHOwMECBDoCjzS9IsyLotV5AAAAABJRU5ErkJggg==</item></binaryContent></worksheet>