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You can see the problem with using </inlineAttr></c></f><f family="Verdana" charset="0" size="10"><b><inlineAttr line-through="false"><c val="#000">linterp</c></inlineAttr></b></f><f family="Verdana" charset="0" size="10"><inlineAttr line-through="false"><c val="#000"> to predict values beyond the data range. The interpolation assumes that the last approximation function, the one calculated between the last two points, will continue to be true for all other points on that end of the range. This assumption is seldom appropriate. This difficulty is true for any interpolation method. Prediction of values outside of a data range is fodder for different methods. </c></inlineAttr></f></p></text></region><region region-id="920" left="36" top="788.25" width="300.75" height="60" align-x="36" align-y="798" show-border="false" show-highlight="false" is-protected="true" z-order="0" 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"><f family="Verdana" charset="0" size="10"><inlineAttr line-through="false"><c val="#000">Moreover, the curve that you get with linear interpolation is not smooth at each connecting data point, so there are no defined derivatives of the interpolated curve. 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