<x:xmpmeta tsc:version="2.0.1" xmlns:x="adobe:ns:meta/" xmlns:tsc="http://www.techsmith.com/xmp/tsc/">
   <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:xmp="http://ns.adobe.com/xap/1.0/" xmlns:xmpDM="http://ns.adobe.com/xmp/1.0/DynamicMedia/" xmlns:xmpG="http://ns.adobe.com/xap/1.0/g/" xmlns:xmpMM="http://ns.adobe.com/xap/1.0/mm/" xmlns:tscDM="http://www.techsmith.com/xmp/tscDM/" xmlns:tscIQ="http://www.techsmith.com/xmp/tscIQ/" xmlns:tscHS="http://www.techsmith.com/xmp/tscHS/" xmlns:stDim="http://ns.adobe.com/xap/1.0/sType/Dimensions#" xmlns:stFnt="http://ns.adobe.com/xap/1.0/sType/Font#" xmlns:exif="http://ns.adobe.com/exif/1.0" xmlns:dc="http://purl.org/dc/elements/1.1/">
      <rdf:Description dc:date="2017-09-22 04:15:01 AM" dc:source="Camtasia,9.0.5,enu" dc:title="k_15orm1" tscDM:firstFrame="k_15orm1_First_Frame.png" tscDM:originId="2625EA67-814F-4A0C-B4C7-2902396CFF3B" tscDM:project="k_15orm1.tscproj">
         <xmpDM:duration xmpDM:scale="1/1000" xmpDM:value="1962900"/>
         <xmpDM:videoFrameSize stDim:unit="pixel" stDim:h="810" stDim:w="1080"/>
         <tsc:langName>
            <rdf:Bag>
               <rdf:li xml:lang="en-US">English</rdf:li></rdf:Bag>
         </tsc:langName>
         <xmpDM:Tracks>
            <rdf:Bag>
               <rdf:li>
                  <rdf:Description xmpDM:trackType="ScreenText" xmpDM:frameRate="f1000" xmpDM:trackName="Screen Text">
                     <xmpDM:markers>
                        <rdf:Seq>
                           <rdf:li><rdf:Description xmpDM:name="Other Regression Models Raj Jain Washington University in Saint Louis Saint Louis, MO 63130 Jain@cse.wustl.edu" xmpDM:startTime="0" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="These slides are available on-line at: http://www.cse.wustl.edu/~jain/cse567-17/" xmpDM:startTime="0" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="" xmpDM:startTime="0" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Overview Multiple Linear Regression: More than one predictor variables Categorical Predictors: Predictor variables are categories such as CPU type, disk" xmpDM:startTime="25667" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="type, and so on." xmpDM:startTime="25667" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Curvilinear Regression: Relationship is nonlinear Transformations: Errors are not normally distributed or the variance is not homogeneous Outliers Common mistakes" xmpDM:startTime="25667" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="in regression" xmpDM:startTime="25667" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Multiple Linear Regression Models Given a sample of n observations with k predictors TP_tmp TP_tmp TP_tmp" xmpDM:startTime="50800" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Vector Notation In vector notation, we have: or All elements in the first column of X are 1." xmpDM:startTime="112967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="See Box 15.1" xmpDM:startTime="112967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="for regression formulas." xmpDM:startTime="112967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="112967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Multiple Linear Regression Models Given a sample of n observations with k predictors TP_tmp TP_tmp TP_tmp" xmpDM:startTime="215267" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Vector Notation In vector notation, we have: or All elements in the first column of X are 1." xmpDM:startTime="270967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="See Box 15.1" xmpDM:startTime="270967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="for regression formulas." xmpDM:startTime="270967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="270967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="398100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Seven programs were monitored to observe their resource demands." xmpDM:startTime="398100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="In particular, the number of disk I/O's, memory size (in KBytes), and CPU time (in milliseconds) were observed." xmpDM:startTime="398100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp" xmpDM:startTime="398100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="440133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) In this case: TP_tmp TP_tmp" xmpDM:startTime="440133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="497567" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) The regression parameters are: The regression equation is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="497567" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="561667" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) From the table we see that SSE is: TP_tmp TP_tmp" xmpDM:startTime="561667" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="707100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) An alternate method to compute SSE is to use: For this data, SSY and SS0 are: Therefore, SST and" xmpDM:startTime="707100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="SSR are: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="707100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="752767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) The coefficient of determination R2 is: Thus, the regression explains 97% of the variation of y." xmpDM:startTime="752767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Coefficient of multiple correlation: Standard deviation of errors is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="752767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="793067" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) Standard deviations of the regression parameters are: The 0.95-quantile" xmpDM:startTime="793067" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="for a t-variate with four degrees of freedom is 2.132." xmpDM:startTime="793067" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="None of the three parameters is significant at a 90% confidence level." xmpDM:startTime="793067" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="793067" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="808167" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) The coefficient of determination R2 is: Thus, the regression explains 97% of the variation of y." xmpDM:startTime="808167" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Coefficient of multiple correlation: Standard deviation of errors is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="808167" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="808233" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) An alternate method to compute SSE is to use: For this data, SSY and SS0 are: Therefore, SST and" xmpDM:startTime="808233" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="SSR are: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="808233" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="810567" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) The coefficient of determination R2 is: Thus, the regression explains 97% of the variation of y." xmpDM:startTime="810567" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Coefficient of multiple correlation: Standard deviation of errors is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="810567" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="811467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) Standard deviations of the regression parameters are: The 0.95-quantile" xmpDM:startTime="811467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="for a t-variate with four degrees of freedom is 2.132." xmpDM:startTime="811467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="None of the three parameters is significant at a 90% confidence level." xmpDM:startTime="811467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="811467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="821033" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) A single future observation for programs with 100 disk I/O's and a memory size of 550: Standard deviation of" xmpDM:startTime="821033" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="the predicted observation is: 90% confidence interval using the t value of 2.132" xmpDM:startTime="821033" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="821033" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="822467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) Standard deviations of the regression parameters are: The 0.95-quantile" xmpDM:startTime="822467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="for a t-variate with four degrees of freedom is 2.132." xmpDM:startTime="822467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="None of the three parameters is significant at a 90% confidence level." xmpDM:startTime="822467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="822467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="822900" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) The coefficient of determination R2 is: Thus, the regression explains 97% of the variation of y." xmpDM:startTime="822900" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Coefficient of multiple correlation: Standard deviation of errors is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="822900" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="824267" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) An alternate method to compute SSE is to use: For this data, SSY and SS0 are: Therefore, SST and" xmpDM:startTime="824267" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="SSR are: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="824267" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="825900" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) From the table we see that SSE is: TP_tmp TP_tmp" xmpDM:startTime="825900" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="829500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) The regression parameters are: The regression equation is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="829500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="839700" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) From the table we see that SSE is: TP_tmp TP_tmp" xmpDM:startTime="839700" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="840167" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) An alternate method to compute SSE is to use: For this data, SSY and SS0 are: Therefore, SST and" xmpDM:startTime="840167" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="SSR are: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="840167" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="840733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) The coefficient of determination R2 is: Thus, the regression explains 97% of the variation of y." xmpDM:startTime="840733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Coefficient of multiple correlation: Standard deviation of errors is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="840733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="841333" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) Standard deviations of the regression parameters are: The 0.95-quantile" xmpDM:startTime="841333" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="for a t-variate with four degrees of freedom is 2.132." xmpDM:startTime="841333" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="None of the three parameters is significant at a 90% confidence level." xmpDM:startTime="841333" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="841333" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="934000" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) A single future observation for programs with 100 disk I/O's and a memory size of 550: Standard deviation of" xmpDM:startTime="934000" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="the predicted observation is: 90% confidence interval using the t value of 2.132" xmpDM:startTime="934000" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="is: TP_tmp TP_tmp TP_tmp" xmpDM:startTime="934000" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="976900" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) Standard deviation for a mean of large number of observations is: 90% confidence interval is: TP_tmp TP_tmp" xmpDM:startTime="976900" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Analysis of Variance Test the hypothesis that SSR is less than or equal to SSE." xmpDM:startTime="984467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Degrees of freedom = Number of independent values required to compute Assuming Errors are i.i.d." xmpDM:startTime="984467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Normal Þ y's are also normally distributed, x's are nonstochastic Þ Can be measured without errors Various sums of squares" xmpDM:startTime="984467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="have a chi-square distribution with the degrees of freedom as given above." xmpDM:startTime="984467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="984467" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="F-Test Given SSi and SSj with ni and nj degrees of freedom, the ratio (SSi/ni)/(SSj/nj) has an F distribution with" xmpDM:startTime="1103500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ni numerator degrees of freedom and nj denominator degrees of freedom." xmpDM:startTime="1103500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Hypothesis that the sum SSi is less than or equal to SSj is rejected at a significance level, if the" xmpDM:startTime="1103500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ratio (SSi/ni)/(SSj/nj) is greater than the 1-a quantile of the F-variate." xmpDM:startTime="1103500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="This procedure is also known as F-test." xmpDM:startTime="1103500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="The F-test can be used to check: Is SSR is significantly higher than SSE? Þ Use F-test Þ Compute (SSR/nR)/(SSE/ne)" xmpDM:startTime="1103500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="= MSR/MSE" xmpDM:startTime="1103500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Analysis of Variance Test the hypothesis that SSR is less than or equal to SSE." xmpDM:startTime="1287833" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Degrees of freedom = Number of independent values required to compute Assuming Errors are i.i.d." xmpDM:startTime="1287833" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Normal Þ y's are also normally distributed, x's are nonstochastic Þ Can be measured without errors Various sums of squares" xmpDM:startTime="1287833" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="have a chi-square distribution with the degrees of freedom as given above." xmpDM:startTime="1287833" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="1287833" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="F-Test Given SSi and SSj with ni and nj degrees of freedom, the ratio (SSi/ni)/(SSj/nj) has an F distribution with" xmpDM:startTime="1299300" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ni numerator degrees of freedom and nj denominator degrees of freedom." xmpDM:startTime="1299300" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Hypothesis that the sum SSi is less than or equal to SSj is rejected at a significance level, if the" xmpDM:startTime="1299300" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ratio (SSi/ni)/(SSj/nj) is greater than the 1-a quantile of the F-variate." xmpDM:startTime="1299300" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="This procedure is also known as F-test." xmpDM:startTime="1299300" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="The F-test can be used to check: Is SSR is significantly higher than SSE? Þ Use F-test Þ Compute (SSR/nR)/(SSE/ne)" xmpDM:startTime="1299300" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="= MSR/MSE" xmpDM:startTime="1299300" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="F-Test (Cont) MSE = Variance of Error MSR/MSE has F[k, n-k-1] distribution F-test = Null hypothesis that y doesn't depend" xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="upon any xj: against an alternate hypothesis that y depends upon at least one xj and therefore, at least one" xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="bj ¹ 0." xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="If the computed ratio is less than the value read from the table, the null hypothesis cannot be rejected at" xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="the stated significance level." xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="In simple regression models, If the confidence interval of b1 does not include zero Þ Parameter is nonzero Þ Regression" xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="explains a significant part of the response variation Þ F-test is not required." xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp TP_tmp and" xmpDM:startTime="1299933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ANOVA Table for Multiple Linear Regression Table 15.3" xmpDM:startTime="1361100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp" xmpDM:startTime="1361100" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.2" xmpDM:startTime="1513767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="For the Disk-Memory-CPU data of Example15.1" xmpDM:startTime="1513767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Computed F ratio &gt; F value from the table Þ Regression does explain a significant part of the variation Note:" xmpDM:startTime="1513767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Regression passed the F test Þ Hypothesis of all parameters being zero cannot be accepted." xmpDM:startTime="1513767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="However, none of the regression parameters are significantly different from zero." xmpDM:startTime="1513767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="This contradiction Þ Problem of multicollinearity TP_tmp" xmpDM:startTime="1513767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Problem of Multicollinearity Two lines are said to be collinear if they have the same slope and same intercept." xmpDM:startTime="1730633" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="These two lines can be represented in just one dimension instead of the two dimensions required for lines which are" xmpDM:startTime="1730633" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="not collinear." xmpDM:startTime="1730633" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Two collinear lines are not independent." xmpDM:startTime="1730633" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="When two predictor variables are linearly dependent, they are called collinear." xmpDM:startTime="1730633" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Collinear predictors Þ Problem of multicollinearity Þ Contradictory results from various significance tests." xmpDM:startTime="1730633" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="High Correlation Þ Eliminate one variable and check if significance improves" xmpDM:startTime="1730633" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.3" xmpDM:startTime="1863967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="For the data of Example 15.2," xmpDM:startTime="1863967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="n=7, S x1i=271, S x2i=1324, S x1i2=1385, S x2i2=326,686, S x1ix2i=67,188." xmpDM:startTime="1863967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Correlation is high Þ Programs with large memory sizes have more I/O's In Example14.1," xmpDM:startTime="1863967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="CPU time on number of disk I/O's regression was found significant." xmpDM:startTime="1863967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="1863967" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Problem of Multicollinearity Two lines are said to be collinear if they have the same slope and same intercept." xmpDM:startTime="1877733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="These two lines can be represented in just one dimension instead of the two dimensions required for lines which are" xmpDM:startTime="1877733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="not collinear." xmpDM:startTime="1877733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Two collinear lines are not independent." xmpDM:startTime="1877733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="When two predictor variables are linearly dependent, they are called collinear." xmpDM:startTime="1877733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Collinear predictors Þ Problem of multicollinearity Þ Contradictory results from various significance tests." xmpDM:startTime="1877733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="High Correlation Þ Eliminate one variable and check if significance improves" xmpDM:startTime="1877733" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.2" xmpDM:startTime="1878133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="For the Disk-Memory-CPU data of Example15.1" xmpDM:startTime="1878133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Computed F ratio &gt; F value from the table Þ Regression does explain a significant part of the variation Note:" xmpDM:startTime="1878133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Regression passed the F test Þ Hypothesis of all parameters being zero cannot be accepted." xmpDM:startTime="1878133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="However, none of the regression parameters are significantly different from zero." xmpDM:startTime="1878133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="This contradiction Þ Problem of multicollinearity TP_tmp" xmpDM:startTime="1878133" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ANOVA Table for Multiple Linear Regression Table 15.3" xmpDM:startTime="1878433" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp" xmpDM:startTime="1878433" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.2" xmpDM:startTime="1879533" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="For the Disk-Memory-CPU data of Example15.1" xmpDM:startTime="1879533" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Computed F ratio &gt; F value from the table Þ Regression does explain a significant part of the variation Note:" xmpDM:startTime="1879533" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Regression passed the F test Þ Hypothesis of all parameters being zero cannot be accepted." xmpDM:startTime="1879533" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="However, none of the regression parameters are significantly different from zero." xmpDM:startTime="1879533" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="This contradiction Þ Problem of multicollinearity TP_tmp" xmpDM:startTime="1879533" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Problem of Multicollinearity Two lines are said to be collinear if they have the same slope and same intercept." xmpDM:startTime="1880500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="These two lines can be represented in just one dimension instead of the two dimensions required for lines which are" xmpDM:startTime="1880500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="not collinear." xmpDM:startTime="1880500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Two collinear lines are not independent." xmpDM:startTime="1880500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="When two predictor variables are linearly dependent, they are called collinear." xmpDM:startTime="1880500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Collinear predictors Þ Problem of multicollinearity Þ Contradictory results from various significance tests." xmpDM:startTime="1880500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="High Correlation Þ Eliminate one variable and check if significance improves" xmpDM:startTime="1880500" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.3" xmpDM:startTime="1880933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="For the data of Example 15.2," xmpDM:startTime="1880933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="n=7, S x1i=271, S x2i=1324, S x1i2=1385, S x2i2=326,686, S x1ix2i=67,188." xmpDM:startTime="1880933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Correlation is high Þ Programs with large memory sizes have more I/O's In Example14.1," xmpDM:startTime="1880933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="CPU time on number of disk I/O's regression was found significant." xmpDM:startTime="1880933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="TP_tmp TP_tmp" xmpDM:startTime="1880933" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.3" xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="(Cont) Similarly, in Exercise 14.3," xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="CPU time is regressed on the memory size and the resulting regression parameters are found to be significant." xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Thus, either the number of I/O's or the memory size can be used to estimate CPU time, but not both." xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Lesson: Adding a predictor variable does not always improve a regression." xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="If the variable is correlated to other predictors, it may reduce the statistical accuracy of the regression." xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Try all 2k possible subsets and choose the one that gives the best results with small number of variables." xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Correlation matrix for the subset chosen should be checked" xmpDM:startTime="1918767" xmpDM:duration="1000"/></rdf:li></rdf:Seq>
                     </xmpDM:markers>
                  </rdf:Description>
               </rdf:li>
               <rdf:li>
                  <rdf:Description xmpDM:trackType="TableOfContents" xmpDM:frameRate="f1000" xmpDM:trackName="Table of Contents">
                     <xmpDM:markers>
                        <rdf:Seq>
                           <rdf:li><rdf:Description xmpDM:name="Other Regression Models" xmpDM:startTime="0" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="0" tscDM:imageoffset="0" tscDM:imagerect="0, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Overview" xmpDM:startTime="25667" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="1" tscDM:imageoffset="0" tscDM:imagerect="75, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Multiple Linear Regression Models" xmpDM:startTime="50800" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="2" tscDM:imageoffset="0" tscDM:imagerect="150, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Vector Notation" xmpDM:startTime="112967" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="3" tscDM:imageoffset="0" tscDM:imagerect="225, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Multiple Linear Regression Models" xmpDM:startTime="215267" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="4" tscDM:imageoffset="0" tscDM:imagerect="300, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Vector Notation" xmpDM:startTime="270967" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="5" tscDM:imageoffset="0" tscDM:imagerect="375, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1" xmpDM:startTime="398100" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="6" tscDM:imageoffset="0" tscDM:imagerect="450, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="440133" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="7" tscDM:imageoffset="0" tscDM:imagerect="525, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="497567" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="8" tscDM:imageoffset="0" tscDM:imagerect="600, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name=" Example 15.1 (Cont)" xmpDM:startTime="561667" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="9" tscDM:imageoffset="0" tscDM:imagerect="675, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="707100" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="10" tscDM:imageoffset="0" tscDM:imagerect="750, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="752767" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="11" tscDM:imageoffset="0" tscDM:imagerect="825, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="793067" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="12" tscDM:imageoffset="0" tscDM:imagerect="900, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="808167" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="13" tscDM:imageoffset="0" tscDM:imagerect="975, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="808233" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="14" tscDM:imageoffset="0" tscDM:imagerect="1050, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="810567" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="15" tscDM:imageoffset="0" tscDM:imagerect="1125, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="811467" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="16" tscDM:imageoffset="0" tscDM:imagerect="1200, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="821033" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="17" tscDM:imageoffset="0" tscDM:imagerect="1275, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="822467" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="18" tscDM:imageoffset="0" tscDM:imagerect="1350, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="822900" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="19" tscDM:imageoffset="0" tscDM:imagerect="1425, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="824267" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="20" tscDM:imageoffset="0" tscDM:imagerect="1500, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name=" Example 15.1 (Cont)" xmpDM:startTime="825900" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="21" tscDM:imageoffset="0" tscDM:imagerect="1575, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="829500" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="22" tscDM:imageoffset="0" tscDM:imagerect="1650, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name=" Example 15.1 (Cont)" xmpDM:startTime="839700" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="23" tscDM:imageoffset="0" tscDM:imagerect="1725, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="840167" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="24" tscDM:imageoffset="0" tscDM:imagerect="1800, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="840733" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="25" tscDM:imageoffset="0" tscDM:imagerect="1875, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="841333" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="26" tscDM:imageoffset="0" tscDM:imagerect="1950, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="934000" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="27" tscDM:imageoffset="0" tscDM:imagerect="2025, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.1 (Cont)" xmpDM:startTime="976900" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="28" tscDM:imageoffset="0" tscDM:imagerect="2100, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name=" Analysis of Variance" xmpDM:startTime="984467" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="29" tscDM:imageoffset="0" tscDM:imagerect="2175, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="F-Test " xmpDM:startTime="1103500" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="30" tscDM:imageoffset="0" tscDM:imagerect="2250, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name=" Analysis of Variance" xmpDM:startTime="1287833" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="31" tscDM:imageoffset="0" tscDM:imagerect="2325, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="F-Test " xmpDM:startTime="1299300" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="32" tscDM:imageoffset="0" tscDM:imagerect="2400, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name=" F-Test (Cont)" xmpDM:startTime="1299933" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="33" tscDM:imageoffset="0" tscDM:imagerect="2475, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ANOVA Table  for Multiple Linear Regression" xmpDM:startTime="1361100" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="34" tscDM:imageoffset="0" tscDM:imagerect="2550, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.2" xmpDM:startTime="1513767" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="35" tscDM:imageoffset="0" tscDM:imagerect="2625, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Problem of Multicollinearity" xmpDM:startTime="1730633" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="36" tscDM:imageoffset="0" tscDM:imagerect="2700, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.3" xmpDM:startTime="1863967" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="37" tscDM:imageoffset="0" tscDM:imagerect="2775, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Problem of Multicollinearity" xmpDM:startTime="1877733" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="38" tscDM:imageoffset="0" tscDM:imagerect="2850, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.2" xmpDM:startTime="1878133" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="39" tscDM:imageoffset="0" tscDM:imagerect="2925, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="ANOVA Table  for Multiple Linear Regression" xmpDM:startTime="1878433" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="40" tscDM:imageoffset="0" tscDM:imagerect="3000, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.2" xmpDM:startTime="1879533" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="41" tscDM:imageoffset="0" tscDM:imagerect="3075, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Problem of Multicollinearity" xmpDM:startTime="1880500" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="42" tscDM:imageoffset="0" tscDM:imagerect="3150, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.3" xmpDM:startTime="1880933" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="43" tscDM:imageoffset="0" tscDM:imagerect="3225, 0, 75, 56"/></rdf:li><rdf:li><rdf:Description xmpDM:name="Example 15.3 (Cont)" xmpDM:startTime="1918767" tscDM:image="k_15orm1_Thumbnails.png" tscDM:imageindex="44" tscDM:imageoffset="0" tscDM:imagerect="3300, 0, 75, 56"/></rdf:li></rdf:Seq>
                     </xmpDM:markers>
                  </rdf:Description>
               </rdf:li>
            </rdf:Bag>
         </xmpDM:Tracks>
         <tscDM:controller>
            <rdf:Description xmpDM:name="tscplayer">
               <tscDM:parameters>
                  <rdf:Bag>
                     <rdf:li xmpDM:name="autohide" xmpDM:value="true"/><rdf:li xmpDM:name="autoplay" xmpDM:value="false"/><rdf:li xmpDM:name="loop" xmpDM:value="false"/><rdf:li xmpDM:name="searchable" xmpDM:value="true"/><rdf:li xmpDM:name="captionsenabled" xmpDM:value="false"/><rdf:li xmpDM:name="sidebarenabled" xmpDM:value="false"/><rdf:li xmpDM:name="unicodeenabled" xmpDM:value="false"/><rdf:li xmpDM:name="backgroundcolor" xmpDM:value="000000"/><rdf:li xmpDM:name="sidebarlocation" xmpDM:value="left"/><rdf:li xmpDM:name="endaction" xmpDM:value="stop"/><rdf:li xmpDM:name="endactionparam" xmpDM:value="true"/><rdf:li xmpDM:name="locale" xmpDM:value="en-US"/></rdf:Bag>
               </tscDM:parameters>
               <tscDM:controllerText>
                  <rdf:Bag>
                  </rdf:Bag>
               </tscDM:controllerText>
            </rdf:Description>
         </tscDM:controller>
         <tscDM:contentList>
            <rdf:Description>
               <tscDM:files>
                  <rdf:Seq>
                     <rdf:li xmpDM:name="0" xmpDM:value="k_15orm1.mp4"/><rdf:li xmpDM:name="1" xmpDM:value="k_15orm1_First_Frame.png"/><rdf:li xmpDM:name="2" xmpDM:value="k_15orm1_Thumbnails.png"/></rdf:Seq>
               </tscDM:files>
            </rdf:Description>
         </tscDM:contentList>
      </rdf:Description>
   </rdf:RDF>
</x:xmpmeta>