http://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&feed=atom&action=historySOCR EduMaterials AnalysisActivities MLR - Revision history2024-03-28T10:34:48ZRevision history for this page on the wikiMediaWiki 1.15.1http://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=11803&oldid=prevIvoDinov: /* See also */2012-09-24T17:08:13Z<p><span class="autocomment">See also</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==See also==</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==See also==</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>[[SOCR_EduMaterials_AnalysesCommandLineVolumeMultipleRegression|Command-line based multiple linear regression execution]]</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>[[SOCR_EduMaterials_AnalysesCommandLineVolumeMultipleRegression|Command-line based multiple linear regression execution]]<ins class="diffchange diffchange-inline">.</ins></div></td></tr>
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</table>IvoDinovhttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=11802&oldid=prevIvoDinov at 17:07, 24 September 20122012-09-24T17:07:58Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* The design matrix X must have full column rank, otherwise the parameter vector β will not be identified — at most we will be able to narrow down its value to some linear subspace of <math>{R}^p</math>. For this property to hold, we must have n > p, where n is the sample size, and p is the column rank. Methods for fitting.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* The design matrix X must have full column rank, otherwise the parameter vector β will not be identified — at most we will be able to narrow down its value to some linear subspace of <math>{R}^p</math>. For this property to hold, we must have n > p, where n is the sample size, and p is the column rank. Methods for fitting.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* The regressors <math>X_i</math> are assumed to be error-free, that is they are not contaminated with measurement errors. Although not realistic in many settings, dropping this assumption leads to significantly more difficult errors-in-variables models.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* The regressors <math>X_i</math> are assumed to be error-free, that is they are not contaminated with measurement errors. Although not realistic in many settings, dropping this assumption leads to significantly more difficult errors-in-variables models.</div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">==See also==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">[[SOCR_EduMaterials_AnalysesCommandLineVolumeMultipleRegression|Command-line based multiple linear regression execution]]</ins></div></td></tr>
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</table>IvoDinovhttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=9880&oldid=prevIvoDinov: added assumptions section2010-04-01T15:48:57Z<p>added assumptions section</p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>'''Note''': If you happen to click on the "'''Clear'''" button in the middle of the procedure, '''all the data will be cleared out'''. Simply start over from step 1 and click on an '''EXAMPLE''' button for the data you want.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>'''Note''': If you happen to click on the "'''Clear'''" button in the middle of the procedure, '''all the data will be cleared out'''. Simply start over from step 1 and click on an '''EXAMPLE''' button for the data you want.</div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">==Assumptions==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">The SOCR MLR analysis implements the [http://en.wikipedia.org/wiki/General_linear_model General Linear Model (GLM)] and does *not* require normality. Only the [http://en.wikipedia.org/wiki/Linear_regression#Assumptions 2 assumptions] listed are:</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">* The design matrix X must have full column rank, otherwise the parameter vector β will not be identified — at most we will be able to narrow down its value to some linear subspace of <math>{R}^p</math>. For this property to hold, we must have n > p, where n is the sample size, and p is the column rank. Methods for fitting.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">* The regressors <math>X_i</math> are assumed to be error-free, that is they are not contaminated with measurement errors. Although not realistic in many settings, dropping this assumption leads to significantly more difficult errors-in-variables models.</ins></div></td></tr>
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</table>IvoDinovhttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=8818&oldid=prevIvoDinov: /* SOCR Multiple Linear Regression Data Input */2009-01-23T19:39:56Z<p><span class="autocomment">SOCR Multiple Linear Regression Data Input</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==SOCR Multiple Linear Regression Data Input==</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==SOCR Multiple Linear Regression Data Input==</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>Go to SOCR Analyses and select '''Multiple Linear Regression''' from the drop-down list of SOCR analyses, in the left panel. There are three ways to enter data in the SOCR Multiple Linear Regression applet:</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Go to <ins class="diffchange diffchange-inline">[http://www.socr.ucla.edu/htmls/ana/MultipleRegression_Analysis.html </ins>SOCR Analyses and select '''Multiple Linear Regression'''<ins class="diffchange diffchange-inline">] </ins>from the drop-down list of SOCR analyses, in the left panel. There are three ways to enter data in the SOCR Multiple Linear Regression applet:</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Click on the '''Example''' button on the top of the right panel.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Click on the '''Example''' button on the top of the right panel.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Generate random data by clicking on the '''Random Example''' button.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Generate random data by clicking on the '''Random Example''' button.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Paste your own data from a spreadsheet into SOCR Multiple Linear Regression data table.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Paste your own data from a spreadsheet into SOCR Multiple Linear Regression data table.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==SOCR Multiple Linear Regression Example==</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==SOCR Multiple Linear Regression Example==</div></td></tr>
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</table>IvoDinovhttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=4728&oldid=prevAnnie: /* SOCR Multiple Linear Regression Example */2007-08-06T22:20:14Z<p><span class="autocomment">SOCR Multiple Linear Regression Example</span></p>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>'''Note''': If you happen to click on the "'''Clear'''" button in the middle of the procedure, '''all the data will be cleared out'''. Simply start over from step 1.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>'''Note''': If you happen to click on the "'''Clear'''" button in the middle of the procedure, '''all the data will be cleared out'''. Simply start over from step 1 <ins class="diffchange diffchange-inline">and click on an '''EXAMPLE''' button for the data you want</ins>.</div></td></tr>
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</table>Anniehttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=4727&oldid=prevAnnie: /* SOCR Multiple Linear Regression Example */2007-08-06T22:18:23Z<p><span class="autocomment">SOCR Multiple Linear Regression Example</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* As you start the SOCR Analyses Applet, click on "'''Multiple Linear Regression'''" from the combo box in the left panel. Here's what the screen should look like.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* As you start the SOCR Analyses Applet, click on "'''Multiple Linear Regression'''" from the combo box in the left panel. Here's what the screen should look like.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><center>[[Image:SOCR_AnalysisActivities_MLR_Chu_072607_Fig1.gif|800px]]</center></div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><center>[[Image:SOCR_AnalysisActivities_MLR_Chu_072607_Fig1.gif|800px]]</center></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;"></ins></div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* The left part of the panel looks like this (make sure that the "Multiple Linear Regression" is showing in the drop-down list of analyses, otherwise you won't be able to find the correct dataset and will not be able to reproduce the results!)</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* The left part of the panel looks like this (make sure that the "Multiple Linear Regression" is showing in the drop-down list of analyses, otherwise you won't be able to find the correct dataset and will not be able to reproduce the results!)</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><center>[[Image:SOCR_AnalysisActivities_MLR_Chu_051707_Fig4.gif|450px]]</center></div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><center>[[Image:SOCR_AnalysisActivities_MLR_Chu_051707_Fig4.gif|450px]]</center></div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* in the SOCR MLR analysis, there are several SOCR built-in examples. In this activity, we'll be using '''Example 4'''. Click on the "'''Example 4'''" button and next, click on the "'''Data'''" button in the right panel. You should see the data displayed in two columns. There are three columns here, '''dist''', '''climb''' and '''time'''.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* in the SOCR MLR analysis, there are several SOCR built-in examples. In this activity, we'll be using '''Example 4'''. Click on the "'''Example 4'''" button and next, click on the "'''Data'''" button in the right panel. You should see the data displayed in two columns. There are three columns here, '''dist''', '''climb''' and '''time'''.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><center>[[Image:SOCR_AnalysisActivities_MLR_Chu_051707_Fig1.gif|700px]]</center></div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><center>[[Image:SOCR_AnalysisActivities_MLR_Chu_051707_Fig1.gif|700px]]</center></div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Use column '''dist''' and '''climb''' as the regressors (independent variables) and column '''time''' as the response (dependent variable). To tell the computer which variables are assigned to be the regressor and response, we have to do a "Mapping." This is done by clicking on the "'''Mapping'''" button first to get to the Mapping Panel, and then map the variables. For this Multiple Linear Regression activity, there are two places the variables can be mapped to. The top part says '''DEPENDENT''' that you'll need to '''map''' the dependent variable you want here. Just click on '''ADD''' under '''DEPENDENT''' and that will do it. If you change your mind, you can click on '''REMOVE'''. Similar for the '''INDEPENDENT''' variable. Once you get the screen to look like the screenshot below, you're done with the '''Mapping''' step. (Note that, since the columns C4 through C16 do not have data and they are not used, just ignore them.)</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Use column '''dist''' and '''climb''' as the regressors (independent variables) and column '''time''' as the response (dependent variable). To tell the computer which variables are assigned to be the regressor and response, we have to do a "Mapping." This is done by clicking on the "'''Mapping'''" button first to get to the Mapping Panel, and then map the variables. For this Multiple Linear Regression activity, there are two places the variables can be mapped to. The top part says '''DEPENDENT''' that you'll need to '''map''' the dependent variable you want here. Just click on '''ADD''' under '''DEPENDENT''' and that will do it. If you change your mind, you can click on '''REMOVE'''. Similar for the '''INDEPENDENT''' variable. Once you get the screen to look like the screenshot below, you're done with the '''Mapping''' step. (Note that, since the columns C4 through C16 do not have data and they are not used, just ignore them.)</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* After we do the "Mapping" to assign variables, now we use the computer to calculate the regression results -- click on the "'''Calculate'''" button. Then select the "'''Result'''" panel to see the output. For each of the coefficients, '''Estimate''' stands for the estimated parameter value, followed by its '''Standard Error''', '''T-Value''' and '''P-Value'''.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* After we do the "Mapping" to assign variables, now we use the computer to calculate the regression results -- click on the "'''Calculate'''" button. Then select the "'''Result'''" panel to see the output. For each of the coefficients, '''Estimate''' stands for the estimated parameter value, followed by its '''Standard Error''', '''T-Value''' and '''P-Value'''.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>The text in the Result Panel summarizes the results of this simple linear regression analysis. The regression line is displayed. At this point, you can think about how the '''dependent variable''' changes, on average, in response to changes of the '''independent variable'''.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>The text in the Result Panel summarizes the results of this simple linear regression analysis. The regression line is displayed. At this point, you can think about how the '''dependent variable''' changes, on average, in response to changes of the '''independent variable'''.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* If you'd like to see graphical component of this analysis, click on the "'''Graph'''" panel. You'll then see the graph panel that displays scatter plot, as well as diagnostic plots of "residual on fit", "Normal QQ" plots, etc. The plot titles indicate plot types.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* If you'd like to see graphical component of this analysis, click on the "'''Graph'''" panel. You'll then see the graph panel that displays scatter plot, as well as diagnostic plots of "residual on fit", "Normal QQ" plots, etc. The plot titles indicate plot types.</div></td></tr>
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</table>Anniehttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=4593&oldid=prevAnnie: /* SOCR Multiple Linear Regression Example */2007-08-05T04:59:07Z<p><span class="autocomment">SOCR Multiple Linear Regression Example</span></p>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>* After we do the "Mapping" to assign variables, now we use the computer to calculate the regression results -- click on the "'''Calculate'''" button. Then select the "'''Result'''" panel to see the output.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>* After we do the "Mapping" to assign variables, now we use the computer to calculate the regression results -- click on the "'''Calculate'''" button. Then select the "'''Result'''" panel to see the output<ins class="diffchange diffchange-inline">. For each of the coefficients, '''Estimate''' stands for the estimated parameter value, followed by its '''Standard Error''', '''T-Value''' and '''P-Value'''</ins>.</div></td></tr>
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</table>Anniehttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=4581&oldid=prevAnnie: /* SOCR Multiple Linear Regression Example */2007-08-05T04:41:04Z<p><span class="autocomment">SOCR Multiple Linear Regression Example</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==SOCR Multiple Linear Regression Example==</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==SOCR Multiple Linear Regression Example==</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>We will demonstrate Multiple Linear Regression with some SOCR built-in example. This example is based on a dataset from the statistical program "R." For more information of the R program, please see [http://cran.r-project.org/ CRAN Home Page]. The dataset used here is "hills" under R's "MASS" library. The dataset describe the record times in 1984 for 35 Scottish hill races. There are three variables: '''dist''' for distance in miles, '''climb''' total height gained during the route, in feet, and '''time''' record time in minutes.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>We will demonstrate Multiple Linear Regression with some SOCR built-in example. This example is based on a dataset from the statistical program "R." For more information of the R program, please see [http://cran.r-project.org/ CRAN Home Page]. The dataset used here is "hills" under R's "MASS" library. The dataset describe the record times in 1984 for 35 Scottish hill races. There are three variables: '''dist''' for distance in miles, '''climb''' total height gained during the route, in feet, and '''time''' record time in minutes. <ins class="diffchange diffchange-inline">In our example, we will use '''time''' as the dependent variable, and '''climb''' and '''dist''' as the independent variables.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">* As you start the SOCR Analyses Applet, click on "'''Multiple Linear Regression'''" from the combo box in the left panel. Here's what the screen should look like.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">* The left part of the panel looks like this (make sure that the "Multiple Linear Regression" is showing in the drop-down list of analyses, otherwise you won't be able to find the correct dataset and will not be able to reproduce the results!)</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline"><center>[[Image:SOCR_AnalysisActivities_MLR_Chu_051707_Fig4.gif|450px]]</center></ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">* in the SOCR MLR analysis, there are several SOCR built-in examples. In this activity, we'll be using '''Example 4'''. Click on the "'''Example 4'''" button and next, click on the "'''Data'''" button in the right panel. You should see the data displayed in two columns. There are three columns here, '''dist''', '''climb''' and '''time'''.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">* Use column '''dist''' and '''climb''' as the regressors (independent variables) and column '''time''' as the response (dependent variable). To tell the computer which variables are assigned to be the regressor and response, we have to do a "Mapping." This is done by clicking on the "'''Mapping'''" button first to get to the Mapping Panel, and then map the variables. For this Multiple Linear Regression activity, there are two places the variables can be mapped to. The top part says '''DEPENDENT''' that you'll need to '''map''' the dependent variable you want here. Just click on '''ADD''' under '''DEPENDENT''' and that will do it. If you change your mind, you can click on '''REMOVE'''. Similar for the '''INDEPENDENT''' variable. Once you get the screen to look like the screenshot below, you're done with the '''Mapping''' step. (Note that, since the columns C4 through C16 do not have data and they are not used, just ignore them.)</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">* After we do the "Mapping" to assign variables, now we use the computer to calculate the regression results -- click on the "'''Calculate'''" button. Then select the "'''Result'''" panel to see the output.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">The text in the Result Panel summarizes the results of this simple linear regression analysis. The regression line is displayed. At this point, you can think about how the '''dependent variable''' changes, on average, in response to changes of the '''independent variable'''.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">* If you'd like to see graphical component of this analysis, click on the "'''Graph'''" panel. You'll then see the graph panel that displays scatter plot, as well as diagnostic plots of "residual on fit", "Normal QQ" plots, etc. The plot titles indicate plot types.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">'''Note''': If you happen to click on the "'''Clear'''" button in the middle of the procedure, '''all the data will be cleared out'''. Simply start over from step 1.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR}}</ins></div></td></tr>
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</table>Anniehttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=4579&oldid=prevAnnie: /* SOCR Multiple Linear Regression Data Input */2007-08-05T04:34:47Z<p><span class="autocomment">SOCR Multiple Linear Regression Data Input</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Generate random data by clicking on the '''Random Example''' button.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Generate random data by clicking on the '''Random Example''' button.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Paste your own data from a spreadsheet into SOCR Multiple Linear Regression data table.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>* Paste your own data from a spreadsheet into SOCR Multiple Linear Regression data table.</div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">==SOCR Multiple Linear Regression Example==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">We will demonstrate Multiple Linear Regression with some SOCR built-in example. This example is based on a dataset from the statistical program "R." For more information of the R program, please see [http://cran.r-project.org/ CRAN Home Page]. The dataset used here is "hills" under R's "MASS" library. The dataset describe the record times in 1984 for 35 Scottish hill races. There are three variables: '''dist''' for distance in miles, '''climb''' total height gained during the route, in feet, and '''time''' record time in minutes.</ins></div></td></tr>
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</table>Anniehttp://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_AnalysisActivities_MLR&diff=4576&oldid=prevAnnie: /* Multiple Linear Regression Background */2007-08-05T04:25:30Z<p><span class="autocomment">Multiple Linear Regression Background</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>** Diagnostic plots such as the Residual on Fit plot</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>** Diagnostic plots such as the Residual on Fit plot</div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">Go to SOCR Analyses and select '''Multiple Linear Regression''' from the drop-down list of SOCR analyses, in the left panel. There are three ways to enter data in the SOCR Multiple Linear Regression applet:</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">* Click on the '''Example''' button on the top of the right panel.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">* Generate random data by clicking on the '''Random Example''' button.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">* Paste your own data from a spreadsheet into SOCR Multiple Linear Regression data table.</ins></div></td></tr>
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</table>Annie