Difference between revisions of "Jarvis Video"
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| style="vertical-align:top;width:20%;" | <div style="none: solid; border-width:2px; background: #FFFFFF; padding: 10px; font-weight:bold; text-align:center; line-height: wrap_content; text-indent: 20px; font-size:18px"><font color="#b1260e" size=5 face="Century Gothic">Data Transformation / Excluding Outliers</font></div><br/> | | style="vertical-align:top;width:20%;" | <div style="none: solid; border-width:2px; background: #FFFFFF; padding: 10px; font-weight:bold; text-align:center; line-height: wrap_content; text-indent: 20px; font-size:18px"><font color="#b1260e" size=5 face="Century Gothic">Data Transformation / Excluding Outliers</font></div><br/> | ||
− | <p>We | + | <p>We have performed transformation and exclusion of outliers for the video dataset in a similar fashion as the article dataset for both the response variables and the explanatory variables.</p><br> |
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| style="vertical-align:top;width:20%;" | <div style="none: solid; border-width:2px; background: #FFFFFF; padding: 10px; font-weight:bold; text-align:center; line-height: wrap_content; text-indent: 20px; font-size:18px"><font color="#b1260e" size=5 face="Century Gothic">Bivariate Fit</font></div><br/> | | style="vertical-align:top;width:20%;" | <div style="none: solid; border-width:2px; background: #FFFFFF; padding: 10px; font-weight:bold; text-align:center; line-height: wrap_content; text-indent: 20px; font-size:18px"><font color="#b1260e" size=5 face="Century Gothic">Bivariate Fit</font></div><br/> | ||
− | <p>We also conduct bivariate analysis on the response variable against each transformed explanatory variable to review the linearity of fit. This step helps us to decide if the transformation of the variable is necessary, and we pick the transformation that provides the highest R<sup>2</sup> value. | + | <p>We also conduct bivariate analysis on the response variable against each transformed explanatory variable to review the linearity of fit. This step helps us to decide if the transformation of the variable is necessary, and we pick the transformation that provides the highest R<sup>2</sup> value. The video dataset only has the three numerical variables below |
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− | [[File: | + | [[File:vidbivfit.png|700px|center]] |
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− | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Bivariate | + | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Bivariate correlation scatterplot and matrix the video model</div> |
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<p>We also ran bivariate fit against all the 18 numerical explanatory variables to test for multicollinearity. The figure below shows the bivariate correlation scatterplot.</p> | <p>We also ran bivariate fit against all the 18 numerical explanatory variables to test for multicollinearity. The figure below shows the bivariate correlation scatterplot.</p> | ||
− | [[File: | + | [[File:vidparamest.png|700px|center]] |
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− | <p>As a result | + | <p>As a result we have the list of numerical continuous explanatory variables to explain the variation of our response variables for the video regression model in preparation for the next step which is the stepwise regression.</p> |
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Revision as of 22:18, 23 April 2017
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Articles | Videos | R |
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Multiple Linear Regression Model What makes a good Facebook post? This section outlines the explanatory model on the video dataset from Facebook Insights supplemented with our crawled variables to form a holistic complete video dataset.
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