Difference between revisions of "JAR v.IS Project Findings"
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[[File:Article_transformation.png|500px|center|frame|Transforming the Response Variables and removing the outliers]] | [[File:Article_transformation.png|500px|center|frame|Transforming the Response Variables and removing the outliers]] | ||
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[[File:outlier.png|700px|center|frame|Transforming the Explanatory Variables and removing the outliers]] | [[File:outlier.png|700px|center|frame|Transforming the Explanatory Variables and removing the outliers]] | ||
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[[File:Bivfit.png|500px|center|frame|Bivariate fit of difficult words count. we select the SQRT transformation instead of the Ln transformation]] | [[File:Bivfit.png|500px|center|frame|Bivariate fit of difficult words count. we select the SQRT transformation instead of the Ln transformation]] | ||
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[[File:Bivfitscattermatrix.png|700px|center|frame|Bivariate correlation scatterplot matrix for all 18 numerical variables for the article model]] | [[File:Bivfitscattermatrix.png|700px|center|frame|Bivariate correlation scatterplot matrix for all 18 numerical variables for the article model]] | ||
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<p>Using this scatterplot together with the bivariate correlation matrix, we eliminated 8 variables that are highly correlated. We ran Standard Least Squares regression on continuous numerical variables to verify the absence of multicollinearity in our remaining variables.</p> | <p>Using this scatterplot together with the bivariate correlation matrix, we eliminated 8 variables that are highly correlated. We ran Standard Least Squares regression on continuous numerical variables to verify the absence of multicollinearity in our remaining variables.</p> | ||
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<p>As a result, we have the narrowed down version of our final list of numerical continuous explanatory variables to explain the variation of our response variables for the article regression model in preparation for the next step which is the stepwise regression.</p> | <p>As a result, we have the narrowed down version of our final list of numerical continuous explanatory variables to explain the variation of our response variables for the article regression model in preparation for the next step which is the stepwise regression.</p> |
Revision as of 21:28, 23 April 2017
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Multiple Linear Regression Model What makes a good Facebook post? This section outlines the explanatory model on the article dataset from Facebook Insights supplemented with our crawled variables to form a holistic complete article dataset.
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