Difference between revisions of "JAR v.IS Project Findings"
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<p>We perform the transformation on the variables to make them more suitable for regression analysis. We perform a square root transformation as well as a natural logarithm transformation on all response and explanatory variables whose distributions are not normal to reduce skewness and yield a more normal distribution.</p><br> | <p>We perform the transformation on the variables to make them more suitable for regression analysis. We perform a square root transformation as well as a natural logarithm transformation on all response and explanatory variables whose distributions are not normal to reduce skewness and yield a more normal distribution.</p><br> | ||
− | [[File:Article_transformation.png| | + | [[File:Article_transformation.png|500px|center|frame|Transforming the Response Variables and removing the outliers]] |
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− | [[File:Bivfit.png| | + | [[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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<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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|style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Article Regression equation for Ln(Total engagement)</div> | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Article Regression equation for Ln(Total engagement)</div> | ||
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Revision as of 21:25, 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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