Difference between revisions of "JAR v.IS Project Findings"
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<p>We proceed with the creation of our explanatory model by running stepwise regression within the Fit Model platform on JMP Pro 13 on the variables filtered from the steps above with the inclusion of categorical variables (that will be dummy coded by JMP). We conduct a p-value threshold regression at 5% which gives the best R<sup>2</sup> and adjusted R<sup>2</sup> values, indicating the best model fit given the available data. We ran the regression for the forward, backward and mixed directions and realised that the R<sup>2</sup> values for the three different directions are the same. We then select the mixed direction to run our model with. AICC and BICC measures are not used since we are looking at an explanatory model instead of a predictive model.</p> | <p>We proceed with the creation of our explanatory model by running stepwise regression within the Fit Model platform on JMP Pro 13 on the variables filtered from the steps above with the inclusion of categorical variables (that will be dummy coded by JMP). We conduct a p-value threshold regression at 5% which gives the best R<sup>2</sup> and adjusted R<sup>2</sup> values, indicating the best model fit given the available data. We ran the regression for the forward, backward and mixed directions and realised that the R<sup>2</sup> values for the three different directions are the same. We then select the mixed direction to run our model with. AICC and BICC measures are not used since we are looking at an explanatory model instead of a predictive model.</p> | ||
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+ | <p>The regression equation and parameter estimates are shown below:</p> | ||
− | [[File:artregeqn.png| | + | [[File:artregeqn.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">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> | ||
− | [[File:artparam.png| | + | [[File:artparam.png|700px|center]] |
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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"> | + | | 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">Model Fit and Model Assumptions</font></div><br/> |
+ | [[File:artmodfit.png|700px|center]] | ||
+ | {|style="width:100%;vertical-align:top;margin-top:20px;" | ||
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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 Model Fit</div> | ||
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+ | <p> | ||
+ | The goodness of fit is represented by the R2 value. R2 is a statistical measure known as the coefficient of determination which measures how close data points are to the line generated by the model. | ||
+ | The R2 value here for the articles model is 0.18 and represents that the variation in Ln Total Engagement for articles is 18% explained by the model. | ||
− | + | To gauge the explanatory power of each additional explanatory variable added, we also consider the adjusted R2 value, which adjusts for the number of explanatory variables in the model – that is, it would only increase if each explanatory variable added improves the model more than what is expected by chance. | |
− | + | ||
− | + | The adjusted R2 value here for the articles model is 0.17 and represents that the variation in Ln Total Engagement for articles is 17% explained by those explanatory variables that affect the response variable. | |
+ | |||
+ | </p> | ||
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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">Interpretation and Managerial insights</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">Interpretation and Managerial insights</font></div><br/> |
Revision as of 21:43, 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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