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"> Checking for Multi-collinearity</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"> Checking for Multi-collinearity</font></div><br/> | ||
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[[File:vidparamest.png|700px|center]] | [[File:vidparamest.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">Stepwise Regression</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">Stepwise Regression</font></div><br/> | ||
− | <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 | + | <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 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 mixed direction is the highest, and we will be using it 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> |
<br> | <br> | ||
<p>The regression equation and parameter estimates are shown below:</p> | <p>The regression equation and parameter estimates are shown below:</p> | ||
− | [[File: | + | [[File:videqn.png|700px|center]] |
{|style="width:100%;vertical-align:top;margin-top:20px;" | {|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"> | + | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Video Regression equation for Ln(Total engagement)</div> |
− | [[File: | + | [[File:vidparam.png|700px|center]] |
{|style="width:100%;vertical-align:top;margin-top:20px;" | {|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"> | + | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Videp Regression Parameter Estimates for Ln(Total engagement)</div> |
{| style="width:100%; vertical-align:top; margin-top:5px;" | {| style="width:100%; vertical-align:top; margin-top:5px;" | ||
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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">Model Fit and Model Assumptions</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">Model Fit and Model Assumptions</font></div><br/> | ||
− | [[File: | + | [[File:vidparam.png|700px|center]] |
{|style="width:100%;vertical-align:top;margin-top:20px;" | {|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"> | + | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Video Regression model fit results</div> |
<p> | <p> | ||
The goodness of fit is represented by the R<sup>2</sup> value. R<sup>2</sup> 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 goodness of fit is represented by the R<sup>2</sup> value. R<sup>2</sup> 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 R<sup>2</sup> value here for the articles model is 0. | + | The R<sup>2</sup> value here for the articles model is 0.20 and represents that the variation in Ln Total Engagement for articles is 20% explained by the model. |
<br><br> | <br><br> | ||
To gauge the explanatory power of each additional explanatory variable added, we also consider the adjusted R<sup>2</sup> 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. | To gauge the explanatory power of each additional explanatory variable added, we also consider the adjusted R<sup>2</sup> 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. | ||
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The adjusted R<sup>2</sup> 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. | The adjusted R<sup>2</sup> 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. | ||
Revision as of 22:25, 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 video dataset from Facebook Insights supplemented with our crawled variables to form a holistic complete video dataset.
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