Difference between revisions of "JAR v.IS Project Findings"

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<p>What makes a good Facebook post? This section outlines the explanatory model on the article dataset from Facebook Insights.</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">Response / Dependent Variables</font></div><br/>
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<p>We choose to make use of “Total Engagement” as the response/ dependent variable. “Total Engagement” for each post is the sum of the total number of reactions (like, love, wow, haha, angry, sad), comments and shares of that post as of the data retrieval date. Reactions are similar to the ‘likes’ on Facebook, but provides the additional option of reacting with five animated emoji rather than a simple ‘like’ reaction.</p><br>
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Other possible response variables include the comment sentiment score measures, and individual engagement metrics but they are ruled out due to reasons such as their non-normal distribution and utility for our sponsor.
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Revision as of 15:03, 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.

Response / Dependent Variables

We choose to make use of “Total Engagement” as the response/ dependent variable. “Total Engagement” for each post is the sum of the total number of reactions (like, love, wow, haha, angry, sad), comments and shares of that post as of the data retrieval date. Reactions are similar to the ‘likes’ on Facebook, but provides the additional option of reacting with five animated emoji rather than a simple ‘like’ reaction.


Other possible response variables include the comment sentiment score measures, and individual engagement metrics but they are ruled out due to reasons such as their non-normal distribution and utility for our sponsor.

Explanatory / Independent Variables

Data Transformation / Excluding Outliers

Bivariate Fit

multi-collinearity

Stepwise Regression

Evaluation of Model Fit

Model Assumptions

Interpretation and Managerial insights