Difference between revisions of "JAR v.IS Project Findings"
Albertb.2013 (talk | contribs) |
Albertb.2013 (talk | contribs) |
||
Line 199: | Line 199: | ||
<p> | <p> | ||
− | The goodness of fit is represented by the | + | 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 | + | The R<sup>2</sup> 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. |
+ | <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. | ||
− | + | 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. | |
− | + | </p><br> | |
+ | <p>We then move on to the model assumptions to validate our regression model findings. There are several assumptions of linear regression models which need to be met, as seen below: | ||
+ | * Relationship between the dependent variable and independent variables is linear | ||
+ | * Expected mean error of the regression model is zero | ||
+ | * Errors/Residuals have constant variance (Homoscedastic) | ||
+ | * Errors/Residuals are independent of each other | ||
+ | * Errors/Residuals are normally distributed and have a population mean of zero | ||
</p> | </p> | ||
+ | |||
+ | |||
{| style="width:100%; vertical-align:top; margin-top:5px;" | {| style="width:100%; vertical-align:top; margin-top:5px;" | ||
|- | |- | ||
| 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 21:47, 23 April 2017
Click here to return to AY16/17 T2 Group List
Articles | Videos | R |
---|
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.
|