Difference between revisions of "JAR v.IS Project Findings"
Albertb.2013 (talk | contribs) |
Albertb.2013 (talk | contribs) |
||
Line 216: | Line 216: | ||
* Errors/Residuals are normally distributed and have a population mean of zero | * Errors/Residuals are normally distributed and have a population mean of zero | ||
</p> | </p> | ||
+ | {| 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=3 face="Century Gothic">Assumption 1: Linearity</font></div><br/> | ||
[[File:Assumption_1n3.png|700px|center]] | [[File:Assumption_1n3.png|700px|center]] | ||
Line 223: | Line 226: | ||
<p>The points are quite symmetrically distributed around the line, and this indicates that the points are random and hence fulfills the linearity assumption.</p> | <p>The points are quite symmetrically distributed around the line, and this indicates that the points are random and hence fulfills the linearity assumption.</p> | ||
+ | |||
+ | {| 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=3 face="Century Gothic">Assumption 2: Zero expected mean error</font></div><br/> | ||
[[File:Assumption_2.png|700px|center]] | [[File:Assumption_2.png|700px|center]] | ||
Line 231: | Line 238: | ||
<p>The residuals largely follow a normal distribution with a mean close to zero and a standard deviation close to one.</p> | <p>The residuals largely follow a normal distribution with a mean close to zero and a standard deviation close to one.</p> | ||
− | + | {| style="width:100%; vertical-align:top; margin-top:5px;" | |
− | [[Assumption_1n3.png|700px|center]] | + | |- |
+ | | 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=3 face="Century Gothic">Assumption 3: Homoscedasticity</font></div><br/> | ||
+ | [[File:Assumption_1n3.png|700px|center]] | ||
{|style="width:100%;vertical-align:top;margin-top:20px;" | {|style="width:100%;vertical-align:top;margin-top:20px;" | ||
|- | |- | ||
Line 238: | Line 247: | ||
<p>The distribution of the points in the plot is rather symmetrical, with no signs of increasing residuals with the increase of the predicted values (it is not funnel shaped). This indicates that the residuals have constant variance and are hence homoscedastic</p> | <p>The distribution of the points in the plot is rather symmetrical, with no signs of increasing residuals with the increase of the predicted values (it is not funnel shaped). This indicates that the residuals have constant variance and are hence homoscedastic</p> | ||
+ | |||
+ | {| 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=3 face="Century Gothic">Assumption 4: Independent Residuals</font></div><br/> | ||
[[File:Assumption_4a.png|700px|center]] | [[File:Assumption_4a.png|700px|center]] | ||
Line 247: | Line 260: | ||
</p> | </p> | ||
− | [[File:Assumption_4b.png| | + | [[File:Assumption_4b.png|400px|center]] |
{|style="width:100%;vertical-align:top;margin-top:20px;" | {|style="width:100%;vertical-align:top;margin-top:20px;" | ||
|- | |- | ||
Line 253: | Line 266: | ||
<p>The Durbin-Watson d = 2.15, which is between the two critical values of 1.5 < d < 2.5. Therefore, we can assume that there is no first order linear auto-correlation in our multiple linear regression data</p> | <p>The Durbin-Watson d = 2.15, which is between the two critical values of 1.5 < d < 2.5. Therefore, we can assume that there is no first order linear auto-correlation in our multiple linear regression data</p> | ||
+ | |||
+ | {| 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=3 face="Century Gothic">Assumption 5: Residuals are normally distributed</font></div><br/> | ||
[[File:Assumption_5.png|700px|center]] | [[File:Assumption_5.png|700px|center]] | ||
Line 265: | Line 282: | ||
|- | |- | ||
| 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/> | ||
+ | |||
<p>A multiple stepwise linear regression was run to explain Ln(Total Engagement) for article performance from post message sentiment score, number of links, SQRT(Number of images) and article authors. These variables statistically significantly explained Ln(Total Engagement), F(33.79, 1.06) = 31.96, p < 0.0001***, adjusted R2 = 0.17. All selected variables provided statistically significantly to the explanation, p < .05. The article regression model has met all 5 assumptions highlighted above, and we believe that our sponsor can benefit from the knowledge of the different determinants of their different social media engagement performance based on the regression equation on their article performance.</p> | <p>A multiple stepwise linear regression was run to explain Ln(Total Engagement) for article performance from post message sentiment score, number of links, SQRT(Number of images) and article authors. These variables statistically significantly explained Ln(Total Engagement), F(33.79, 1.06) = 31.96, p < 0.0001***, adjusted R2 = 0.17. All selected variables provided statistically significantly to the explanation, p < .05. The article regression model has met all 5 assumptions highlighted above, and we believe that our sponsor can benefit from the knowledge of the different determinants of their different social media engagement performance based on the regression equation on their article performance.</p> |
Latest revision as of 22:02, 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.
|