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=3 face="Century Gothic">Assumption 1: Linearity</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=3 face="Century Gothic">Assumption 1: Linearity</font></div><br/> | ||
− | [[File: | + | [[File:Assumption_1n3v.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: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/> | | 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: | + | [[File:Assumption_2v.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=3 face="Century Gothic">Assumption 3: Homoscedasticity</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=3 face="Century Gothic">Assumption 3: Homoscedasticity</font></div><br/> | ||
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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=3 face="Century Gothic">Assumption 4: Independent Residuals</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=3 face="Century Gothic">Assumption 4: Independent Residuals</font></div><br/> | ||
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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">Durbin-Watson test of no autocorrelation</div> | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Durbin-Watson test of no autocorrelation</div> | ||
− | <p>The Durbin-Watson d = | + | <p>The Durbin-Watson d = 1.6, 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="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=3 face="Century Gothic">Assumption 5: Residuals are normally distributed</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=3 face="Century Gothic">Assumption 5: Residuals are normally distributed</font></div><br/> | ||
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− | <p>A multiple stepwise linear regression was run to explain Ln(Total Engagement) for | + | <p>A multiple stepwise linear regression was run to explain Ln(Total Engagement) for video performance from post message sentiment score, Ln(duration of video in seconds), video category, hourly time interval to post and video actors. These variables statistically significantly explained Ln(Total Engagement), F(12.63, 1.47) = 8.57, p < 0.0001***, adjusted R<sup>2</sup> = 0.17. All selected variables added statistically significantly to the explanation, p < .05. The video regression model has met all 5 assumptions highlighted above, and we believe that our sponsor can benefit from the knowledge of the determinants of their different social media engagement based on the regression equation on their video performance.</p> |
<br><br> | <br><br> | ||
<p> | <p> | ||
− | While our | + | While our video explanatory regression models can explain up to 17-18% of the variation in the post’s engagement performance, insights can still be gleaned from it. Below are the points that can be drawn for the video regression model: |
<br><br> | <br><br> | ||
− | * | + | * Positive sounding post messages when added to the description of the video can help increase engagement. |
− | * | + | * Video duration matters and longer videos tend to perform based on our results. However, we believe that there is an ideal video length as overtly lengthy videos could deter engagement. |
− | + | * A, B, C, D, and E videos since they are significantly more popular and should place more emphasis in its content creation. | |
− | * | + | * Best time to post is in the late afternoons, evenings, and nights between 4pm to 11pm |
+ | * Actors A, B and C are performing well and can be suited for such videos whereas actors D, E, F, G, H and I do not perform that well, suggesting the need for either improvement or adjustment of assignments. | ||
</p> | </p> |
Latest revision as of 22:39, 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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