Difference between revisions of "Jarvis Video"
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|style="vertical-align:top;width:30%;" | <div style="background: #ffffff; padding: 5px; font-weight: bold; text-align:center; line-height: wrap_content; text-align: center;font-size:30px; font-family:Century Gothic"> <font color= #b1260e>Multiple Linear Regression Model</font></div><br/> | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; padding: 5px; font-weight: bold; text-align:center; line-height: wrap_content; text-align: center;font-size:30px; font-family:Century Gothic"> <font color= #b1260e>Multiple Linear Regression Model</font></div><br/> | ||
− | <p>What makes a good Facebook post? This section outlines the explanatory model on the | + | <p>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.</p> |
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− | |+ ''' | + | |+ '''Video Dataset Metadata for Analysis''' |
! scope="col" | Header | ! scope="col" | Header | ||
! scope="col" | Description | ! scope="col" | Description | ||
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| Post Message Sentiment || Crawled Variable: Sentiment Score calculated using PyCharm python script, AFINN Sentiment words and emoji package | | Post Message Sentiment || Crawled Variable: Sentiment Score calculated using PyCharm python script, AFINN Sentiment words and emoji package | ||
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− | | | + | | Video Duration in Seconds || Derived Variable: The duration in seconds of the video |
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− | | Number of | + | | Number of actors || Derived Variable: Total Number of actors inside a video |
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− | | | + | | Video Category || Crawled Variable: The categories of the video, 7 levels |
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− | | | + | | Day of Week || Derived Variable: The time of the day from the adjusted posted column of the video, categorical 7 levels |
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− | | | + | | Time Interval (Hour) || Derived Variable: The time intervals of the articles derived from recursive splitting of the hour from the time of day column, to coincide with morning, afternoon, evening and night, categorical 4 levels |
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− | | | + | | Video thumbnail includes Words || Derived Categorical Dummy Variable: 1 being video thumbnail includes words and 0 being video thumbnail does not include words |
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− | | | + | | Video thumbnail includes Actor faces|| Derived Categorical Dummy Variable: 1 being video thumbnail includes actor faces and 0 being video thumbnail does not include actor faces |
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− | | | + | | Video thumbnail includes subject matter || Derived Categorical Dummy Variable: 1 being video thumbnail includes subject matter and 0 being video thumbnail does not include subject matter |
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− | | | + | | Actors' names || Derived Variable From JMP Make Indicator Columns from Actor multiresponse column. Actors that are from sponsored companies are marked as external. Actors that act in fewer than 4 videos are marked as others. There are 101 different actors |
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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">Data Transformation / Excluding Outliers</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">Data Transformation / Excluding Outliers</font></div><br/> | ||
− | <p>We | + | <p>We have performed transformation and exclusion of outliers for the video dataset in a similar fashion as the article dataset for both the response variables and the explanatory variables.</p><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=5 face="Century Gothic">Bivariate Fit</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">Bivariate Fit</font></div><br/> | ||
− | <p>We also conduct bivariate analysis on the response variable against each transformed explanatory variable to review the linearity of fit. This step helps us to decide if the transformation of the variable is necessary, and we pick the transformation that provides the highest R<sup>2</sup> value. | + | <p>We also conduct bivariate analysis on the response variable against each transformed explanatory variable to review the linearity of fit. This step helps us to decide if the transformation of the variable is necessary, and we pick the transformation that provides the highest R<sup>2</sup> value. The video dataset only has the three numerical variables below |
</p> | </p> | ||
− | [[File: | + | [[File:vidbivfit.png|700px|center]] |
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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">Bivariate | + | |style="vertical-align:top;width:30%;" | <div style="background: #ffffff; text-align:center; line-height: wrap_content; text-align: center;font-size:12px">Bivariate correlation scatterplot and matrix the video model</div> |
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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/> | ||
− | + | [[File:vidparamest.png|700px|center]] | |
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− | <p>As a result | + | <p>As a result we have the list of numerical continuous explanatory variables to explain the variation of our response variables for the video regression model in preparation for the next step which is the stepwise regression.</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">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]] |
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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]] |
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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> |
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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. | ||
− | |||
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. | ||
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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]] |
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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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</p> | </p> | ||
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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> |
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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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