Final Assignment:Red Bull Predictive ModelingScenario: You are an analyst at Red Bull. Your goal is to use predictive modeling to showwhich online teams’ channel should get a $100,000 bonus. You have...

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Answered 3 days AfterAug 05, 2021

Answer To: Final Assignment:Red Bull Predictive ModelingScenario: You are an analyst at Red Bull. Your goal is...

Atreye answered on Aug 09 2021
162 Votes
1. Output of simple linear regression for each subcategory:
The predicted sales amount for spending $100 is tabulated below:
    Banner
    $144158
    Facebook
    $162021
    Instagram
    $145320
    e-zine
    $139367
    TV ads
    $141999
    Twitter
    $198160
    Youtube
    $200013
Conclusion:
From the above chart, it is evident that sales for YouTube is the highest among all other categories. This implies that YouTube’s team should w
in the bonus.
2. Plot of simple linear regression for each subcategory:
Output of simple linear regression for log transformed sales:
Plot of simple linear regression of log-sales for each subcategory:
    Banner
    11.1235
    Facebook
    11.1482
    Instagram
    11.1224
    e-zine
    10.8341
    TV ads
    10.8558
    Twitter
    11.3529
    YouTube
    11.2692
The predicted log-sales amount for spending $100 is tabulated below:
Conclusion:
From the above chart, it is evident that log sales for Twitter is the highest among all other categories. This implies that Twitter’s team should win the bonus.
3. Multiple regression model:
The predicted sales amount for spending $100 using multiple regression model is tabulated below:
    twitter
    38656.39
    banner
    42416.32
    Facebook
    45940.5
    Instagram
    46006.01
    YouTube
    38930.96
    TV
    37454.87
    E-zine
    40779.06
Conclusion:
From the above chart, it is evident that sales for Instagram is the highest among all other categories. This implies that Instagram’s team should win the bonus.
4. Remove variables from the Multiple Regression Red Bull model
1. If we remove the twitter and YouTube category from multiple regression model, then it will increase adjusted R-square value from 0.655212357 to 0.66165928.
2. they are both insignificant as the corresponding p-values are 0.961728885 and 0.766094073 which are not significant.
3. From the below graphs it is evident that they are not associated with the response variable sales.
4. From the correlation matrix, it is seen that there is very high correlation between TV and E-zine. So, TV will be removed from the existing model to avoid redundancy.
    
    twitter
    banner
    Facebook
    Instagram
    YouTube
    TV
    E-zine
    twitter
    1
    
    
    
    
    
    
    banner
    -0.0441
    1
    
    
    
    
    
    Facebook
    -0.0436
    0.13665
    1
    
    
    
    
    Instagram
    -0.0469
    0.49549
    0.21514
    1
    
    
    
    YouTube
    -0.0397
    0.12088
    -0.1539
    -0.0735
    1
    
    
    TV
    -0.0767
    -0.0472
    0.30747
    -0.0798
    0.01185
    1
    
    E-zine
    -0.0775
    -0.0698
    0.29394
    -0.082
    -0.0381
    0.97185
    1
5. It does make sense, that banner, Facebook, Instagram and E-zine will contribute to the model.
6. Finally, Twitter, YouTube and TV will be removed from the multiple linear regression model.
The final multiple regression model output is as below:
The predicted sales amount for spending $100 using final multiple regression model is tabulated below:
    banner
    40972.46
    Facebook
    44076.94
    Instagram
    44744.51
    E-zine
    38240.38
Conclusion:
From the above chart, it is evident that sales for Instagram is the highest among all other categories. This implies that Instagram’s team should win the bonus.
References
George A. Morgan, R. J. (June01,2003). Use and interpretation of multiple regression. JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATARY, Volume 42, Isusue 6, P738-740.
Khushbu Kumari, S. Y. (2018). Linear regression analysis study. Journal of the PRACTICE OF CARDIOVASCULAR SCIENCES, Volume 4, Issue 1, P33-P36.
SUMMARY OUTPUT
Regression Statistics
Multiple R0.072998143
R Square0.005328729
Adjusted R Square-0.004054962
Standard Error228542.8304
Observations108
ANOVA
dfSSMSFSignificance F
Regression129660953961296609539610.5678712890.452776649
Residual1065.53657E+1252231825309
Total1075.56623E+12
CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept198389.495322143.356498.9593235481.22913E-14154488.1373242290.8532154488.1373242290.9
twitter-2.2935068943.04351253-0.7535723510.452776649-8.3275665033.740552714-8.3275665033.740553
SUMMARY OUTPUT
Regression Statistics
Multiple R0.045806221
R Square0.00209821
Adjusted R Square-0.007315958
Standard Error228913.6631
Observations108
ANOVA
dfSSMSFSignificance...
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