Colonial Broadcasting
Colonial Broadcasting Company
Executive Summary
Colonial Broadcasting Co (CBC), a major American television network, must determine which of the different factors plays a key role in optimizing the ratings of its movie. The following report contains statistical analysis on the different relationships between the factors influencing ratings.
The Regression Model
For a detailed description of the variables and the defined statistical terms used in this report, see [ Annex 1 ]. Based on the sample data provided and the statistical analysis, the following regression equation has been derived:
Ratings = 13.729 - 1.540*BBS + 1.281*Winter + 1.164*Sunday +1.593*Monday + 1.854*Fact + 0.910*(SQRT)Stars + 8.413*Log (Previous Rating) - 10.206 *Log (Competition)
This equation accounts for 44.3% of the observed variation in ratings, with a standard error of 1.897 (see [ Annex 3 ] for full details). Assumptions for this model can also be found on the same Annex.
Methodology
Set up the model, choose the data
The sample size of 88 observations is greater than 30 and therefore sufficient to be considered representative of the entire population. Ratings was chosen as the most appropriate dependent variable since the success of a network relies on how many people watch their particular program/movie. An initial multiple regression was then run with all the remaining non-transformed variables against ratings. This resulted in an adjusted R2 value of 36%, meaning the regression equation accounts for 36% of the observed variation in ratings. The standard error was 2.04, and the t-stats showed that every explanatory variable was statistically relevant except ABN, Month and Day (see [ Annex 7 ]). Intuitively, some data points possibly could have a non-linear relationship and different tests were performed to see what kind of relationships existed. It was concluded that several did exist and an explanation will be shown later why these were so.
Look at the scatter plots
Executive Summary
Colonial Broadcasting Co (CBC), a major American television network, must determine which of the different factors plays a key role in optimizing the ratings of its movie. The following report contains statistical analysis on the different relationships between the factors influencing ratings.
The Regression Model
For a detailed description of the variables and the defined statistical terms used in this report, see [ Annex 1 ]. Based on the sample data provided and the statistical analysis, the following regression equation has been derived:
Ratings = 13.729 - 1.540*BBS + 1.281*Winter + 1.164*Sunday +1.593*Monday + 1.854*Fact + 0.910*(SQRT)Stars + 8.413*Log (Previous Rating) - 10.206 *Log (Competition)
This equation accounts for 44.3% of the observed variation in ratings, with a standard error of 1.897 (see [ Annex 3 ] for full details). Assumptions for this model can also be found on the same Annex.
Methodology
Set up the model, choose the data
The sample size of 88 observations is greater than 30 and therefore sufficient to be considered representative of the entire population. Ratings was chosen as the most appropriate dependent variable since the success of a network relies on how many people watch their particular program/movie. An initial multiple regression was then run with all the remaining non-transformed variables against ratings. This resulted in an adjusted R2 value of 36%, meaning the regression equation accounts for 36% of the observed variation in ratings. The standard error was 2.04, and the t-stats showed that every explanatory variable was statistically relevant except ABN, Month and Day (see [ Annex 7 ]). Intuitively, some data points possibly could have a non-linear relationship and different tests were performed to see what kind of relationships existed. It was concluded that several did exist and an explanation will be shown later why these were so.
Look at the scatter plots