diff --git a/Business Analytics/README.txt b/Business Analytics/README.txt index e69de29..7742473 100644 --- a/Business Analytics/README.txt +++ b/Business Analytics/README.txt @@ -0,0 +1,21 @@ +For the business analytics file, we have used data about viewership per date and viewership per team to create our predictions. + +Using Date + +On the Date VPG sheet, we have organized it such that the numbers displayed are the average numbers of viewers per game per day. We have +then created graphs, one for each season, which creates a cubic trendline as well as a variance value. + +Using Team + +On the Team VPG sheet, we have organized it such that the numbers displayed are the average numbers of viewers per game per team. We have +then created graphs, one for each season, which creates a cubic trendline as well a variance value. + +Combining Data + +We will collect 3 data points and 3 variance values to predict the viewership for the game. From the two teams that are playing against +each other, we will use the appropriate team trendline from the Team VPG sheet to get an average viewership value from each team. From the +date of the game, we will use the appropriate date trendline from the Date VPG sheet to get an average viewership value for that specific +date. The average viewership values of the teams and the average viewership value of the date will make up the 3 data points, and the 2 +variance values of the teams along with the variance value of the date will make up the variance 3 variance values. Then, to get the +final predicted viewership value, we will multiple each data point by the fraction of the inverse of its corresponding variance value over +the sum of all inversed variance values.