The profit prediction model, developed for the top four brands based on quantity sold, will accurately describe the relationship between significant variables (such as Customers' Ratings and/or Customers' Age) and profit, providing valuable insights into the key factors influencing profitability for these specific brands.
To create a profit prediction model considering Customers' Ratings and Customers' Age for the top four brands based on quantity sold, the initial step is to filter the transaction data for these specific brands. Let's assume the top four brands are A, B, C, and D based on the quantity sold.
Filter Transaction Data:
Extract data entries corresponding to transactions involving brands A, B, C, and D. This filtering ensures that the analysis focuses exclusively on the top four brands based on quantity sold.
Regression Analysis:
Perform a regression analysis using Customers' Ratings and Customers' Age as independent variables and profit as the dependent variable. Utilize statistical software to assess the significance of these variables.
Variable Selection:
Retain only the significant variables in the final model. If both Customers' Ratings and Customers' Age are statistically significant, they will be included. If either or both of them are not significant, they may be excluded from the final model.
Model Description:
The final model, based on the regression analysis output, will accurately describe the relationship between the significant variables (Customers' Ratings and/or Customers' Age) and profit for the top four brands. The model will provide insights into how these customer-related factors contribute to profit variations for the selected brands.
In conclusion, the profit prediction model will be tailored to the top four brands, considering only the significant variables, which may include Customers' Ratings and/or Customers' Age.