Imagine you are a consultant for a catalog sales company. The marketing area is trying to figure out what drives sales of women’s clothing and sales of jewelry. You have available to you several promotional and customer service related variables. Your job is to come up with predictive models of what you think best predict sales in both categories.
1. Predictive models: Build individual regression models for both variables – sales of women’s clothing and sales of jewelry. Please ensure that you include seasonality in your model as appropriate.
2. Compare and contrast the results for both categories and see if there are any differences in terms of what drives sales of both of these categories.
a. Be specific when you state which coefficients are different (state the p-values)
b. Do you have any explanation for why they may be different?
3. Recommendations: If you were making recommendations to a manager, based on your results, where would you suggest they invest money in order to drive sales for each category? Assume that it costs the same amount of money to change each of the variables in your model.
• Build two separate regression models one for women’s clothing, one for jewelry;
• Present the final regression model. Please write out the specific regression equation in the write-up for each model [e.g., Sales of men’s clothing = -4655.889 + 459.56*Phone lines + 136.07*CSR].
• Indicate the R-square.
• Comment, in words, about the different parameter estimates in the model: which ones are positive, which are negative, magnitude of the effects; indicate which coefficients are significant at a 95% or higher confidence level.
• If you conduct any outlier analysis (ran models with and without the outlier), please make sure to include the summary of the results in your write-up.
• Conduct the residual analysis and comment on if the residuals look/do not look normally distributed.
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