Most Appropriate Regression Model to Predict Sales - Management Assignment Help

Assignment Task

Task
Can demographic information be helpful in predicting sales at sporting goods stores? The file ‘Sporting.xls’ contains the monthly sales totals from a random sample of 38 stores in a large chain of nationwide sporting goods stores. All stores in the franchise, and thus within the sample, are approximately the same size and carry the same merchandise. The county or, in some cases, counties in which the store draws the majority of its customers is referred to here as the customer base. For each of the 38 stores, demographic information about the customer base is provided. The data are real, but the name of the franchise is not used, at the request of the company. The data set contains the following variables:
Sales—Latest one-month sales total (dollars)Age—Median age of customer base (years)HS—Percentage of customer base with a high school diplomaCollege—Percentage of customer base with a college diplomaGrowth—Annual population growth rate of customer base over the past 10 yearsIncome—Median family income of customer base (dollars)

Describe your data using descriptive statistics for continuous (or quantitative) variables (e.g. mean, standard deviation, minimum and maximum values) and frequencies for discrete (or qualitative) variables (i.e. % of observations taking each distinct value for that variable).

Develop the most appropriate multiple regression model to predict a store’s monthly sales. In addition, provide a detailed explanation of the results, including a comparison of the most appropriate multiple regression model to the best simple linear regression model.

The human resources (HR) director for a large company that produces highly technical industrial instrumentation devices has the business objective of improving recruiting decisions concerning sales managers. The company has 45 sales regions, each headed by a sales manager. Many of the sales managers have degrees in electrical engineering and, due to the technical nature of the product line, several company officials believe that only applicants with degrees in electrical engineering should be considered. At the time of their application, candidates are asked to take the Strong-Campbell Interest Inventory Test and the Wonderlic Personnel Test. Due to the time and money involved with the testing, some discussion has taken place about dropping one or both of the tests. To start, the HR director gathered information on each of the 45 current sales managers, including years of selling experience, electrical engineering background, and the scores from both the Wonderlic and Strong-Campbell tests. The HR director has decided to use regression modeling to predict a dependent variable of “sales index” score, which is the ratio of the regions’ actual sales divided by the target sales. The target values are constructed each year by upper management, in consultation with the sales managers, and are based on past performance and market potential within each region. The file ‘Managers.xls’ contains information on the 45 current sales managers. The following variables are included:

Sales—Ratio of yearly sales divided by the target sales value for that region. The target values were mutually agreed-upon “realistic expectations.’’
Wonder—Score from the Wonderlic Personnel Test. The higher the score, the higher the applicant’s perceived ability to manage.SC—Score on the Strong-Campbell Interest Inventory Test. The higher the score, the higher the applicant’s perceived interest in sales.Experience—Number of years of selling experience prior to becoming a sales manager.Engineer—Dummy variable that equals 1 if the sales manager has a degree in electrical engineering and 0 otherwise.
Describe your data using descriptive statistics for continuous (or quantitative) variables (e.g. mean, standard deviation, minimum and maximum values) and frequencies for discrete (or qualitative) variables (i.e. % of observations taking each distinct value for that variable).
Develop the most appropriate regression model to predict sales.
Do you think that the company should continue administering both the Wonderlic and Strong-Campbell tests? Explain.
Do the data support the argument that electrical engineers outperform the other sales managers? Would you support the idea to hire only electrical engineers? Explain.
How important is prior selling experience in this case? Explain.
Discuss in detail how the HR director should incorporate the regression model you developed into the recruiting process.
 

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