Statistics for managers class discussion wk9 - Coursework Example

Summary: Multiple Regression Analysis for Dunnhumby Multiple regression model for dunnhumble involved the use of a logistic regression in order to analyze the data that the researchers collected from the customers. The researchers primarily collected their data from electronic point-of-sale transactions, discount card purchase records, and traditional market research. Afterward, they conducted a logistic regression analysis that included the lifestyle, preferences, and behavior of customers as the criterion variables.

The aim of the regression analysis was to find a line of best fit that would indicate the most effective merchandizing programs to be implemented. The logistic regression model involved the development of an approximated multiple regression equations. The probability prediction that a customer belongs to a particular client group was the dependent variable. On the other hand, the measures of shopping behaviors of customers, represented as x1, x2, x3, . . . , xp, were the independent variables in the regression analysis.

The independent variables included the day/hour of purchase, items purchased, and amount purchased. The logistic regression analysis was useful in the sense that it helped the marketers of dunnhumby to identify the most crucial independent variables as far as predicting customer population and customer group is concerned.In view of how dunnhumby applied the multiple regression model, the model can also be used in other business scenarios. For example, an automobile company such as GM Motors can use a multiple regression analysis to identify its customer shopping behavior, season of shopping, customer preferences, and customer experiences, for a particular model of car sold at a given price.

It will help the company to produce and distribute the car that most consumers prefer in the largest quantity. Besides, Amazon.com can use a multiple regression analysis to determine the items that are bought most frequently by customers, the month that most customers visit the website to check the item, and the amount of items purchased within a period. Subsequently, Amazon will develop an interface that enables all customers to preview the featured item and perhaps buy it. Fundamentally, multiple regression analysis allows the determination of the connection between multiple independent variables and one dependent variable.

However, it has the major drawback that it makes assumptions that need to be checked. In addition, multiple regression analysis uses unknown independent variables to draw conclusions and make recommendations. Overall, multiple regression analysis allows researchers and marketers to identify the most suitable business strategy for organizations to adopt.ReferencesAnderson, D. R., Sweeney, D. J., & Williams, T. A. (2012). Statistics for business and economics. Mason, Ohio: Southwestern Cengage Learning.