Regression and Statistical Effects - Assignment Example

The data is divided into two groups. The total data set is divided into 4 segments: low lifetime, low revenue; low lifetime high revenue; high lifetime, low revenue, and high lifetime, high revenue. The first step ion the analysis is the calculation of bivariate Pearson’s correlation coefficient between lifetime and profit. The author graphically analyses the trends between profitability and lifetime of the customer. Then, a linear regression model is developed to describe the relation for the four segments.

The author then suggests a method using discriminant analysis that helps managers find out the most profitable customers. The research finds that it is not necessary that long-life customers are more profitable than short-life customers. The author also concludes that long-life customer do not necessarily pay more, and have higher costs. Regression analysis has been used in the study to the relation between the profitability of the customer and time. The researcher regress the profitability of the 4 segments with respect to time using the equation The relation between the profitability and time can be ascertained by the sign of the regression coefficient.

A positive coefficient indicates a positive relation between the profitability and time for the particular segment. The researcher also makes use of a dummy variable to reflect the effects of a large first month purchase because purchase amount for the first month is generally found to be higher than succeeding months. Besides this, the researcher also attempts to draw broad conclusions regarding the direction of relation between the profits and time by using Pearson’s correlation coefficient, and charts depicting the behaviors of the 2 groups.

The small correlation coefficient (0.175 for Cohort 1 and 0.219 for Cohort 2) indicates a moderate linear relation between the lifetime duration and lifetime profits. Besides this, the researcher draws the graphs with lifetime