Database Marketing Gets Serious!

The students successfully completed the first assignment with descriptive statistics, cross tabs and chi squared analyes. They learned that categorical data puts data in groups (think cross tabs) and that metric or continuous data is more suited to other types of statistical analysis such as t-tests, correlation and regression. In marketing, we like variation and the null hypothesis is usually what we don't want to have happen, no difference in counts for the chi square, no difference in means for a t-test and no linear relationship for correlation and regression. We are looking for a p value of under .05 for significance level. We had fun looking at live data and used the President's approval rating as a dependent variable in a simple regression. There was a regression relationship (F stat is <.05), 83% of the variance (Adjusted R sqared) in approval rating is explained by ONE significant variable (p < .05), unemployment rate. Interpreting the regression, the President's approval rating drops by about 6.6% for every one percentage point increase in the unemployment rate. The class is working now on creating a model to predict performance in our IM classes. They are looking to create a parsimonious model, that means as few variables with the most explanatory power, to explain an particular hypothesis.