Company W is testing a sales software. Their salesforce of 500 people is divided into four regions: Northeast, Southeast, Central and West. Each sales person is expected to sell the same amount of products. During the last 3 months, only half of the sales representatives in each region were given the software program to help them manage their contacts.
The VP of Sales at WidgeCorp, who is comfortable with statistics, wants to know the possible null and alternative hypotheses for a non-parametric test on this data using the chi-square distribution. A non-parametric test is used on data that is qualitative or categorical, such as gender, age group, region, and color. It is used when it doesn't make sense to look at the mean of such variables.
The null hypothesis is that there is no difference in the average sales performance between the sales representatives that were given the software program and those that were not given the software program. The alternate hypothesis is that the new software has improved the average sales performance on average. In this case we can use the two way Chi Square test for determining the significance of the difference between the frequencies of occurrence in to four categories with two groups. In this case, the investigator wishes to see if those sales persons that have been issued the new software have performed better than those that have not been issued the software in each of the four regions.
In statistics, Analysis of variance is a method for making simultaneous comparisons between two or more means, this technique gives values that can be tested to find out whether significance relationship exists between the variable. In the given case there is a ...
This posting gives you an in-depth insight into analysis of variance, multivariate statistics, and non-parametric methods.