I have a series of figures for car sales that show me (a) the usual number of car sales for particular models and (b) the number of car sales by a particular car salesperson for each model.

Let's say the values are:

Model | Sales by all Salespeople | Sales by Salesperson x
    A |                      100 |                     20
    B |                       50 |                     40
    C |                       50 |                      0

I want to find out if Salesperson x is significantly more responsible for sales of a particular model.

My first thought is to determine the rate of sales per model vs the rate of sales per model for the salesperson, i.e.:

Model | % Total Sales for all Salespeople | % Total Sales for Salesperson x
    A |                               50% |                             33%
    B |                               25% |                             66%
    C |                               25% |                              0%

However, naturally every salesperson has a significant variation in the cars they sell.


  • How do I determine if salesperson variation from the mean is statistically significant?

Initial Thoughts:

I think the Pearson correlation has some bearing, as perhaps may chi-square distribution, but I really don't have the background yet to understand why, so introductory help is appreciated.


Chi square test, but I'd set it up as follows:

Model | this salesman | all other salesmen

A | 20 | 80

B | 40 | 10


Chi square test of independence, with expected values equal to row total * column total / grand total.

Null hypothesis: this salesman = same as all other.

Most stat books will have this test.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.