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Null and alternative hypotheses for non-parametric test

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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. (You can refer to the article for this week for further information.)

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To use the chi-square distribution, the quantitative variable sales volume has to be converted into categories or

groups such as "Achieved target" vs "Did not achieve target".

(1) A chi-square test that can be carried out in this context ...

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