Use Common Statistical Tests to Draw Conclusions From Data

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Company W is testing a sales software. Its sales force of 500 people is divided into four regions: Northeast, Southeast, Central, and West. Each salesperson 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 Northeast using the software sold 165 and the group with no software sold 100, The Southeast with software sold 200 and the group with no software sold 125. The Central groups with software sold 175 and the group with no software sold 125. The West group with software sold 180 and the group with no software sold 130. Using this data calculate the Chi Square statistic.

The VP of Sales at WidgeCorp, who is comfortable with statistics, wants to know the possible null and alternative hypotheses for a nonparametric test on this data using the chi-square distribution. A nonparametric test is used on data that are qualitative or categorical, such as gender, age group, region, and color. It is used when it does not make sense to look at the mean of such variables. (You can refer to the article for this phase for further information.)

The following information may be helpful in understanding Chi Square and Hypothesis testing:

Chi Square / Hypothesis Testing

Please review this helpful video. The presenter uses the “flip of the coin” and the “role of the die.” These are examples and analogies used in the CTU resources.

The following are assumptions you might make in this assignment that might make the assignment more helpful and make the responses more uniform:

  • Continue to use the Widgecorp context. Assume the salespersons are test sales of snack foods or drinks.
  • Additionally, assume you have the same number of salespersons in each region.

Reference

Bozeman Science. (2011, November 13). Chi-squared test [Video file]. Retrieved from

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