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Chi-Square Unequal Frequencies
This week, we are looking at hypothesis tests using qualitative variables. All previous hypothesis tests have involved quantitative variables. Specifically, we are going to perform a Chi-Square Goodness of Fit unequal frequencies test. This test is used to compare an expected result to an observed one. This might involve comparing a survey taken 10 years ago to a recent one to see if results have changed. Unequal frequencies could also be used to compare a company’s claim about its product to a sample survey. The null hypothesis will be that the expected equals the observed and the alternative will be the expected is not equal to the observed. You will need to apply this wording to your problem (see the example).
To prepare for this Discussion:
Review the Week 5 Discussion resources.
Look at the data set that you used in Discussion 1 or 2. In order to do this week’s Discussion, you will need to have data involving qualitative variables. Likely, you will need to modify your data set. You may want to start over with a new data set
Post a 1- to 2-paragraph write-up that includes the following:
Describe your scenario and provide your data set.
State your null and alternative hypotheses. Be sure to state your chosen level of significance.
Enter your data into the Sample Editor in Statdisk. Choose Analysis, Goodness of Fit and unequal frequencies.
Perform the test and state your conclusion. Remember the conclusion statement should be of the following format: Since the p-value of # is more/less than the level of significance of #, the null hypothesis is/is not rejected; therefore, the data supports (paraphrase the hypothesis supported). Did you get the results that you expected?
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