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## PowerPoint Slideshow about ' Chapter 16 – Categorical Data Analysis' - marny-morse

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Chi-Square

- Categorical data are statistically analyzed by means of a chi-square statistic.
- A single variable is analyzed with the chi-square goodness-of-fit test.
- The goodness-of-fit test consists of determining whether the frequency counts in the categories of the variable agree with a specific distribution.

The Multinomial Experiment

- The experiment consist of n identical experiments.
- The outcome of each trial falls into one of k categories.

The Multinomial Experiment

- The probabilities associated with the k outcomes denoted by p1, p2, p3,…,pk remain the same from trial to trial. Since there are k possible outcome we have:

The Multinomial Experiment

- The experimenter records the values o1, o2,....,ok where oj (j = 1, 2, .....,k) is equal to the number of trials in which the outcome is in category j.
- Note: o1+o2+......+ok = n

Chi-Square Goodness-of-Fit Test

- Application: Multinomial experiments.
- Assumptions:
- The experiment satisfies the properties of a multinomial experiment.
- No expected cell counts, ej, is less than 1, and no more than 20% of the ej‘s are less than 5. (This is so the chi-square approximation will be good)

Chi-Square Goodness-of-Fit Test

- The test is a right-tailed test, where the p-value is found in the chi-square table with k-1 degrees of freedom. Usually the exact value cannot be found, but bounds for it can be found from the closest to the observed value of the chi-square statistic.
- Chi-Square Statistic:

Chi-Square Test of Independence

- Application: Test the independence of the classifying variables

Assumptions:

- The experiment satisfies the properties of a multinomial experiment.
- No expected cell counts, ej, is less than 1, and no more than 20% of the ej‘s are less than 5. (This is so the chi-square approximation will be good)

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