- School of Mathematical Sciences
- Ross Hall 2239
- Campus Box 122
- University of Northern Colorado
- Greeley, CO 80639
- Phone: (970) 351-2820
- Fax: (970) 351-1225
- Dr. Robert Powers
- Ross Hall 2240-C
- Campus Box 122
- University of Northern Colorado
- Greeley, CO 80639
- Phone: (970) 351-1157
- robert.powers@unco.edu
Categorical Tests
Math 550 at UNC, Summer 2013
The following files include information on continuous random variables and continuous distributions such as the uniform and normal distributions. Click on the "Preview" thumbnails to see a slideshow of the documents, or click the "PDF" button to download a handout.
Downloadable Handouts
| Document Title | Content | Preview | Download |
| Goodness-of-Fit Examples | Consider whether the number of phone calls fits a uniform distribution; apply the goodness-of-fit test to suspected forged checks using Benford’s Law. |
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| Testing Claims about the Distribution of a Variable | Step-by-step guide to applying the Goodness of Fit test using the Chi-square distribution. |
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| Expected Counts | Relates the chi-squared distributiop to expected counts of independent repeated trials. |
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| The Goodness-of-Fit Test | Outlines the goodness-of-fit test for determining if a sample fits a potential distribution. Includes an Expected M & M's activity. |
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| Expected M&Ms | Activity to determine if two distributions of colored candies fit within pre-specified parameters. |
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| Independence and Homogeneity Examples | Look at potential indendence and homogeneity questions in four examples, including an interesting example of whether the proportions of morning and night people are the same in each age group. |
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| Testing Claims about the Proportions of Multiple Populations | Step-by-step guide to using Chi-squared statistics to test whether proportions of people in multiple populations are equal. |
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| Testing Claims about the Independence of Two Variables of a Single Population | Step-by-step guide to testing for independence between two variables. |
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| Expected Counts | Basic outline of tests for independence and its relation to expected counts. |
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