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Chi square

# Chi square - Chi square goodness of fit tests to see if the...

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Chi square goodness of fit tests to see if the sample we have fits with the hypothesized distribution close enough. There is some room for error, but the test shows us if this is more than natural variation or error. Continuing with the M&M example, for a bag of 200 M&Ms, the expected (hypothesized values), would be: Red: 16.6% of 200 = 33.2 Blue: 16.6% of 200 = 33.2 Yellow: 16.6% of 200 = 33.2 Orange: 16.6% of 200 = 33.2 Brown: 16.6% of 200 = 33.2 Green: 16.6% of 200 = 33.2 Hypothesized (expected) values don’t have to be integers. Now, in JMP, you can record the ACTUAL breakdown of colors to see if it matches the model (hypothesized values). In JMP: Open your data set which could either have a column of quantitative values such as: Brown Blue Red Red Red Yellow Orange Red OR you could have one row for each color and its frequency: Red: 21 Blue: 13 Etc… 1. Analyze >> Distribution 2. Drag the column of interest to the Y. (If you have the second type

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Unformatted text preview: of data set, drag the frequency column to the Freq box. 3. Click OK 4. Click on red arrow and select Test Probabilities 5. An area will appear to enter your hypothesized distributions. Here is where you could enter 16.6, 16.6, etc. or whatever the model says the distribution of the population should be. 6. Click Done. 7. Observe the Prob>Chisq for the Pearson test. 8. If it is less than .05, you reject Ho. The population distribution most-likely does not fit the model. Else, you fail to reject Ho. The population most-likely does fit the model. Test of Independence We are still looking at distribution of a population amongst a categorical variable. However, now we are looking at introducing a second category. This is similar to correlation analysis we did with quantitative variables earlier on....
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• Fall '13
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• Chi-square distribution, Pearson's chi-square test, chi square goodness

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