class17 - More Chi-Square Statistics Click to edit Master...

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Click to edit Master subtitle style 11/13/10 More Chi-Square Statistics
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11/13/10 2Slide 1- 2 Chi-Square Test of Homogeneity For the chi-square test of goodness- of-fit, you were looking at comparing the classes of a categorical variable to a model of what was expected Good fit meant your data “mirrored” what we expected from the model, and we failed to reject the null We rejected the null if our data was not a good fit for the model If we want to compare the
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11/13/10 3Slide 1- 3 Comparing Observed Distributions (cont.) We will use the same statistics as was used for the goodness-of-fit, but instead of comparing our distribution to a model, we are comparing the distributions of multiple groups across the same categorical variable. The expected counts are found directly from the data and we have different degrees of freedom.
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11/13/10 4Slide 1- 4 Assumptions and Conditions The assumptions and conditions are the same as for the chi-square goodness-of-fit test: Counted Data Condition: The data must be counts. Randomization Condition and 10% Condition: As long as we don’t want to generalize, we don’t have to check these condition.
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11/13/10 5Slide 1- 5 Calculations To find the expected counts, we multiply the row total by the column total and divide by the grand
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class17 - More Chi-Square Statistics Click to edit Master...

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