Chi-Square - Chapter 18 The Chi-Square Statistic Parametric...

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Unformatted text preview: Chapter 18: The Chi-Square Statistic Parametric and Nonparametric Tests • Chapter 18 introduces two non- parametric hypothesis tests using the chi-square statistic: the chi-square test for goodness of fit and the chi-square test for independence. Parametric and Nonparametric Tests (cont.) • The term "non-parametric" refers to the fact that the chi‑square tests do not require assumptions about population parameters nor do they test hypotheses about population parameters. • Previous examples of hypothesis tests, such as the t tests and analysis of variance, are parametric tests and they do include assumptions about parameters and hypotheses about parameters. Parametric and Nonparametric Tests (cont.) • The most obvious difference between the chi‑square tests and the other hypothesis tests we have considered (t and ANOVA) is the nature of the data. • For chi‑square, the data are frequencies rather than numerical scores. The Chi-Square Test for Goodness-of-Fit • The chi-square test for goodness-of-fit uses frequency data from a sample to test hypotheses about the shape or proportions of a population. • Each individual in the sample is classified into one category on the scale of measurement. • The data, called observed frequencies , simply count how many individuals from the sample are in each category. The Chi-Square Test for Goodness-of-Fit (cont.) • The null hypothesis specifies the proportion of the population that should be in each category....
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Chi-Square - Chapter 18 The Chi-Square Statistic Parametric...

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