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15: Chi-square

  • Page ID
    42090
    • Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin
    • University of Missouri System

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    • 15.1: Chi-Square Goodness of Fit Test
      This page discusses the chi-square (\(\chi^2\)) test, a nonparametric method for analyzing categorical data through frequency distributions, focusing on the goodness of fit. It details a study on public preference for pineapple on pizza, employing a chi-square test with data from 45 participants. The resulting test statistic of 1.08 was below the critical value of 3.84, leading to failure to reject the null hypothesis of equal preferences. An effect size of 0.
    • 15.2: Chi-Square Test of Independence
      This page discusses the \(\chi^2\) test for independence, used to assess the relationship between two categorical variables via contingency tables. It details the process of hypothesizing independence, calculating expected frequencies, and evaluating the \(\chi^2\) statistic. The analysis reveals a significant relationship between childhood college sports viewership and college selection influenced by sports teams, as evidenced by an observed chi-square value of 20.


    This page titled 15: Chi-square is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin via source content that was edited to the style and standards of the LibreTexts platform.