11: Chi-Square Test — Read — Invisible Network

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11.1: Goodness of Fit Test
A chi-square goodness of fit test determines whether a sample distribution matches an expected distribution. It compares observed frequencies to what is theoretically expected. This test is useful for checking how well data fits a claimed or known distribution.
11.2: Test for Independence
A chi-square test for independence evaluates whether two categorical variables are related or independent. It uses a contingency table to compare observed and expected frequencies. This test is commonly used to analyze survey data or relationships between traits in populations.
11.3: Chapter 11 Formulas
In this section, the key formulas for Chapter 11 are displayed. These include the chi-square test for goodness of fit, which is used to compare observed frequencies to expected frequencies based on a theoretical distribution, and the chi-square test for independence, which is used to determine whether two categorical variables are associated in a contingency table. For both tests, the test statistic is calculated by summing the squared differences between observed and expected frequencies divide
11.4: Chapter 11 - Key Terms and Symbols
In this section, the key formulas and symbols for a chi-square test are provided for easy access and reference.

This page titled 11: Chi-Square Test was last modified on Fri, 05 Jul 2024 20:32:06 GMT and is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Toros Berberyan, Tracy Nguyen, and Alfie Swan.

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