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A Chi-square test for dependance is appropriate for what type of data? Qualitative Continuous Discrete

A Chi-square test for dependance is appropriate for what type of data? Qualitative Continuous Discrete

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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### Understanding the Chi-square Test for Dependence **Question:** A Chi-square test for dependence is appropriate for what type of data? - ○ Qualitative - ○ Continuous - ○ Discrete **Explanation:** The Chi-square test for dependence is used to determine if there is a significant association between two categorical variables. This means that it is particularly suitable for qualitative (categorical) data, where data is grouped into categories (e.g., gender, color, type, etc.). The test assesses whether the distribution of sample categorical data matches an expected distribution. **Key Points:** - **Qualitative Data:** Includes categories and labels (e.g., colors, brands, types). - **Continuous Data:** Includes measurements that can take any value within a range (e.g., height, weight, temperature). - **Discrete Data:** Includes countable values, often integers (e.g., number of students, cars). For further reading, see [Chi-square Test of Independence on Wikipedia](https://en.wikipedia.org/wiki/Chi-squared_test).
Transcribed Image Text:### Understanding the Chi-square Test for Dependence **Question:** A Chi-square test for dependence is appropriate for what type of data? - ○ Qualitative - ○ Continuous - ○ Discrete **Explanation:** The Chi-square test for dependence is used to determine if there is a significant association between two categorical variables. This means that it is particularly suitable for qualitative (categorical) data, where data is grouped into categories (e.g., gender, color, type, etc.). The test assesses whether the distribution of sample categorical data matches an expected distribution. **Key Points:** - **Qualitative Data:** Includes categories and labels (e.g., colors, brands, types). - **Continuous Data:** Includes measurements that can take any value within a range (e.g., height, weight, temperature). - **Discrete Data:** Includes countable values, often integers (e.g., number of students, cars). For further reading, see [Chi-square Test of Independence on Wikipedia](https://en.wikipedia.org/wiki/Chi-squared_test).
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