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T-Tests and Chi-Square Tests in R: Practical Applications
In this hands-on section, we will delve deeper into specific statistical tests and how to perform them in R:
T-Tests: Explore the world of t-tests, a fundamental tool for comparing the means of two groups. You will learn how to conduct independent and paired t-tests, accompanied by examples and interpretation of the results.
Chi-Square Tests: Chi-square tests are invaluable for analyzing categorical data. You will master the chi-square goodness-of-fit test and the chi-square test of independence. Through practical examples, you will grasp their significance and application.
Performing t-tests and chi-square tests in R is essential for comparing means and analyzing categorical data. Here's a practical guide on how to conduct these tests in R:
Independent T-Test: This test is used to compare the means of two independent groups. You can perform it using the t.test() function. For example, comparing the exam scores of two different groups:
Paired T-Test: Use this test when you have paired or matched data points. It assesses the difference between paired observations. You can perform it using the t.test() function. For example, comparing pre- and post-treatment scores:
Chi-Square Goodness-of-Fit Test: This test checks if the observed frequencies match the expected frequencies in a categorical variable. Use the chisq.test() function. For example, testing the distribution of eye colors in a population:
chisq_test_result <- chisq.test(observed_frequencies, p = expected_probabilities)
Chi-Square Test of Independence: This test examines the association between two categorical variables. It helps determine if there's a relationship between the two. Use the chisq.test() function. For example, testing the association between gender and preferred car color:
