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- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/12%3A_F_Distribution_and_One-Way_ANOVA/12.08%3A_PracticeThis page discusses statistical exercises on variances focusing on F tests and one-way ANOVA. It presents various scenarios involving comparisons among coworkers, students, cyclists, and teams regardi...This page discusses statistical exercises on variances focusing on F tests and one-way ANOVA. It presents various scenarios involving comparisons among coworkers, students, cyclists, and teams regarding commute times, test scores, sports performance, and ages for obtaining driver licenses.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/14%3A_Apppendices/14.00%3A_A__Statistical_Tables/14.0.00%3A_F___DistributionThis page presents a table of critical values for the F-distribution, organized by degrees of freedom for both the numerator and denominator, across various significance levels (p-values) ranging from...This page presents a table of critical values for the F-distribution, organized by degrees of freedom for both the numerator and denominator, across various significance levels (p-values) ranging from 0.001 to 0.100. Each row represents specific numerator degrees of freedom while the columns detail corresponding critical values needed for hypothesis testing in statistical analyses like ANOVA and regression.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/11%3A_The_Chi-Square_Distribution/11.06%3A_Comparison_of_the_Chi-Square_TextsThis page discusses the χ2 test applied in three scenarios: 1) Goodness-of-Fit, assessing if a sample fits a known distribution; 2) Independence, evaluating the relationship between two qualitative va...This page discusses the χ2 test applied in three scenarios: 1) Goodness-of-Fit, assessing if a sample fits a known distribution; 2) Independence, evaluating the relationship between two qualitative variables; and 3) Homogeneity, determining if two populations share the same distribution. Each scenario has a null hypothesis positing a fit or independence, and an alternative hypothesis suggesting otherwise.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/12%3A_F_Distribution_and_One-Way_ANOVA/12.01%3A_Test_of_Two_VariancesThis page discusses the F distribution, crucial for comparing variances in contexts like ANOVA. It covers the F test for variance equality, highlighting the need for normality and independence, with t...This page discusses the F distribution, crucial for comparing variances in contexts like ANOVA. It covers the F test for variance equality, highlighting the need for normality and independence, with the F statistic as a ratio of sample variances compared to critical values.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/11%3A_The_Chi-Square_Distribution/11.05%3A_Test_for_HomogeneityThis page discusses the distinction between goodness-of-fit tests and tests for homogeneity, focusing on the latter's use of χ^2 statistics to compare population distributions, requiring a minimum exp...This page discusses the distinction between goodness-of-fit tests and tests for homogeneity, focusing on the latter's use of χ^2 statistics to compare population distributions, requiring a minimum expected value of five. It provides a case study on male and female college students' living arrangements showing differing distributions and includes exercises about car distributions among different demographics and applications to Ivy League schools.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/11%3A_The_Chi-Square_Distribution/11.02%3A_Test_of_a_Single_VarianceThis page emphasizes the importance of understanding both the mean and variability in populations, especially in production and assessments. It covers hypothesis testing for population variance, detai...This page emphasizes the importance of understanding both the mean and variability in populations, especially in production and assessments. It covers hypothesis testing for population variance, detailing null and alternative hypotheses, test statistics, and examples of variance testing in various contexts like education and service quality.
