A contingency table is a tool used to summarize and analyze the relationship between two categorical variables. It is a type of cross-tabulation that displays the frequencies or counts of the combinations of categories for the two variables. To create a contingency table, the following steps are typically followed:Identify the two categorical variables to be analyzed.Collect

 One-way ANOVA (Analysis of Variance) is a statistical test used to compare the means of three or more samples to determine if there are significant differences among them. It is based on the assumption that the samples are drawn from normally distributed populations with equal variances.Steps in One-way ANOVA:Specify the null and alternative hypotheses. The

 The F Test for Equality of Variances between two groups is a statistical test used to compare the variances of two samples to determine whether they are equal. It is based on the assumption that the samples are drawn from normally distributed populations.Steps in the F Test for Equality of Two Variances:Specify the null and

 The Chi-Square Test for One Variance is a statistical test used to compare the variance of a sample to a known population variance. It is used to test a hypothesis about the population variance and is based on the assumption that the sample is drawn from a normally distributed population.Steps in the Chi-Square Test for

 (also called Two Sample Z Test for Proportions) Two Proportions Z Test A two-proportions z-test is a statistical test used to compare the proportions of two independent samples. It is used to test a hypothesis about the difference between the proportions of the two samples and is based on the assumption that the samples are drawn

 Paired t-Test A paired t-test is a statistical test used to compare the means of two related samples or matched pairs. It is used to test a hypothesis about the difference between the means of the two samples. It is based on the assumption that the differences between the pairs are normally distributed.Dependent vs Independent