Which of the following statistical methods requires two or more categorical variables?

This is because a chi-squared test is a type of statistical method that tests the association or

independence between two or more categorical variables, such as gender, race, or occupation. A chi-

squared test can be used to compare the observed frequencies of the categories with the expected

frequencies under the null hypothesis of no association or independence. For example, a chi-squared

test can be used to determine if there is a relationship between smoking and lung cancer. The other

statistical methods do not require two or more categorical variables. Here is why:

Simple linear regression is a type of statistical method that models the relationship between a

continuous dependent variable and a continuous or categorical independent variable, such as height,

weight, or education level. A simple linear regression can be used to estimate the slope and

intercept of the best-fitting line that describes how the dependent variable changes with the

independent variable. For example, a simple linear regression can be used to predict the weight of a

person based on their height.

Z-test is a type of statistical method that tests the significance of the difference between a sample

mean and a population mean, or between two sample means, when the population standard

deviation or the sample sizes are large enough. A z-test can be used to compare the average scores

of two groups of students on a standardized test.

Two-sample t-test is a type of statistical method that tests the significance of the difference between

two sample means when the population standard deviation is unknown or the sample sizes are

small. A two-sample t-test can be used to compare the average salaries of two groups of employees

in different departments.