chi square and regression

Chi-Square Tests Chi-Square tests are used to determine if there is a significant association between two categorical variables. They compare the observed frequencies in each category to the frequencies we would expect if there were no association between the variables. The most common types are: Pearson's Chi-Square Test: Used to test the independence of two categorical variables in a contingency table1. Chi-Square Goodness of Fit Test: Used to determine if a sample data matches a population with a specific distribution1. Regression Regression analysis is used to understand the relationship between a dependent variable and one or more independent variables. There are different types of regression: Linear Regression: Models the relationship between a dependent variable and one or more independent variables using a linear equation. It's used for predicting continuous outcomes2. Logistic Regression: Used when the dependent variable is binary (e.g., yes/no, success/failure). It estimates the probability of a binary outcome based on one or more predictor variables3. Key Differences Chi-Square Tests: Focus on the association between categorical variables. Regression: Focus on predicting the value of a dependent variable based on one or more independent variables.