Regression Analysis Cheat Sheet

A Regression Analysis Cheat Sheet is a reference guide that helps users quickly access and understand key concepts, formulas, and steps involved in performing regression analysis. It provides a handy summary of information to assist in conducting and interpreting regression analysis in various fields such as statistics, economics, social sciences, and more.

FAQ

Q: What is regression analysis?
A: Regression analysis is a statistical method used to investigate the relationship between a dependent variable and one or more independent variables.

Q: What is the purpose of regression analysis?
A: The purpose of regression analysis is to understand and predict the relationship between variables, identify factors that influence the dependent variable, and estimate the effects of these factors.

Q: What are the types of regression analysis?
A: There are various types of regression analysis, including linear regression, multiple regression, logistic regression, and polynomial regression.

Q: What is linear regression?
A: Linear regression is a type of regression analysis that models the relationship between a dependent variable and one independent variable with a linear equation.

Q: What is multiple regression?
A: Multiple regression is a type of regression analysis that models the relationship between a dependent variable and two or more independent variables.

Q: What is logistic regression?
A: Logistic regression is a type of regression analysis used when the dependent variable is categorical or binary.

Q: What is polynomial regression?
A: Polynomial regression is a type of regression analysis that models the relationship between a dependent variable and an independent variable using a polynomial equation.

Q: What are the assumptions of regression analysis?
A: The assumptions of regression analysis include linearity, independence, homoscedasticity (constant variance), and normality of residuals.

Q: How is regression analysis performed?
A: Regression analysis is performed by fitting a regression model to the data, estimating the model parameters, and assessing the model's goodness of fit.

Q: What is the coefficient of determination (R-squared)?
A: The coefficient of determination, also known as R-squared, is a measure of how well the regression model fits the data. It represents the proportion of the variance in the dependent variable that can be explained by the independent variables.