Regression analysis

Regression Analysis

Regression analysis (pronunciation: /rɪˈɡrɛʃən əˈnælɪsɪs/) is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').

Etymology

The term "regression" was coined by Francis Galton in the 19th century to describe a biological phenomenon. The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as regression toward the mean).

Related Terms

Dependent Variable: The main factor that you’re trying to understand or predict.
Independent Variable: The factors that you hypothesize have an impact on your dependent variable.
Coefficient: A measure of the relationship between the dependent variable and each independent variable.
Linear Regression: A linear approach to modeling the relationship between a dependent variable and one or more independent variables.
Multiple Regression: A statistical technique used to predict the outcome of a response variable through several explanatory variables and model the relationships between them.
Logistic Regression: A statistical model that in its basic form uses a logistic function to model a binary dependent variable.
Residuals: The difference between the observed value of the dependent variable and the predicted value from the regression model.
Correlation: A statistical measure that expresses the extent to which two variables are linearly related.
Outliers: An observation that lies an abnormal distance from other values in a random sample from a population.