Linear Regression Models

Definition

Linear regression models are statistical models that examine the relationship between a dependent variable and one or more independent variables. They use a straight line to represent this relationship and can be used to make predictions or understand the impact of changes in the independent variables on the dependent variable.

Analogy

Think of linear regression models as fitting a puzzle piece into a larger picture. The model helps us understand how different pieces (independent variables) fit together to create the complete picture (dependent variable).

Related terms

Positive Correlation: Positive correlation refers to a relationship between two variables where an increase in one variable is associated with an increase in the other variable.

Computer Outputs: Computer outputs refer to the results generated by statistical software when running linear regression models, such as coefficients, p-values, and R-squared values.

Independent Variables: Independent variables are factors that are manipulated or controlled in an experiment or study and are used to predict or explain changes in the dependent variable.