Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. The simplest form of linear regression involves two variables, with one being the predictor variable (independent variable) and the other being the response variable (dependent variable). The goal is to find the best-fitting linear relationship that can be used to make predictions about the response variable based on the values of the predictor variable.

The linear regression equation takes the form:

$Y$

• Y is the dependent variable (the variable we are trying to predict),
• X is the independent variable (the variable used for prediction),
• β0​ is the intercept (the value of Y when X is 0),
• β1​ is the slope (the change in Y for a one-unit change in X),
• ε represents the error term, accounting for the variability in Y that cannot be explained by the linear relationship.

The goal of linear regression is to estimate the values of β0​ and β1​ that minimize the sum of the squared differences between the observed and predicted values of the dependent variable. This process is often done using the method of least squares.

Linear regression is widely used in various fields, including economics, biology, engineering, and social sciences, for tasks such as predicting future values, understanding the relationship between variables, and identifying trends in data.

For a video tutorial on performing this analysis in SPPS, click https://youtu.be/xp4Sffz5bbA?si=py6prNrG7GdLmyfZ

For a video tutorial on performing this analysis in Excel, click https://youtu.be/F_U5m77lqMU?si=N8x4lEYvbquIEi8y