Overview of the Linear Regression Model

A linear regression attempts to predict the value of a measure response variable as a linear function of one or more effects. The linear regression model uses the least squares method to determine the model. The least squares method creates a line of best fit by minimizing the residual sum of squares for every observation in the input data set. The residual sum of squares is the vertical distance between an observation and the line of best fit. The least squares method requires no assumptions about the distribution of the input data.

The linear regression model requires a measure response variable and at least one effect variable or interaction term.