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.