- Assumptions of Linear Regression
Linear regression is an analysis that assesses whether one or more predictor variables explain the dependent (criterion) variable.
It has five key assumptions:
- Linear relationship
- Multivariate normality
- No or little multicollinearity
- No auto-correlation
- Homoscedasticity
See also:
http://www.statisticssolutions.com/assumptions-of-linear-regression/
https://statistics.laerd.com/spss-tutorials/linear-regression-using-spss-statistics.php