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Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.
The following are the major assumptions made by standard linear regression models with standard estimation techniques (e.g. ordinary least squares): 1  Weak exogeneity. This essentially means that the predictor variables x can be treated as fixed values, rather than random variables.