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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.
Testing for independence (lack of correlation) of errors. Testing for homoscedasticity (constant variance) of errors. Testing for normality of the error distribution. There are four principal assumptions which justify the use of linear regression models for purposes of inference or prediction: (i) linearity and additivity of the relationship between dependent and independent variables: