Simple linear regression.
Polynomial regression.
Multiple linear regression.
Multiple linear regression with interactions.
Assessing normality assumption for linear regression in R.
Draw multivariate statistical data in R.
Comparing nested models in linear regerssion using ANOVA with R.
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The normality assumption in linear regression is necessary to ensure the estimates of parameters are unbiased and the hypothesis testing is correct. It states that for the fixed or given values of explanatory variables, the dependent variables are normally distributed around the mean 0. It is equivalent to say that the residuals after model estimation follow a normal distribution with the mean 0.
Linear regression is widely used to model the relationship between response or dependent variable and explanatory or independent variables. The parameter in the model has linear form. When there is only one explanatory assumed in the model, it is called simple linear regression.