What are the main assumptions of linear regression?

There are several assumptions of linear regression. If any of them is violated, model predictions and interpretation may be worthless or misleading.

1. Linear relationship between features and target variable.
2. Additivity means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company have of two features - the number of items a sold and the number of items b sold. When company sells more items a the revenue increases and this is independent of the number of items b sold. But, if customers who buy a stop buying b, the additivity assumption is violated.
3. Features are not correlated (no collinearity) since it can be difficult to separate out the individual effects of collinear features on the target variable.
4. Errors are independently and identically normally distributed (y_i = B0 + B1*x1_i + … + error_i):
   1. No correlation between errors (consecutive errors in the case of time series data).
   2. Constant variance of errors - homoscedasticity. For example, in case of time series, seasonal patterns can increase errors in seasons with higher activity.
   3. Errors are normaly distributed, otherwise some features will have more influence on the target variable than to others. If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow.

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