# What are the basic assumptions of the Linear Regression Algorithm?

he basic assumptions of the Linear regression algorithm are as follows:

• Linearity: The relationship between the features and target.
• Homoscedasticity: The error term has a constant variance.
• Multicollinearity: There is no multicollinearity between the features.
• Independence: Observations are independent of each other.
• Normality: The error(residuals) follows a normal distribution.

Now, let’s break these assumptions into different categories:

### Assumptions about the form of the model:

It is assumed that there exists a linear relationship between the dependent and the independent variables. Sometimes, this assumption is known as the ‘linearity assumption’.

• Normality assumption: The error terms, ε(i), are normally distributed.
• Zero mean assumption: The residuals have a mean value of zero.
• Constant variance assumption: The residual terms have the same (but unknown) value of variance, σ2. This assumption is also called the assumption of homogeneity or homoscedasticity.
• Independent error assumption: The residual terms are independent of each other, i.e. their pair-wise covariance value is zero.