Important Assumptions of Linear Regression.

Linear regression is one of the most basic and widely used Machine Learning algorithms. It is a statistical method for performing predictive analysis.

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data, with one variable acting as an explanatory variable and then as a dependent variable.

The linear regression model represents the relationship between variables with a sloped straight line.

Here are some important assumptions of Linear Regression.

Linear regression is based on the assumption of a linear relationship between the dependent and independent variables.

There is little or no multicollinearity between the features:

Multicollinearity denotes a high degree of correlation between the independent variables. Due to multicollinearity, determining the true relationship between predictors and target variables may be tough.

Assumption of Homoscedasticity: Homoscedasticity occurs when the error term is the same across all independent variable values. There should be no clear pattern distribution of data in the scatter plot with homogeneity of variance.

There are also no autocorrelations:

In error terms, the linear regression model supposes no autocorrelation. If there is any correlation in the error term, the model's correctness will be massively diminished.

The Advantage of Linear Regression

Linear regression models are simple to know and effective for smaller, less complex datasets. They can be determined by hand for small datasets.

Simple linear regression can be used to find the association continuous variables. The formula exposes a statistical but not a deterministic relationship. It can convey correlation but not causation. It clearly shows how closely the two values are associated, but not whether one variable impacted the other. For example, there is a clear relationship between study hours and test grades. It cannot discuss why students study for a certain number of hours or why a certain outcome takes place.

Conclusion

Here, we learned about Linear Regression Assumptions, advantages of Linear Regression . You can learn more about linear regression vs logistic regression in blog here.