Assumptions of Linear Regression in Linear Regression

Linear regression relies on several key assumptions to ensure the validity of its results. These assumptions include linearity, which means that the relationship between the independent and dependent variables is linear; independence, indicating that the residuals (errors) are independent of each other; homoscedasticity, which means that the residuals have constant variance at all levels of the independent variable; normality, suggesting that the residuals are normally distributed; and no multicollinearity, meaning that the independent variables are not highly correlated with each other. Violating these assumptions can lead to biased estimates, unreliable statistical tests, and invalid predictions. Therefore, it is crucial to check these assumptions before interpreting the results of a linear regression analysis.

1. Assumptions of Linear Regression Shruti More – 927 Anjali Prajapati – 901

2. Linear Regression

3. Linear Analysis Y = β ​+ β 1 ​ X 1 ​+ β 2 ​ X 2 ​+…+ β p ​ X p ​+ ϵ

4. Linearity

5. Check for Linearity Scatter Plots : 1.

6. 2. Residual V/S Fitted plot : Residuals = Observed values – Estimated values(fitted values)

7. What if the data founds to be Non-linear?

8. Transformed Dependent Variable

9. Normality

10. Detection

11. Transformation

12. Thank You!