Assumptions of Linear Regression in Linear Regression
- 1. Assumptions of Linear Regression SHRUTI MORE – 927 ANJALI PRAJAPATI – 901
- 2. Linear Regression Relationship between the independent variable(s) and the dependent variable is linear that can be represented by a straight line when plotted on a graph. Mathematically a linear relationship is often expressed as; y=mx+b A set of statistical techniques used to model the relationship between a dependent variable and one or more independent variables. The goal is to understand how changes in the independent variable(s) are associated with changes in the dependent variable.
- 3. Linear Analysis Y=β0+β1X1+β2X2+…+βpXp+ϵ •Y represents the dependent variable. β0 is the intercept (the value of Y when all independent variables are zero). β1,β2,…,βp are the coefficients associated with each independent variable X1,X2,…,Xp, respectively. X1,X2,…,Xp are the independent variables. •ϵ represents the error term
- 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?
- 9. Normality
- 14. Detection Histogram Q-Q Plot Shapiro-Wilk test Komolgorov- Smirnov test Anderson- Darling test
- 16. Thank You!