Assumptions of Linear Regression Explained :- By Saurabh
Before Implementing a Linear Regression model,its important to go through the assumptions.As linear Regression is a parametric algorithm, It's important that the data satisfies the following assumptions So lets go through it one by one. 1.Linearity This assumes that there is a linear relationship between the predictors (e.g independent variable) and the response variable(e.g dependent variable). You can use the scatter plot to detect the linearity of the variables. 2.No Outliers There should be no outliers in the data.You can check for outliers with the help of box plot 3. No Multicollinearity There should be no multicollinearity between the independent variables. 4. Autocorrelation There should be no correlation between the residual (error) terms.Absence of this concept is known as autocorelation. 5. Normality The dependant varible should be normally distributed.If not you convert it through log function. It’s not uncommon for assumptions to be violated on real-world data, but it...