Linear Regression: Notes and Interview Questions
What is a Linear Regression? Linear regression is adopting a linear approach to modeling the relationship between a dependent variable (scalar response) and one or more independent variables (explanatory variables). What Are the Basic Assumptions? - Linear relationship: there is a linear relationship between the features and target. - Multivariate normality: all variables to be multivariate normal. When the data is not normally distributed, a non-linear transformation might help. (KS test is used to check normality) - No multi-collinearity: independent variables should not be too highly correlated with each other. (drop one of the variables) - No auto-correlation: residuals should not be dependent on each other. (DW test is used to detect autocorrelation) - Should be Homoscedastic: variance/spread of the errors should be constant. (use the Box-Cox normality plot to transform Y variable to achieve homoscedasticity) - Normality: error terms should be normally distributed. Advantages Line...