2Regression is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable (target) and one or more independent variables (or ‘predictors’).
Regression analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed.
It is used in variety of places such as forecasting, time series analysis etc. across industries.
- Regression analysis is used characterize the variation of the dependent variable around the prediction of the regression function using a probability distribution
- A function of the independent variables called the regression function is to be estimated
- Regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable as correlation does not prove causation.
Types of regression:
- Linear Regression
- Simple Linear Regression
- multiple linear regression.
- Logistic Regression
- Simple Logistic Regression
- Multiple Logistic Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
- ElasticNet Regression1