Regression analysis is a most commonly use statistical tools for modelling or forecasting.
What is Regression Analysis?
Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables. For example, relationship between income (independent variable) and expenditure (dependent variable) is best studied through regression.
Why do we use Regression Analysis?
There are multiple benefits of using regression analysis. They are as follows:
1. It indicates the significant relationships between dependent variable and independent variable.
2. It indicates the strength of impact of multiple independent variables on a dependent variable.
Which type of Regression analysis is appropriate for your data?
Linear and Logistic regressions are usually the common techniques people learn in regression analysis. Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions.
Based on the nature of dependent variable, technique of regression analysis is varied. Types of regression analysis are Linear Regression, Logistic Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Bayesian regression, Jackknife regression and so on .
