Regression Analysis

Introduction
The procedure of predictive modeling that looks over the relationship between the dependent and the independent variable. The dependent variable is the ‘Target’ whereas the independent variable is the ‘Predictor’. Regression analysis is used for prediction, time series modeling and finding the natural effects of the rapport between the variables. It basically predicts the value of one variable on the basis of other several variables.

Reasons for using Regression Analysis
There are two main reasons for using regression analysis that is followed as;
• It helps in signifying the important relationships between both dependent and independent variables.
• Signifies the power of the impact of the number of variables on a dependent variable.
Types of Regression Analysis
There are different kinds of regression analysis motivated by three types of measures that are; Number of Independent Variable, Shape of The Regression Line and Type of Dependent Variable.

MOST COMMON USED REGRESSIONS
Linear Regression and Logistic Regression are two main types of regressions that are used widely.

1. LINEAR REGRESSION
Linear regression is the most popular technique of modeling and it is the first topic that comes while learning about predictive modeling. Dependent variables and independent variables are continuous whereas the character of the regression line is linear. The key difference between simple and multiple linear regressions is that simple regression linear regression has only one independent variable whereas multiple linear regressions have more than one independent variable.

2. LOGISTIC REGRESSION
Logistic regression is used for finding the probability of both the success and failure events. When the dependent variable is dual logistic regression is a must use. While working with the binominal distribution the user must choose the most suitable function of connection for this type of distribution. Logistic regression has wide usage in the classification problems and it does not demand the linear relationship between the dependent and the independent variables.

Other types of regression analysis are Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, and ElasticNet Regression.

• POLYNOMIAL REGRESSION- When the power of an independent variable is more than one in a regression equation then it is the ‘Polynomial Regression’.

• STEPWISE REGRESSION- While dealing with numerous independent variables ‘Stepwise Regression’ is used where an independent variable is selected with the help of the automatic process.

• RIDGE REGRESSION- When a data suffers from the multicollinearity then the ridge regression technique is used.

• LASSO REGRESSION- Lasso regression is almost similar to the ridge regression used for correcting the complete regression coefficient size.

• ELASTIC NET REGRESSION- ElasticNet regression is a combination of both Lasso and Ridge regression techniques. It is used in the case of multiple features that are interrelated.

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