# REGRESSION MEANING | LINEAR REGRESSION | MULTIPLE REGRESSION | REGRESSION ANALYSIS | NUMERICAL AND STATISTICAL METHODS

REGRESSION

## WHAT IS REGRESSION ?

Regression is defined as a method of estimating the value of one variable when that of the other is known and the variables are correlated.

Regression analysis is used to predict or estimate one variable in terms of the other variable.

It is a highly valuable tool for prediction purpose in economics and business.

It is useful in statistical estimation of demand curves, supply curves, production function, cost function, consumption function, etc.

## TYPES OF REGRESSION

Regression is classified into two types :

1. Simple and multiple regressions

2. Linear and nonlinear regressions

1) Simple and Multiple Regressions:

Depending upon the study of the number of variables, regression may be simple or multiple.

Simple Regression :-

The regression analysis for studying only two variables at a time is known as simple regression.

Multiple Regression :-

The regression analysis for studying more than variables at a time is known as multiple regression.

2) Linear and Nonlinear Regressions :

Depending upon the regression curve, regression may be linear or nonlinear.

Linear Regression :-

If the regression curve is a straight line, the regression is said to be linear.

Nonlinear Regression :-

If the regression curve is not a straight line i.e., not a first-degree equation in the variables x and y, the regression is said to be nonlinear or curvilinear.

## NOTES

REGRESSION – FORMULAS AND EXAMPLES | HANDWRITTEN NOTES