# What is Regression?

Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. It can be utilized to assess the strength of the relationship between variables and for modeling the future relationship between them.

This analysis includes several variations, such as linear, multiple linear, and nonlinear. The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship.

It can help finance and investment professionals as well as professionals in other businesses. It also helps predict sales for a company based on weather, previous sales, GDP growth, or other types of conditions. The capital asset pricing model is an often-used this model in finance for pricing assets and discovering costs of capital. Linear Regression

It is a linear approach to modeling the relationship between the scalar components and one or more independent variables. If It has one independent variable, then it is known as a simple linear regression. If it has more than one independent variable, then it is known as multiple linear regression. It only focuses on the conditional probability distribution of the given values rather than the joint probability distribution.

### Difference Between Correlation and Regression

 Basis Correlation Regression Meaning A statistical measure that defines co-relationship or association of two variables. Describes how an independent variable is associated with the dependent variable. Dependent and Independent variables No difference Both variables are different. Usage To describe a linear relationship between two variables. To fit the best line and estimate one variable based on another variable. Objective To find a value expressing the relationship between variables. To estimate values of a random variable based on the values of a fixed variable.