# Regression

Regression is a statistical technique that determines the strength of the relationship between dependent variable and a series of other changing (independent) variables. It estimates the relationship among variables and in simple regression, it examines the relationship between one independent and one dependent variable. Regression statistics is used to predict the dependent variable, when the independent variable is known.

A regression equation is of the form Y = a + bx + c, where,

Y: Variable you wish to predict. (Dependent variable)

x: Variable you're using to predict 'Y'. (Independent variable)

a: Y-intercept of the line.

b: Slope.

c: Value called the Regression residual.Regressions are of several types namely linear regression, logistic regression, stepwise regression, robust regression, non linear regression, non parametric regression, multiple regression. Regression is used on an intuitive level everyday. In business, a well dressed man is thought to be financially successful. Quantitative regression adds precision by developing a mathematical formula, that can be used for predictive purposes. The **purpose of regression** is to find a formula, that fits the relationship between the two variables.