This tag is for questions on (linear or nonlinear) regression, which is a way of describing how one variable, the outcome, is numerically related to predictor variables. The dependent variable is also referred to as $~Y~$, dependent or response and is plotted on the vertical axis (ordinate) of a graph.

Regression is a statistical measurement used in finance, investing and other disciplines that attempts to determine the strength of the relationship between one dependent variable (usually denoted by $~Y~$) and a series of other changing variables (known as independent variables).

Types of Regression –

  • Linear regression
  • Logistic regression
  • Polynomial regression
  • Stepwise regression
  • Stepwise regression
  • Ridge regression
  • Lasso regression
  • ElasticNet regression

The two basic types of regression are linear regression and multiple linear regression.

The general form of each type of regression is:

  • Linear regression: $~Y = a + b~X + u~$
  • Multiple regression: $~Y = a + b_1~X_1 + b_2~X_2 + b_3~X_3 + ... + b_t~X_t + u~$


  • $Y =~$ the variable that you are trying to predict (dependent variable).
  • $X =~$ the variable that you are using to predict Y (independent variable).
  • $a =~$ the intercept.
  • $b =~$ the slope.
  • $u =~$ the regression residual.

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.


This tag often goes along with the tag.