Types of Regression Analysis

Linear Regression

Linear regression is a type of model where the relationship between an independent variable and a dependent variable is assumed to be linear. The estimate of variable “y” is obtained from an equation, y’- y_bar = byx(x-x_bar)……(1) and estimate of variable “x” is obtained through the equation x’-x_bar = bxy(y-y_bar)…..(2). The graphical representation of linear equations on (1) & (2) is known as Regression lines. These lines are obtained through the Method of Least Squares. 

There are two kinds of Linear Regression Model:-

  • Simple Linear Regression: A linear regression model with one independent and one dependent variable.
  • Multiple Linear Regression: A linear regression model with more than one independent variable and one dependent variable.

Assumptions of Linear Regression

  • Sample size : n = 20 (cases per independent variable)
  • Heteroscedasticity is absent —
  • Linear Relationships exist between the variables.
  • Independent Sample observations.
  • No multicollinearity & auto-correlation
  • Independent Sample observations.

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