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Regressions (i2tutorials)

What is the difference between Simple Linear Regression and Multi Linear Regression and Polynomial Regression?

Simple Linear Regression

Simple Linear Regression establishes the relationship between two variables using a straight line. It attempts to draw a line that comes closest to the data by finding the slope and intercept which define the line and minimize regression errors. Simple linear regression has only one x and one y variable.

Multi Linear Regression

Multiple Linear regressions are based on the assumption that there is a linear relationship between both the dependent and independent variables or Predictor variable and Target variable. It also assumes that there is no major correlation between the independent variables. Multi Linear regressions can be linear and nonlinear. It has one y and two or more x variables or one dependent variable and two or more independent variables.

Polynomial Regression

Y=θo + θ₁+ θ₂X² + … + θₘXᵐ + residual error

Polynomial Regression is a one of the types of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E (y |x).

Polynomial Regression provides the best approximation of the relationship between the dependent and independent variable.

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