differences between simple linear regression and multiple linear regre

1.	differences between simple linear regression and multiple linear regression
Answer» Linear regression is one of the most common techniques of regression analysis. It is also called a simple linear regression. It establishes the relationship between two variables using a straight line. Linear regression attempts to draw a line that comes closest to the data by finding the slope and intercept that define the line and minimize regression errors. Multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables. It is rare that a dependent variable is explained by only one variable. In this case, an analyst uses multiple regression, which attempts to explain dependent variable using more than one independent variable. Multiple regressions can be linear and nonlinear.

differences between simple linear regression and multiple linear regression

Answer»

Linear regression is one of the most common techniques of regression analysis. It is also called a simple linear regression. It establishes the relationship between two variables using a straight line. Linear regression attempts to draw a line that comes closest to the data by finding the slope and intercept that define the line and minimize regression errors.

Multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables.

It is rare that a dependent variable is explained by only one variable. In this case, an analyst uses multiple regression, which attempts to explain dependent variable using more than one independent variable. Multiple regressions can be linear and nonlinear.

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