What is the major difference between simple regression and multiple regression?
Answers
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.
The difference between the two is the number of independent variables.
Explanation:
In simple linear regression a single independent variable is used to predict the value of a dependent variable.
In multiple linear regression two or more independent variables are used to predict the value of a dependent variable.
The difference between the two is the number of independent variables.
In both cases there is only a single dependent variable.
#Learn more :
What is regression? Difference between regression and correlation
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