Multiple linear Regression

Multiple Linear Regression is also known as Multiple Regression. It is an extension of simple Linear Regression.

In Linear Regression, a single independent variable is used to predict the value of a dependent variable.

In Multiple Regression, two or more independent variables are used to predict the value of a dependent variable.

Most real world phenomena are multi-factorial in nature, which means there is more than one factor that impacts on, or changes in the dependent variable. In order to predict the dependent variable as accurately as possible, it is usually necessary to include multiple independent variables in the model.

Multiple linear regression allows us to test how well we can predict a dependent variable on the basis of multiple independent variables.

For example,

Suppose we want to predict the temperature of an area, and we have information about latitude, altitude, ocean currents, humidity, cloud cover, etc then we can use multiple regression to predict the temperature of an area accurately.

The multiple regression equation is as follows:

Y = a + b1X1 + b2X2 + b3X3 + ..... + bnXn + e


Y = the variable we are trying to predict (dependent variable)

X = the variable we are using to predict (independent variable)

a = the intercept

b = the slope (Coefficient of X1)

e = error

I hope this example helps in understanding Multiple Regression concept.

Thanks for reading!

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