# What is multiple_linear_regression
* Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.
* Multiple regression is an extension of linear (OLS) regression that uses just one explanatory variable.
* MLR is used extensively in econometrics and financial inference.
* Regression models are used to describe relationships between variables by fitting a line to the observed data. Regression allows you to estimate how the dependent variable changes as the independent variable(s) change.
* Multiple linear regression is used to estimate the relationship between two or more independent variables and one dependent variable. You can use multiple linear regression when you want to know:
* How strong the relationship is between two or more independent variables and one dependent variable (e.g. how rainfall, temperature, and amount of fertilizer added affect crop growth).
* The value of the dependent variable at a certain value of the independent variables (e.g. the expected yield of a crop at certain levels of rainfall, temperature, and fertilizer addition).
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* Linear regression is one to one but Multiple regression is many to one relationship
* Adding more independent variables to the model doesn't mean that regression will give better in fact it will be worse. This can be overfitting
* Adding more independent variables will lead to multicollinearity.
* The idea of all independent variables to be co-related with the dependent variables,s not with each other.
Python code is available on GITHUB
Multiple Examples of Multiple Linear regression are available on below link:
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