Multiple Linear Regression

What is Multiple Linear Regression?

Multiple linear regression is used to estimate the relationship between two or more independent variables(X) and one dependent variable (Y):

• X is regarded as the predictor, explanatory, or independent variable.
• Y is regarded as the response, outcome, or dependent variable. As a result, the formula for multiple linear regression is expressed as the formula below.
  

$ y = b_{0} + b_{1} \times x_{1} + b_{2} \times x_{2}+ \ldots + b_{n} \times x_{n} $

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).
  
  

Example

Code

---

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

Expected values

---

[[103015.2]
 [132582.28]
 [132447.74]
 [ 71976.1]
 [178537.48]
 [116161.24]
 [ 67851.69]
 [ 98791.73]
 [113969.44]
 [167921.07]]

Predicted values

---

[[103282.38]
 [144259.4 ]
 [146121.95]
 [77798.83]
 [191050.39]
 [105008.31]
 [81229.06]
 [97483.56]
 [110352.25]
 [166187.94]]

Implementation

• GitHub