Import necessary libraries.

  Load the sample data set.

  Assign the independent(X)and dependent(y)variables.

  Pass the variables to the model.

  In sklearn library,dont have method like summary()

#import related libraries

import pandas as pd

#import stats model

import statsmodels.api as sm

#load sample data

data={‘salary’:[100,200,300,

400,500,400,300,200,100,50],

‘age’:[25,26,25,23,30,29,23,23,25,25],

‘rating’:[4,3.24,2.5,2.25,2,2.25,2.5,

2.75,3.2,4.2], ‘bonus’:[2500,1200,900

,3000,1800,1400,850,250,750,1000]}

#create data frame

df=pd.DataFrame(data)

#assign independent variable

X = df.iloc[:, :1].values

#assign dependent variable

y = df.iloc[:, 3].values

#build regression model

model=sm.OLS(y,X).fit()

#take summary of model

result=model.summary()

#print the result

print(result)