Linear Regression 11 | Python for MLR Model Diagnosis — Part 1&2

Linear Regression 11 | Python for MLR Model Diagnosis — Part 1&2

1. Import packages

from math import *

import pandas as pd
import numpy as np
import scipy
from scipy import stats
from scipy.stats import kstest
from scipy.stats import boxcox
import scipy.linalg as linalg

from sklearn import linear_model
import statsmodels.api as sm
import statsmodels.formula.api as smf

from statsmodels.stats.diagnostic import het_breuschpagan

from patsy import dmatrices
from statsmodels.stats.outliers_influence import variance_inflation_factor

import matplotlib.pyplot as plt
import seaborn as sns
%config InlineBackend.figure_format='retina'

2. Build the model

model =smf.ols('y ~ x1 + x2 + x3', data=df).fit()

3. Check Multicollinearity

4. Check Influential Points

5. Check Heteroscedasticity

6. Check Normality