In regression analysis, multicollinearity occurs when two or more predictor variables are highly correlated with each other, such that they do not provide unique or independent information in the regression model.

If the degree of correlation is high enough between predictor variables, it can cause problems when fitting and interpreting the regression model.

The most straightforward way to detect multicollinearity in a regression model is by calculating a metric known as the variance inflation factor, often abbreviated **VIF**.

VIF measures the strength of correlation between predictor variables in a model. It takes on a value between 1 and positive infinity.

We use the following rules of thumb for interpreting VIF values:

**VIF = 1:**There is no correlation between a given predictor variable and any other predictor variables in the model.**VIF between 1 and 5:**There is moderate correlation between a given predictor variable and other predictor variables in the model.**VIF > 5**: There is severe correlation between a given predictor variable and other predictor variables in the model.

The following example shows how to detect multicollinearity in a regression model in Python by calculating VIF values for each predictor variable in the model.

**Example: Testing for Multicollinearity in Python**

Suppose we have the following pandas DataFrame that contains information about various basketball players:

import pandas as pd #create DataFrame df = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86], 'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19], 'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5], 'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]}) #view DataFrame print(df) rating points assists rebounds 0 90 25 5 11 1 85 20 7 8 2 82 14 7 10 3 88 16 8 6 4 94 27 5 6 5 90 20 7 9 6 76 12 6 6 7 75 15 9 10 8 87 14 9 10 9 86 19 5 7

Suppose we would like to fit a multiple linear regression model using **rating** as the response variable and **points**, **assists**, and **rebounds** as the predictor variables.

To calculate the **VIF** for each predictor variable in the model, we can use the variance_inflation_factor() function from the **statsmodels** library:

from patsy import dmatrices from statsmodels.stats.outliers_influence import variance_inflation_factor #find design matrix for regression model using 'rating' as response variable y, X = dmatrices('rating ~ points+assists+rebounds', data=df, return_type='dataframe') #create DataFrame to hold VIF values vif_df = pd.DataFrame() vif_df['variable'] = X.columns #calculate VIF for each predictor variable vif_df['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] #view VIF for each predictor variable print(vif_df) VIF variable 0 101.258171 Intercept 1 1.763977 points 2 1.959104 assists 3 1.175030 rebounds

We can see the VIF values for each of the predictor variables:

**points:**1.76**assists:**1.96**rebounds:**1.18

**Note: **Ignore the VIF for the “Intercept” in the model since this value is irrelevant.

Since each of the VIF values for the predictor variables in the model are close to 1, multicollinearity is not a problem in the model.

**Additional Resources**

The following tutorials explain how to perform other common tasks in Python:

How to Perform Simple Linear Regression in Python

How to Perform Multiple Linear Regression in Python

How to Create a Residual Plot in Python