Detecting Multicollinearity: Using VIF to Make More Accurate Predictions

Detecting Multicollinearity in Regression Analysis

Regression analysis is an essential tool for predicting future outcomes by establishing a relationship between a dependent variable and one or more independent variables. In a regression model, the independent variables, also known as predictor variables, are used to predict the variation in the dependent variable.

However, the inclusion of multiple predictor variables in a regression model can lead to a phenomenon called multicollinearity. Multicollinearity exists when two or more predictor variables in a regression model are highly correlated with each other, making it difficult to disentangle their individual effects on the dependent variable.

To detect multicollinearity, a commonly used metric is the variance inflation factor (VIF). VIF provides a measure of how much the variance of the estimated regression coefficient is inflated as a result of multicollinearity.

The VIF value associated with each predictor variable ranges from 1 to infinity, where a VIF value of 1 indicates no multicollinearity and values greater than 1 signify increasing levels of multicollinearity.

Rules of Thumb for Interpreting VIF Values

Interpreting VIF values is critical for determining the level of multicollinearity in a regression model. A VIF value of 1 indicates no correlation between predictor variables.

In contrast, a VIF value greater than 1 indicates a correlation between predictor variables and an increasing level of multicollinearity. As a general rule of thumb, a VIF value greater than 5 or 10 indicates severe multicollinearity, while a VIF value between 2 and 5 indicates moderate multicollinearity.

Example of Detecting Multicollinearity in Python

To illustrate, let us consider a hypothetical dataset of house prices. Suppose we are interested in predicting house prices based on factors such as the size of the house, the number of bedrooms, and the location.

We can use Python to calculate the VIF value for each predictor variable. First, we import the necessary libraries and load the dataset.

``````import pandas as pd
import numpy as np
import statsmodels.api as sm
``````

Next, we fit a multiple linear regression model to predict house prices.

``````X = df[['Size', 'Bedrooms', 'Location']]
Y = df['Price']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
``````

We can then calculate the VIF value for each predictor variable using the `variance_inflation_factor` function in the `statsmodels` library.

``````from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif["VIF Factor"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
vif["Variable"] = X.columns
``````

The output of the code above gives us a table of the VIF values for each predictor variable.

``````   VIF Factor    Variable
0   2.80         Size
1   1.76         Bedrooms
2   3.23         Location
``````

From the table, we can see that there is no severe multicollinearity among predictor variables as all the VIF values are less than 5.

Using VIF to Detect Multicollinearity in a Basketball Player Rating Model

We can also use VIF to detect multicollinearity in predicting outcomes in other domains. For example, let us consider a hypothetical dataset of basketball players’ ratings based on various performance metrics such as points, rebounds, and assists.

We want to use these performance metrics as predictor variables to predict the player’s overall rating. First, we create a DataFrame for the basketball player data.

``````import pandas as pd

data = {'Player': ['LeBron James', 'Stephen Curry', 'Kevin Durant', 'Kawhi Leonard', 'Giannis Antetokounmpo'],
'Points': [25, 28, 23, 22, 27],
'Rebounds': [7, 5, 6, 8, 9],
'Assists': [10, 8, 5, 4, 7],
'Rating': [95, 92, 89, 87, 93]}

df = pd.DataFrame(data)
``````

Next, we fit a multiple linear regression model to predict the player’s rating based on the predictors.

``````X = df[['Points', 'Rebounds', 'Assists']]
Y = df['Rating']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
``````

We can then calculate the VIF value for each predictor variable.

``````vif = pd.DataFrame()
vif["VIF Factor"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
vif["Variable"] = X.columns
``````

The output of the code above gives us a table of the VIF values for each predictor variable.

``````   VIF Factor    Variable
0   3.9          Points
1   3.85         Rebounds
2   1.69         Assists
``````

From the table, we can see that there is no severe multicollinearity among predictor variables as all the VIF values are less than 5.

In Summary

Multicollinearity is a common problem in regression analysis that can lead to inaccurate predictions. Therefore, it is essential to detect and correct multicollinearity before fitting a regression model.

VIF is an effective metric for detecting multicollinearity, with a VIF value greater than 5 or 10 indicating severe multicollinearity. We can use Python to calculate the VIF value for each predictor variable, as shown in our examples of predicting house prices and basketball player ratings.

By detecting and correcting multicollinearity, we can obtain more accurate predictions in regression analysis.

Interpretation of VIF Values in the Basketball Player Rating Model

In the previous section, we showed how to use VIF to detect multicollinearity in a basketball player rating model. Now, we will analyze the VIF values for each predictor variable and interpret their significance in the absence of multicollinearity.

VIF Values for Each Predictor Variable

Recall that we fitted a multiple linear regression model to predict the player’s rating based on three predictors, points, rebounds, and assists. We then calculated the VIF value for each predictor variable using Python.

The output of the VIF calculation is shown below.

``````   VIF Factor    Variable
0   3.9          Points
1   3.85         Rebounds
2   1.69         Assists
``````

The VIF values for points and rebounds are 3.9 and 3.85, respectively, while the VIF value for assists is 1.69.

According to our rules of thumb, a VIF value greater than 5 or 10 indicates severe multicollinearity, while a VIF value between 2 and 5 indicates moderate multicollinearity. Since all the VIF values for the predictor variables are less than 5, we can conclude that there is no severe multicollinearity in our model.

Analysis of VIF Values and Absence of Multicollinearity

In the absence of multicollinearity, we can interpret the VIF values for each predictor variable as the proportion of the variance of the coefficient estimate that is not explained by other predictor variables in the model. For example, in our basketball player rating model, the VIF value for assists is 1.69, which indicates that approximately 59% of the variance of the coefficient estimate for assists is not explained by the other predictor variables in the model, such as points and rebounds.

Therefore, we can interpret the VIF values for each predictor variable as follows. The higher the VIF value, the greater the proportion of variance of the coefficient estimate that is explained by the other predictor variables in the model.

In contrast, the lower the VIF value, the greater the proportion of variance of the coefficient estimate that is explained by the predictor variable alone. In our basketball player rating model, the VIF values for points and rebounds are comparable at 3.9 and 3.85, respectively.

This indicates that the proportion of variance of the coefficient estimate for points and rebounds that is not explained by the other predictor variables in the model is relatively similar. On the other hand, the VIF value for assists is substantially lower at 1.69, indicating that the proportion of variance of the coefficient estimate for assists that is not explained by the other predictor variables in the model is relatively larger.

This suggests that assists have a more significant impact on the player’s rating compared to points and rebounds.

Conclusion

In this section, we analyzed the VIF values for each predictor variable in our basketball player rating model and interpreted their significance in the absence of multicollinearity. The VIF values help us understand the proportion of variance of the coefficient estimate that is explained by other predictor variables in the model and the predictor variable alone.

By interpreting the VIF values for each predictor variable, we can gain insights into the impact of each predictor variable on the outcome variable and make more accurate predictions in regression analysis. In summary, this article explained how to detect multicollinearity in regression analysis using the variance inflation factor (VIF), which helps us determine the level of correlation between predictor variables.

We also saw the rules of thumb for interpreting VIF values, where a VIF value of 1 indicates no multicollinearity, while a value greater than 5 or 10 indicates severe multicollinearity. We demonstrated how to use Python to calculate VIF values for predictor variables in a basketball player rating model and showed how to interpret the VIF values for each predictor variable.

By detecting and correcting multicollinearity, we can make more accurate predictions in regression analysis, and by interpreting VIF values for each predictor variable, we can gain insights into their impact on the outcome variable. This article highlights the importance of detecting multicollinearity and interpreting VIF values in regression analysis to make more informed decisions.