What does a VIF value of 1 mean?

What does a VIF value of 1 mean?

A value of 1 means that the predictor is not correlated with other variables. If one variable has a high VIF it means that other variables must also have high VIFs. In the simplest case, two variables will be highly correlated, and each will have the same high VIF.

How do you import the variance inflation factor?

here code using dataframe python:

1. To create data. import numpy as np. import scipy as sp. a = [1, 1, 2, 3, 4] b = [2, 2, 3, 2, 1]
2. To create dataframe. import pandas as pd. data = pd.DataFrame() data[“a”] = a. data[“b”] = b.
3. Calculate VIF. cc = np.corrcoef(data, rowvar=False) VIF = np.linalg.inv(cc) VIF.diagonal()
4. Result.

How is the variance of a predictor inflated?

As you can see, three of the variance inflation factors — 8.42, 5.33, and 4.41 — are fairly large. The VIF for the predictor Weight, for example, tells us that the variance of the estimated coefficient of Weight is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.

When do you use a variance inflation factor?

Recall that we learned previously that the standard errors — and hence the variances — of the estimated coefficients are inflated when multicollinearity exists. A variance inflation factor exists for each of the predictors in a multiple regression model.

How is the variance of the weight coefficient inflated?

Therefore, the variance inflation factor for the estimated coefficient Weight is by definition: Again, this variance inflation factor tells us that the variance of the weight coefficient is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.

How are variance inflation factors used to detect multicollinearity?

As you can see, three of the variance inflation factors —8.42, 5.33, and 4.41 —are fairly large. The VIF for the predictor Weight, for example, tells us that the variance of the estimated coefficient of Weight is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.