Variance inflation factor logistic regression

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Variance inflation factor logistic regression

Variance inflation factor logistic regression

A lot of times, we get overwhelmed and just go straight to p-values, ignoring a lot of valuable information in the process. This post will give you an introduction to one of the other statistics Minitab displays for you, the VIF, or Variance Inflation Factor. To start, let's look at what the VIF tells us.

Variance inflation factor logistic regression

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Variance inflation factor Multicollinearity References Menard, S. (1995). Applied Logistic Regression Analysis: Sage University Series on Quantitative Applications in ... One way to measure multicollinearity is the variance inflation factor (VIF), which assesses how much the variance of an estimated regression coefficient increases if your predictors are correlated.... from statsmodels.regression.linear_model import OLS from statsmodels.tools.tools import add_constant def variance_inflation_factors(exog_df): ''' Parameters ----- exog_df : dataframe, (nobs, k_vars) design matrix with all explanatory variables, as for example used in regression. Investigators that seek to employ regression analysis usually encounter the problem of multicollinearity with dependency on two or more explanatory variables. Multicollinearity is associated with unstable estimated coefficients and it results in high variances of the least squares estimators in linear regression models (LRMs).

Variance inflation factor logistic regression