class_docstr: Diagnostic plots to identify potential problems in a linear regression fit.
  Mainly,
      a. non-linearity of data
      b. Correlation of error terms
      c. non-constant variance 
      d. outliers
      e. high-leverage points
      f. collinearity
  
  Author:
      Prajwal Kafle (p33ajkafle@gmail.com, where 3 = r)
      Does not come with any sort of warranty. 
      Please test the code one your end before using.
class_docstr_tok: ["Diagnostic","plots","to","identify","potential","problems","in","a","linear","regression","fit",".","Mainly",",","a.","non-linearity","of","data","b",".","Correlation","of","error","terms","c.","non-constant","variance","d.","outliers","e.","high-leverage","points","f.","collinearity","Author",":","Prajwal","Kafle","(","p33ajkafle","@","gmail.com",",","where","3","=","r",")","Does","not","come","with","any","sort","of","warranty",".","Please","test","the","code","one","your","end","before","using","."]
docstr: VIF table
  
  VIF, the variance inflation factor, is a measure of multicollinearity.
  VIF > 5 for a variable indicates that it is highly collinear with the 
  other input variables.
docstr_tok: ["VIF","table","VIF",",","the","variance","inflation","factor",",","is","a","measure","of","multicollinearity",".","VIF",">","5","for","a","variable","indicates","that","it","is","highly","collinear","with","the","other","input","variables","."]
code: def vif_table(self):
      """
      VIF table
  
      VIF, the variance inflation factor, is a measure of multicollinearity.
      VIF > 5 for a variable indicates that it is highly collinear with the
      other input variables.
      """
      vif_df = pd.DataFrame()
      vif_df["Features"] = self.xvar_names
      vif_df["VIF Factor"] = [
          variance_inflation_factor(self.xvar, i)
          for i in range(self.xvar.shape[1])
      ]
  
      print(vif_df.sort_values("VIF Factor").round(2))
  
file_path: statsmodels/examples/python/linear_regression_diagnostics_plots.py
imports: import statsmodels
  import statsmodels.formula.api
  import pandas
  import numpy
  import seaborn
  from statsmodels.tools.tools import maybe_unwrap_results
  from statsmodels.graphics.gofplots import ProbPlot
  from statsmodels.stats.outliers_influence import variance_inflation_factor
  import matplotlib.pyplot
  from typing import Type
label_desc: Use image node_id 7 for calling the Linear_Reg_Diagnostic obj's underlying member method code with example usage: obj.vif_table() without return types