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: Helper function for plotting Cook's distance curves
docstr_tok: ["Helper","function","for","plotting","Cook","'s","distance","curves"]
code: def __cooks_dist_line(self, factor):
      """
      Helper function for plotting Cook's distance curves
      """
      p = self.nparams
      formula = lambda x: np.sqrt((factor * p * (1 - x)) / x)
      x = np.linspace(0.001, max(self.leverage), 50)
      y = formula(x)
      return x, y
  
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 8 for calling the Linear_Reg_Diagnostic obj's underlying member method code with example usage: obj.__cooks_dist_line(factor) and returns: x,  y