title: logit regression and singular Matrix error in Python
body: <p>am trying to run logit regression for german credit data (www4.stat.ncsu.edu/~boos/var.select/german.credit.html). To test the code, I have used only numerical variables and tried regressing it with the result using the following code.</p>
  
  <pre><code>import pandas as pd
  import statsmodels.api as sm
  import pylab as pl
  import numpy as np
  
  df = pd.read_csv("germandata.txt",delimiter=' ')
  df.columns = ["chk_acc","duration","history","purpose","amount","savings_acc","employ_since","install_rate","pers_status","debtors","residence_since","property","age","other_plans","housing","existing_credit","job","no_people_liab","telephone","foreign_worker","admit"]
  
  #pls note that I am only retaining numeric variables
  cols_to_keep = ['admit','duration', 'amount', 'install_rate','residence_since','age','existing_credit','no_people_liab']
  
  # rank of cols_to_keep is 8
  print np.linalg.matrix_rank(df[cols_to_keep].values)
  data = df[cols_to_keep]
  
  data['intercept'] = 1.0
  
  train_cols = data.columns[1:]
  
  #to check the rank of train_cols, which in this case is 8
  print np.linalg.matrix_rank(data[train_cols].values)
  
  #fit logit model
  logit = sm.Logit(data['admit'], data[train_cols])
  result = logit.fit()
  </code></pre>
  
  <hr>
  
  <p>All the 8.0 columns seem independent when I check the data. Inspite of this I am getting Singular Matrix Error. Can you please help?</p>
  
  <p>Thanks</p>