Reproduce Table 2.2 of Applied Logistic Regression (3rd Edition) with Statsmodels

glow500.py

import pandas as pd
import statsmodels.api as sm

# glow500.xls at https://www.umass.edu/statdata/statdata/data/glow/index.html
xls_file = pd.ExcelFile('glow500.xls')
df = xls_file.parse(header=0)

rate_dummies = pd.get_dummies(df['RATERISK'])
rate_dummies.columns = ['RATERISK1', 'RATERISK2', 'RATERISK3']

y = df['FRACTURE']

X = df.drop(['SUB_ID', 'SITE_ID', 'PHY_ID', 'RATERISK', 'FRACSCORE', 'FRACTURE'], axis=1)
X = pd.concat([X, rate_dummies], axis=1)
X = X.drop('RATERISK1', axis=1)

X = X.drop(['HEIGHT', 'BMI', 'MOMFRAC', 'ARMASSIST', 'SMOKE'], axis=1)
X = sm.add_constant(X)

result = sm.Logit(y, X).fit(disp=0)
print(result.summary())