import wooldridge as woo

import numpy as np

import pandas as pd

import statsmodels.api as sm

import statsmodels.formula.api as smf

lawsch85 = woo.dataWoo('lawsch85')

# define cut points for the rank:

cutpts = [0, 10, 25, 40, 60, 100, 175]

# create categorical variable containing ranges for the rank:

lawsch85['rc'] = pd.cut(lawsch85['rank'], bins=cutpts,

labels=['(0,10]', '(10,25]', '(25,40]',

'(40,60]', '(60,100]', '(100,175]'])

# display frequencies:

freq = pd.crosstab(lawsch85['rc'], columns='count')

print(f'freq: \n{freq}\n')

# run regression:

reg = smf.ols(formula='np.log(salary) ~ C(rc, Treatment("(100,175]")) +'

'LSAT + GPA + np.log(libvol) + np.log(cost)',

data=lawsch85)

results = reg.fit()

# print regression table:

table_reg = pd.DataFrame({'b': round(results.params, 4),

'se': round(results.bse, 4),

't': round(results.tvalues, 4),

'pval': round(results.pvalues, 4)})

print(f'table_reg: \n{table_reg}\n')

# ANOVA table:

table_anova = sm.stats.anova_lm(results, typ=2)

print(f'table_anova: \n{table_anova}\n')