import wooldridge as woo

import pandas as pd

import numpy as np

import statsmodels.formula.api as smf

# define a function for the standardization:

def scale(x):

x_mean = np.mean(x)

x_var = np.var(x, ddof=1)

x_scaled = (x - x_mean) / np.sqrt(x_var)

return x_scaled

# standardize and estimate:

hprice2 = woo.dataWoo('hprice2')

hprice2['price_sc'] = scale(hprice2['price'])

hprice2['nox_sc'] = scale(hprice2['nox'])

hprice2['crime_sc'] = scale(hprice2['crime'])

hprice2['rooms_sc'] = scale(hprice2['rooms'])

hprice2['dist_sc'] = scale(hprice2['dist'])

hprice2['stratio_sc'] = scale(hprice2['stratio'])

reg = smf.ols(

formula='price_sc ~ 0 + nox_sc + crime_sc + rooms_sc + dist_sc + stratio_sc',

data=hprice2)

results = reg.fit()

# print regression table:

table = 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: \n{table}\n')