Compute linear regression standardized coefficient (beta) with Python

Question

I would like to compute the beta or standardized coefficient of a linear regression model using standard tools in Python (numpy, pandas, scipy.stats, etc.).

A friend of mine told me that this is done in R with the following command:

lm(scale(y) ~ scale(x))

Currently, I am computing it in Python like this:

from scipy.stats import linregress
from scipy.stats.mstats import zscore

(beta_coeff, intercept, rvalue, pvalue, stderr) = linregress(zscore(x), zscore(y))
print('The Beta Coeff is: %f' % beta_coeff)

Is there a more straightforward function to compute this figure in Python?

In case you have more than one independent variable. Please see this post — steven
– steven, Commented Feb 12, 2019 at 14:17

Eoin · Accepted Answer · 2015-11-25 11:25:06Z

6

Python is a general purpose language, but R was designed specifically for statistics. It's almost always going to take a few more lines of code to achieve the same (statistical) goal in python, purely because R comes ready to fit regression models (using lm) as soon as you boot it up.

The short answer to your question is No - your python code is already pretty straightforward.

That said, I think a closer equivalent to your R code would be

import statsmodels.api as sm
from scipy.stats.mstats import zscore

print sm.OLS(zscore(y), zscore(x)).fit().summary()

answered Nov 25, 2015 at 11:25

Eoin

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