numpy - Multiple regression with OLS in python -

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i have code multiple ols-regression newey-west procedure.

import pandas pd import numpy np import statsmodels.api sm  df = pd.dataframe({'a':[1,3,5,7,4,5,6,4,7,8,9],                    'b':[3,5,6,2,4,6,7,8,7,8,9]})  results = sm.ols(df.a, sm.add_constant(df.b)).fit() new = results.get_robustcov_results(cov_type='hac',maxlags=1) print new.summary()

it works, how should change code, if have more variables like....

df = pd.dataframe({'a':[1,3,5,7,4,5,6,4,7,8,9],                    'b':[3,5,6,2,4,6,7,8,7,8,9],                    'c':[3,5,6,2,4,8,7,8,9,9,9],                    'd':[3,5,6,2,5,8,8,9,8,10,9]})

... , wanted analyse influence on variable a, analysis of variable b in original code?

how should code-line results = sm.ols(df.a, sm.add_constant(df.b)).fit() looks like?

thanks!!

you can supply multiple variables this:

results = sm.ols(df.a, sm.add_constant(df[list('bcd')])).fit()

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