Weekday As Dummy / Factor Variable In A Linear Regression Model Using Statsmodels

The question: How can I add a dummy / factor variable to a model using sm.OLS()? The details: Data sample structure: Date A B weekday 2013-05-04 25.03 88.51 Saturday 20

Solution 1:

You can use pandas categorical to create the dummy variables, or, simpler, use the formula interface where patsy transforms all non-numeric columns to the dummy variables, or other factor encoding.

Using the formula interface in this case (same as lower case ols in statsmodels.formula.api) shows the result below. Patsy sorts levels of the categorical variable alphabetically. 'Friday' is missing in the list of variables and has been selected as reference category.

>>> res = sm.OLS.from_formula('A ~ B + weekday', df).fit()
>>> print(res.summary())
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      A   R-squared:                       0.301
Model:                            OLS   Adj. R-squared:                  0.029
Method:                 Least Squares   F-statistic:                     1.105
Date:                Thu, 03 May 2018   Prob (F-statistic):              0.401
Time:                        15:26:02   Log-Likelihood:                -97.898
No. Observations:                  26   AIC:                             211.8
Df Residuals:                      18   BIC:                             221.9
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
========================================================================================
                           coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------------
Intercept               -1.4717     19.343     -0.076      0.940     -42.110      39.167
weekday[T.Monday]        2.5837      9.857      0.262      0.796     -18.124      23.291
weekday[T.Saturday]     -6.5889      9.599     -0.686      0.501     -26.755      13.577
weekday[T.Sunday]        9.2287      9.616      0.960      0.350     -10.975      29.432
weekday[T.Thursday]     -1.7610     10.321     -0.171      0.866     -23.445      19.923
weekday[T.Tuesday]       2.6507      9.664      0.274      0.787     -17.652      22.953
weekday[T.Wendesday]    -6.9320      9.911     -0.699      0.493     -27.754      13.890
B                        0.4047      0.258      1.566      0.135      -0.138       0.948
==============================================================================
Omnibus:                        1.039   Durbin-Watson:                   2.313
Prob(Omnibus):                  0.595   Jarque-Bera (JB):                0.532
Skew:                          -0.350   Prob(JB):                        0.766
Kurtosis:                       3.007   Cond. No.                         638.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

See patsy documentation for options for categorical encodings http://patsy.readthedocs.io/en/latest/categorical-coding.html

For example, the reference coding can be specified explicitly as in this formula

"A ~ B + C(weekday, Treatment('Sunday'))"

http://patsy.readthedocs.io/en/latest/API-reference.html#patsy.Treatment