Results._doc_ and results methods have their own docstrings. Have a look at dir(results) to see available results. Variable: y R-squared: 0.161 Model: OLS Adj. summary ()) OLS Regression Results = Dep. fit () # Inspect the results In : print ( results. dot ( X, beta ) + e # Fit regression model In : results = sm. add_constant ( X ) In : beta = In : e = np. In : import numpy as np In : import statsmodels.api as sm # Generate artificial data (2 regressors + constant) In : nobs = 100 In : X = np. You can also use numpy arrays instead of formulas: Standard Errors assume that the covariance matrix of the errors is correctly specified.
#STATSMODEL PYTHON 3.5 DOWNLOAD ARCHIVE#
To download an archive containing all the documents for this version of Python in one of various formats, follow one of links in this table. Variable: Lottery R-squared: 0.348 Model: OLS Adj. ols ( 'Lottery ~ Literacy + np.log(Pop1831)', data = dat ). data # Fit regression model (using the natural log of one of the regressors) In : results = smf. The res object has many useful attributes.In : import numpy as np In : import statsmodels.api as sm In : import as smf # Load data In : dat = sm. Variable: Lottery R-squared: 0.338 Model: OLS Adj. summary ()) # Summarize model OLS Regression Results = Dep.
OLS ( y, X ) # Describe model In : res = mod. independent, predictor, regressor, etc.).
The first is a matrix of endogenous variable(s) (i.e.ĭependent, response, regressand, etc.). To fit most of the models covered by statsmodels, you will need to create The model isĮstimated using ordinary least squares regression (OLS).
#STATSMODEL PYTHON 3.5 DOWNLOAD SERIES#
We need toĬontrol for the level of wealth in each department, and we also want to includeĪ series of dummy variables on the right-hand side of our regression equation toĬontrol for unobserved heterogeneity due to regional effects. We want to know whether literacy rates in the 86 French departments areĪssociated with per capita wagers on the Royal Lottery in the 1820s. dropna () In : df Out: Department Lottery Literacy Wealth Region 80 Vendee 68 28 56 W 81 Vienne 40 25 68 W 82 Haute-Vienne 55 13 67 C 83 Vosges 14 62 82 E 84 Yonne 51 47 30 C Substantive motivation and model ¶
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