scikits.statsmodels.regression.OLS

class scikits.statsmodels.regression.OLS(endog, exog=None)

A simple ordinary least squares model.

Methods

inherited from regression.GLS

Parameters:

endog : array-like

1d vector of response/dependent variable

exog: array-like :

Column ordered (observations in rows) design matrix.

Notes

OLS, as the other models, assumes that the design matrix contains a constant.

Examples

>>> import numpy as np
>>>
>>> import scikits.statsmodels as sm
>>>
>>> Y = [1,3,4,5,2,3,4]
>>> X = range(1,8) #[:,np.newaxis]
>>> X = sm.add_constant(X)
>>>
>>> model = sm.OLS(Y,X)
>>> results = model.fit()
>>> # or results = model.results
>>> results.params
array([ 0.25      ,  2.14285714])
>>> results.t()
array([ 0.98019606,  1.87867287])
>>> print results.t_test([0,1])
<T test: effect=2.1428571428571423, sd=1.1406228159050935, t=1.8786728732554485, p=0.059539737780605395, df_denom=5>
>>> print results.f_test(np.identity(2))
<F test: F=19.460784313725501, p=0.00437250591095, df_denom=5, df_num=2>

Attributes

weights scalar Has an attribute weights = array(1.0) due to inheritance from WLS.
See regression.GLS    

Methods

fit() Full fit of the model.
hessian(params) The Hessian matrix of the model
information(params) Fisher information matrix of model
initialize()
loglike(params) The likelihood function for the clasical OLS model.
predict(exog[, params]) Return linear predicted values from a design matrix.
score(params) Score vector of model.
whiten(Y) OLS model whitener does nothing: returns Y.

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