A simple ordinary least squares model.
Parameters: | endog : array-like
exog : array-like
missing : str
hasconst : None or bool
|
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See also
Notes
No constant is added by the model unless you are using formulas.
Examples
>>> import numpy as np
>>>
>>> import statsmodels.api as sm
>>>
>>> Y = [1,3,4,5,2,3,4]
>>> X = range(1,8)
>>> X = sm.add_constant(X)
>>>
>>> model = sm.OLS(Y,X)
>>> results = model.fit()
>>> results.params
array([ 2.14285714, 0.25 ])
>>> results.tvalues
array([ 1.87867287, 0.98019606])
>>> print(results.t_test([1, 0])))
<T test: effect=array([ 2.14285714]), sd=array([[ 1.14062282]]), t=array([[ 1.87867287]]), p=array([[ 0.05953974]]), df_denom=5>
>>> print(results.f_test(np.identity(2)))
<F test: F=array([[ 19.46078431]]), p=[[ 0.00437251]], df_denom=5, df_num=2>
Attributes
weights | scalar | Has an attribute weights = array(1.0) due to inheritance from WLS. |
Methods
fit([method, cov_type, cov_kwds, use_t]) | Full fit of the model. |
fit_regularized([method, maxiter, alpha, ...]) | Return a regularized fit to a linear regression model. |
from_formula(formula, data[, subset]) | Create a Model from a formula and dataframe. |
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(params[, exog]) | Return linear predicted values from a design matrix. |
score(params) | Score vector of model. |
whiten(Y) | OLS model whitener does nothing: returns Y. |
Attributes
df_model | The model degree of freedom, defined as the rank of the regressor matrix minus 1 if a constant is included. |
df_resid | The residual degree of freedom, defined as the number of observations minus the rank of the regressor matrix. |
endog_names | |
exog_names |