(Sorry to ask but http://statsmodels.sourceforge.net/ is currently down and I can't access the docs)
I'm doing a linear regression using statsmodels
, basically:
import statsmodels.api as sm model = sm.OLS(y,x) results = model.fit()
I know that I can print out the full set of results with:
print results.summary()
which outputs something like:
OLS Regression Results ============================================================================== Dep. Variable: y R-squared: 0.952 Model: OLS Adj. R-squared: 0.951 Method: Least Squares F-statistic: 972.9 Date: Mon, 20 Jul 2015 Prob (F-statistic): 5.55e-34 Time: 15:35:22 Log-Likelihood: -78.843 No. Observations: 50 AIC: 159.7 Df Residuals: 49 BIC: 161.6 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [95.0% Conf. Int.] ------------------------------------------------------------------------------ x1 1.0250 0.033 31.191 0.000 0.959 1.091 ============================================================================== Omnibus: 16.396 Durbin-Watson: 2.166 Prob(Omnibus): 0.000 Jarque-Bera (JB): 3.480 Skew: -0.082 Prob(JB): 0.175 Kurtosis: 1.718 Cond. No. 1.00 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
I need a way to print out only the values of coef
and std err
.
I can access coef
with:
print results.params
but I've found no way to print out std err
.
How can I do this?
Standard Error of OLS Estimates All of the terms in the equations above except σ2 can be calculated from the sample drawn. Therefore, we will need an unbiased estimator ^σ2=∑^u2in−2 σ ^ 2 = ∑ u ^ i 2 n − 2 . The denominator n−2 represents the degrees of freedom. ∑^u2i ∑ u ^ i 2 is the residual sum of squares.
Python: StatsModels I will point out two issues here. The condition number (abbreviated “Cond. No.” in the summary) is a measure of “how close to singular” a matrix is; the higher, the “more singular” (and infinite means singular — i.e. noninvertible), and the more “error” a best fit approximation is.
Applying the answer given here I used dir() to print all the attributes of the results
object.
After that I searched for the one that contained the std err
value and it turned out to be:
print results.bse
(Not sure what the b
stands for in bse
, but I guess the se
stands for "standard error")
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