statsmodels.stats.weightstats.CompareMeans.tconfint_diff CompareMeans.tconfint_diff(alpha=0.05, alternative='two-sided', usevar='pooled') [source] confidence interval for the difference in means Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following : ?two-sided?: H1:

statsmodels.iolib.summary2.Summary.add_df Summary.add_df(df, index=True, header=True, float_format='%.4f', align='r') [source] Add the contents of a DataFrame to summary table Parameters: df : DataFrame header: bool : Reproduce the DataFrame column labels in summary table index: bool : Reproduce the DataFrame row labels in summary table float_format: string : Formatting to float data columns align : string Data alignment (l/c/r)

statsmodels.sandbox.distributions.extras.ACSkewT_gen.fit_loc_scale ACSkewT_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.duration.hazard_regression.PHReg.efron_loglike PHReg.efron_loglike(params) [source] Returns the value of the log partial likelihood function evaluated at params, using the Efron method to handle tied times.

statsmodels.sandbox.stats.multicomp.qcrit statsmodels.sandbox.stats.multicomp.qcrit = '\n 2 3 4 5 6 7 8 9 10\n5 3.64 5.70 4.60 6.98 5.22 7.80 5.67 8.42 6.03 8.91 6.33 9.32 6.58 9.67 6.80 9.97 6.99 10.24\n6 3.46 5.24 4.34 6.33 4.90 7.03 5.30 7.56 5.63 7.97 5.90 8.32 6.12 8.61 6.32 8.87 6.49 9.10\n7 3.34 4.95 4.16 5.92 4.68 6.54 5.06 7.01 5.36 7.37 5.61 7.68 5.82 7.94 6.00 8.17 6.16 8.37\n8 3.26 4.75 4.04 5.64 4.53 6.20 4.89 6.62 5.17 6.96 5.40 7.24 5.60 7.47 5.77 7.68 5.92 7.86\n9 3.20 4.60 3.9

statsmodels.tsa.arima_process.arma_periodogram statsmodels.tsa.arima_process.arma_periodogram(ar, ma, worN=None, whole=0) [source] periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a sin

statsmodels.tsa.tsatools.lagmat2ds statsmodels.tsa.tsatools.lagmat2ds(x, maxlag0, maxlagex=None, dropex=0, trim='forward') [source] generate lagmatrix for 2d array, columns arranged by variables Parameters: x : array_like, 2d 2d data, observation in rows and variables in columns maxlag0 : int for first variable all lags from zero to maxlag are included maxlagex : None or int max lag for all other variables all lags from zero to maxlag are included dropex : int (default is 0) exclude

statsmodels.genmod.generalized_linear_model.GLMResults.null static GLMResults.null() [source]

statsmodels.discrete.discrete_model.ProbitResults.summary2 ProbitResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title a

statsmodels.robust.norms.LeastSquares.weights LeastSquares.weights(z) [source] The least squares estimator weighting function for the IRLS algorithm. The psi function scaled by the input z Parameters: z : array-like 1d array Returns: weights : array weights(z) = np.ones(z.shape)