Regression Diagnostics and Specification Tests Introduction In many cases of statistical analysis, we are not sure whether our statistical model is correctly specified. For example when using ols, then linearity and homoscedasticity are assumed, some test statistics additionally assume that the errors are normally distributed or that we have a large sample. Since our results depend on these statistical assumptions, the results are only correct of our assumptions hold (at least approximately).

statsmodels.tsa.arima_process.arma_acf statsmodels.tsa.arima_process.arma_acf(ar, ma, nobs=10) [source] theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, m

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.fit_loc_scale TransfTwo_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.genmod.cov_struct.Independence.summary Independence.summary() [source]

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.logsf TransfTwo_gen.logsf(x, *args, **kwds) Log of the survival function of the given RV. Returns the log of the ?survival function,? defined as (1 - cdf), evaluated at x. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, opti

statsmodels.sandbox.distributions.extras.ACSkewT_gen.var ACSkewT_gen.var(*args, **kwds) Variance of the distribution Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: var : float the variance of the distribution

statsmodels.sandbox.distributions.extras.ACSkewT_gen.entropy ACSkewT_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1).

statsmodels.genmod.generalized_estimating_equations.GEEResults.split_centered_resid static GEEResults.split_centered_resid() Returns the residuals centered within each group. The residuals are returned as a list of arrays containing the centered residuals for each cluster.

statsmodels.regression.linear_model.RegressionResults.HC1_se static RegressionResults.HC1_se() [source] See statsmodels.RegressionResults

statsmodels.tsa.vector_ar.irf.IRAnalysis.plot_cum_effects IRAnalysis.plot_cum_effects(orth=False, impulse=None, response=None, signif=0.05, plot_params=None, subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, seed=None) Plot cumulative impulse response functions Parameters: orth : bool, default False Compute orthogonalized impulse responses impulse : string or int variable providing the impulse response : string or int variable affected by the impulse signif : float