statsmodels.regression.quantile_regression.QuantRegResults.predict QuantRegResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can p

statsmodels.emplike.descriptive.DescStatUV.test_kurt DescStatUV.test_kurt(kurt0, return_weights=False) [source] Returns -2 x log-likelihood and the p-value for the hypothesized kurtosis. Parameters: kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple The log-likelihood ratio and p-value of kurt0

statsmodels.discrete.discrete_model.LogitResults.resid_pearson static LogitResults.resid_pearson() Pearson residuals Notes Pearson residuals are defined to be where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.tsa.ar_model.AR class statsmodels.tsa.ar_model.AR(endog, dates=None, freq=None, missing='none') [source] Autoregressive AR(p) model Parameters: endog : array-like 1-d endogenous response variable. The independent variable. dates : array-like of datetime, optional An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex. freq : str, optional The frequency of the time-series. A Pandas offset or ?B?, ?D?, ?W?, ?

statsmodels.iolib.foreign.StataReader class statsmodels.iolib.foreign.StataReader(fname, missing_values=False, encoding=None) [source] Stata .dta file reader. Provides methods to return the metadata of a Stata .dta file and a generator for the data itself. Parameters: file : file-like A file-like object representing a Stata .dta file. missing_values : bool If missing_values is True, parse missing_values and return a Missing Values object instead of None. encoding : string, optional Use

statsmodels.genmod.generalized_linear_model.GLM.hessian_factor GLM.hessian_factor(params, scale=None, observed=True) [source] Weights for calculating Hessian Parameters: params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. observed : bool If True, then the observed Hessian is returned. I

statsmodels.regression.mixed_linear_model.MixedLMResults.conf_int MixedLMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence

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.sandbox.distributions.transformed.TransfTwo_gen.interval TransfTwo_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parame

statsmodels.genmod.cov_struct.Independence.summary Independence.summary() [source]