statsmodels.tsa.arima_model.ARIMAResults.t_test ARIMAResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple of arrays i

statsmodels.miscmodels.tmodel.TLinearModel.jac TLinearModel.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.sandbox.stats.multicomp.ccols statsmodels.sandbox.stats.multicomp.ccols = array([ 2, 3, 4, 5, 6, 7, 8, 9, 10])

statsmodels.tsa.vector_ar.var_model.VARResults class statsmodels.tsa.vector_ar.var_model.VARResults(endog, endog_lagged, params, sigma_u, lag_order, model=None, trend='c', names=None, dates=None) [source] Estimate VAR(p) process with fixed number of lags Parameters: endog : array endog_lagged : array params : array sigma_u : array lag_order : int model : VAR model instance trend : str {?nc?, ?c?, ?ct?} names : array-like List of names of the endogenous variables in order of appearance in e

statsmodels.sandbox.stats.runs.runstest_2samp statsmodels.sandbox.stats.runs.runstest_2samp(x, y=None, groups=None, correction=True) [source] Wald-Wolfowitz runstest for two samples This tests whether two samples come from the same distribution. Parameters: x : array_like data, numeric, contains either one group, if y is also given, or both groups, if additionally a group indicator is provided y : array_like (optional) data, numeric groups : array_like group labels or indicator the dat

statsmodels.graphics.boxplots.beanplot statsmodels.graphics.boxplots.beanplot(data, ax=None, labels=None, positions=None, side='both', jitter=False, plot_opts={}) [source] Make a bean plot of each dataset in the data sequence. A bean plot is a combination of a violinplot (kernel density estimate of the probability density function per point) with a line-scatter plot of all individual data points. Parameters: data : sequence of ndarrays Data arrays, one array per value in positions. ax : M

statsmodels.stats.weightstats.DescrStatsW class statsmodels.stats.weightstats.DescrStatsW(data, weights=None, ddof=0) [source] descriptive statistics and tests with weights for case weights Assumes that the data is 1d or 2d with (nobs, nvars) observations in rows, variables in columns, and that the same weight applies to each column. If degrees of freedom correction is used, then weights should add up to the number of observations. ttest also assumes that the sum of weights corresponds to th

statsmodels.sandbox.regression.gmm.IVGMM.fitstart IVGMM.fitstart() [source]

statsmodels.discrete.discrete_model.ProbitResults.llf static ProbitResults.llf()

statsmodels.stats.power.TTestIndPower class statsmodels.stats.power.TTestIndPower(**kwds) [source] Statistical Power calculations for t-test for two independent sample currently only uses pooled variance Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs1, alpha[, ratio, ...]) Calculate the power of a t-test for two independent sample solve_power([effect_size, nobs1, alpha, ...]) solve for any one p