statsmodels.stats.proportion.proportion_confint statsmodels.stats.proportion.proportion_confint(count, nobs, alpha=0.05, method='normal') [source] confidence interval for a binomial proportion Parameters: count : int or array number of successes nobs : int total number of trials alpha : float in (0, 1) significance level, default 0.05 method : string in [?normal?] method to use for confidence interval, currently available methods : normal : asymptotic normal approximation agresti_

statsmodels.genmod.families.links.NegativeBinomial.deriv NegativeBinomial.deriv(p) [source] Derivative of the negative binomial transform Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the negative binomial transform link function Notes g?(x) = 1/(x+alpha*x^2)

statsmodels.stats.weightstats.DescrStatsW.var_ddof DescrStatsW.var_ddof(ddof=0) [source] variance of data given ddof Parameters: ddof : int, float degrees of freedom correction, independent of attribute ddof Returns: var : float, ndarray variance with denominator sum_weights - ddof

statsmodels.iolib.table.csv2st statsmodels.iolib.table.csv2st(csvfile, headers=False, stubs=False, title=None) [source] Return SimpleTable instance, created from the data in csvfile, which is in comma separated values format. The first row may contain headers: set headers=True. The first column may contain stubs: set stubs=True. Can also supply headers and stubs as tuples of strings.

statsmodels.discrete.discrete_model.CountResults.tvalues static CountResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.regression.quantile_regression.QuantRegResults.resid static QuantRegResults.resid()

statsmodels.discrete.discrete_model.MNLogit.loglike MNLogit.loglike(params) [source] Log-likelihood of the multinomial logit model. Parameters: params : array-like The parameters of the multinomial logit model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes where if individual i chose alternative j and 0 if not.

statsmodels.discrete.discrete_model.MultinomialModel.hessian MultinomialModel.hessian(params) The Hessian matrix of the model

statsmodels.genmod.generalized_linear_model.GLMResults.remove_data GLMResults.remove_data() [source] remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

statsmodels.discrete.discrete_model.Logit.jac Logit.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Logit model Jacobian of the log-likelihood for each observation Parameters: params: array-like : The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations