statsmodels.regression.linear_model.OLSResults.tvalues static OLSResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.discrete.discrete_model.MultinomialResults.initialize MultinomialResults.initialize(model, params, **kwd)

Quantile regression Link to Notebook GitHub This example page shows how to use statsmodels' QuantReg class to replicate parts of the analysis published in Koenker, Roger and Kevin F. Hallock. "Quantile Regressioin". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143?156 We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). Setup We first need to load some modules and

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.r_squared KernelCensoredReg.r_squared() Returns the R-Squared for the nonparametric regression. Notes For more details see p.45 in [2] The R-Squared is calculated by: where is the mean calculated in fit at the exog points.

statsmodels.iolib.summary2.Summary.add_title Summary.add_title(title=None, results=None) [source] Insert a title on top of the summary table. If a string is provided in the title argument, that string is printed. If no title string is provided but a results instance is provided, statsmodels attempts to construct a useful title automatically.

statsmodels.discrete.discrete_model.CountResults.cov_params CountResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like,

statsmodels.sandbox.tsa.fftarma.ArmaFft.arma2ar ArmaFft.arma2ar(nobs=None)

statsmodels.sandbox.regression.gmm.IVGMMResults.remove_data IVGMMResults.remove_data() 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 an attribute

statsmodels.discrete.discrete_model.CountResults class statsmodels.discrete.discrete_model.CountResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for count data Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterion.

statsmodels.emplike.descriptive.DescStatMV.mv_mean_contour DescStatMV.mv_mean_contour(mu1_low, mu1_upp, mu2_low, mu2_upp, step1, step2, levs=[0.2, 0.1, 0.05, 0.01, 0.001], var1_name=None, var2_name=None, plot_dta=False) [source] Creates a confidence region plot for the mean of bivariate data Parameters: m1_low : float Minimum value of the mean for variable 1 m1_upp : float Maximum value of the mean for variable 1 mu2_low : float Minimum value of the mean for variable 2 mu2_upp : float