statsmodels.regression.quantile_regression.QuantRegResults.cov_HC0 static QuantRegResults.cov_HC0() See statsmodels.RegressionResults

Interactions and ANOVA Link to Notebook GitHub Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html Download and format data: In [1]: from __future__ import print_function from statsmodels.compat import urlopen import numpy as np np.set_printoptions(precision=4, suppress=True) import statsmodels.api as sm import pandas as pd pd.set_option("display.width", 100) import matplotlib.pyplot as plt from statsmodels.fo

Pitfalls This page lists issues which may arise while using statsmodels. These can be the result of data-related or statistical problems, software design, ?non-standard? use of models, or edge cases. statsmodels provides several warnings and helper functions for diagnostic checking (see this blog article for an example of misspecification checks in linear regression). The coverage is of course not comprehensive, but more warnings and diagnostic functions will be added over time. While the under

statsmodels.duration.hazard_regression.PHRegResults.schoenfeld_residuals static PHRegResults.schoenfeld_residuals() [source] A matrix containing the Schoenfeld residuals. Notes Schoenfeld residuals for censored observations are set to zero.

statsmodels.duration.hazard_regression.PHRegResults.martingale_residuals static PHRegResults.martingale_residuals() [source] The martingale residuals.

statsmodels.tsa.ar_model.ARResults.bse static ARResults.bse() [source]

statsmodels.tsa.arima_model.ARMAResults.arroots static ARMAResults.arroots() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.tvalues static VARResults.tvalues() [source] Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test significance.

statsmodels.tsa.vector_ar.var_model.VARProcess class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, intercept, sigma_u, names=None) [source] Class represents a known VAR(p) process Parameters: coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k) Returns: **Attributes:** : Methods acf([nlags]) Compute theoretical autocovariance function acorr([nlags]) Compute theoretical autocorrelation function forecast(y, steps) Produ

statsmodels.discrete.discrete_model.BinaryResults.llnull static BinaryResults.llnull()