acorr_breush_godfrey(results[, nlags, store]) |
Breush Godfrey Lagrange Multiplier tests for residual autocorrelation |
acorr_ljungbox(x[, lags, boxpierce]) |
Ljung-Box test for no autocorrelation |
anova_lm(*args, **kwargs) |
ANOVA table for one or more fitted linear models. |
binom_test(count, nobs[, prop, alternative]) |
Perform a test that the probability of success is p. |
binom_test_reject_interval(value, nobs[, ...]) |
rejection region for binomial test for one sample proportion |
binom_tost(count, nobs, low, upp) |
exact TOST test for one proportion using binomial distribution |
binom_tost_reject_interval(low, upp, nobs[, ...]) |
rejection region for binomial TOST |
breaks_cusumolsresid(olsresidual[, ddof]) |
cusum test for parameter stability based on ols residuals |
breaks_hansen(olsresults) |
test for model stability, breaks in parameters for ols, Hansen 1992 |
chisquare_effectsize(probs0, probs1[, ...]) |
effect size for a chisquare goodness-of-fit test |
cochrans_q(x) |
Cochran’s Q test for identical effect of k treatments |
corr_clipped(corr[, threshold]) |
Find a near correlation matrix that is positive semi-definite |
corr_nearest(corr[, threshold, n_fact]) |
Find the nearest correlation matrix that is positive semi-definite. |
cov_cluster(results, group[, use_correction]) |
cluster robust covariance matrix |
cov_cluster_2groups(results, group[, ...]) |
cluster robust covariance matrix for two groups/clusters |
cov_hac(results[, nlags, weights_func, ...]) |
heteroscedasticity and autocorrelation robust covariance matrix (Newey-West) |
cov_hc0(results) |
See statsmodels.RegressionResults |
cov_hc1(results) |
See statsmodels.RegressionResults |
cov_hc2(results) |
See statsmodels.RegressionResults |
cov_hc3(results) |
See statsmodels.RegressionResults |
cov_nearest(cov[, method, threshold, ...]) |
Find the nearest covariance matrix that is postive (semi-) definite |
cov_nw_panel(results, nlags, groupidx[, ...]) |
Panel HAC robust covariance matrix |
cov_white_simple(results[, use_correction]) |
heteroscedasticity robust covariance matrix (White) |
durbin_watson(resids[, axis]) |
Calculates the Durbin-Watson statistic |
fdrcorrection(pvals[, alpha, method, is_sorted]) |
pvalue correction for false discovery rate |
fdrcorrection_twostage(pvals[, alpha, ...]) |
(iterated) two stage linear step-up procedure with estimation of number of true |
gof_chisquare_discrete(distfn, arg, rvs, ...) |
perform chisquare test for random sample of a discrete distribution |
het_arch(resid[, maxlag, autolag, store, ...]) |
Engle’s Test for Autoregressive Conditional Heteroscedasticity (ARCH) |
het_breushpagan(resid, exog_het) |
Breush-Pagan Lagrange Multiplier test for heteroscedasticity |
het_white(resid, exog[, retres]) |
White’s Lagrange Multiplier Test for Heteroscedasticity |
jarque_bera(resids[, axis]) |
Calculate residual skewness, kurtosis, and do the JB test for normality |
lillifors(x[, pvalmethod]) |
Lillifors test for normality, |
linear_harvey_collier(res) |
Harvey Collier test for linearity |
linear_lm(resid, exog[, func]) |
Lagrange multiplier test for linearity against functional alternative |
linear_rainbow(res[, frac]) |
Rainbow test for linearity |
mcnemar(x[, y, exact, correction]) |
McNemar test |
multipletests(pvals[, alpha, method, ...]) |
test results and p-value correction for multiple tests |
normal_ad(x[, axis]) |
Anderson-Darling test for normal distribution unknown mean and variance |
omni_normtest(resids[, axis]) |
Omnibus test for normality |
power_binom_tost(low, upp, nobs[, p_alt, alpha]) |
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power_ztost_prop(low, upp, nobs, p_alt[, ...]) |
Power of proportions equivalence test based on normal distribution |
powerdiscrepancy(observed, expected[, ...]) |
Calculates power discrepancy, a class of goodness-of-fit tests as a measure of discrepancy between observed and expected data. |
proportion_confint(count, nobs[, alpha, method]) |
confidence interval for a binomial proportion |
proportion_effectsize(prop1, prop2[, method]) |
effect size for a test comparing two proportions |
proportions_chisquare(count, nobs[, value]) |
test for proportions based on chisquare test |
proportions_chisquare_allpairs(count, nobs) |
chisquare test of proportions for all pairs of k samples |
proportions_chisquare_pairscontrol(count, nobs) |
chisquare test of proportions for pairs of k samples compared to control |
proportions_ztest(count, nobs[, value, ...]) |
test for proportions based on normal (z) test |
proportions_ztost(count, nobs, low, upp[, ...]) |
Equivalence test based on normal distribution |
recursive_olsresiduals(olsresults[, skip, ...]) |
calculate recursive ols with residuals and cusum test statistic |
runstest_1samp(x[, cutoff, correction]) |
use runs test on binary discretized data above/below cutoff |
runstest_2samp(x[, y, groups, correction]) |
Wald-Wolfowitz runstest for two samples |
se_cov(cov) |
get standard deviation from covariance matrix |
symmetry_bowker(table) |
Test for symmetry of a (k, k) square contingency table |
ttest_ind(x1, x2[, alternative, usevar, ...]) |
ttest independent sample |
ttost_ind(x1, x2, low, upp[, usevar, ...]) |
test of (non-)equivalence for two independent samples |
ttost_paired(x1, x2, low, upp[, transform, ...]) |
test of (non-)equivalence for two dependent, paired sample |
tukeyhsd(mean_all, nobs_all, var_all[, df, ...]) |
simultaneous Tukey HSD |
unitroot_adf(x[, maxlag, trendorder, ...]) |
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zconfint(x1[, x2, value, alpha, ...]) |
confidence interval based on normal distribution z-test |
ztest(x1[, x2, value, alternative, usevar, ddof]) |
test for mean based on normal distribution, one or two samples |
ztost(x1, low, upp[, x2, usevar, ddof]) |
Equivalence test based on normal distribution |