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Installing
Getting started
User Guide
Examples
API Reference
About statsmodels
Developer Page
Release Notes

GitHub
PyPI
DOI

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endog, exog, what’s that?
Import Paths and Structure
Fitting models using R-style formulas
Pitfalls

Linear Regression
Generalized Linear Models
Generalized Estimating Equations
Generalized Additive Models (GAM)
Robust Linear Models
Linear Mixed Effects Models
Regression with Discrete Dependent Variable
Generalized Linear Mixed Effects Models
ANOVA
Other Models othermod

Time Series analysis tsa
Time Series Analysis by State Space Methods statespace
Vector Autoregressions tsa.vector_ar

Methods for Survival and Duration Analysis
Nonparametric Methods nonparametric
Generalized Method of Moments gmm
Other Models miscmodels
Multivariate Statistics multivariate

Statistics stats
Contingency tables
Multiple Imputation with Chained Equations
Treatment Effects treatment
Empirical Likelihood emplike
Distributions
Graphics
Input-Output iolib
Tools
Working with Large Data Sets
- statsmodels.base.distributed_estimation.DistributedModel
- statsmodels.base.distributed_estimation.DistributedResults
Optimization

The Datasets Package

Sandbox

User Guide
Working with Large Data Sets
statsmodels.base.distributed_estimation.DistributedResults
statsmodels.base.distributed_estimation.DistributedResults.pvalues

statsmodels.base.distributed_estimation.DistributedResults.pvalues#

DistributedResults.pvalues#: The two-tailed p values for the t-stats of the params.

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statsmodels.base.distributed_estimation.DistributedResults.llf

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statsmodels.base.distributed_estimation.DistributedResults.tvalues

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DistributedResults.pvalues

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