Examples#

This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository.

We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page

 

Linear Regression Models#

Ordinary Least Squares

../_images/ols.png

Generalized Least Squares

../_images/gls.png

Quantile Regression

../_images/quantile_regression.png

Recursive Least Squares

../_images/recursive_ls.png

Rolling Least Squares

../_images/rolling_ls.png

Regression Diagnostics

../_images/regression_diagnostics.png

Weighted Least Squares

../_images/wls.png

Linear Mixed-Effects

../_images/mixed_lm.png

Variance Component Analysis

../_images/variance_components.png
 

Plotting#

Regression Plots

../_images/regression_plots.png

Linear Regression Diagnostic

../_images/linear_regression_diagnostics_plots.png

Categorical Interactions

../_images/categorical_interaction_plot.png

Box Plots

../_images/plots_boxplots.png
 

Discrete Choice Models#

Getting Started

../_images/discrete_overview.png

Fair’s Affairs Data

../_images/fairs_data.png

Ordinal Regression

../_images/ordered_model.png

Hurdel count Models

../_images/count_hurdle.png

Post-estimation for Poisson Regression

../_images/post_estimation.png
 

Nonparametric Statistics#

Univariate Kernel Density Estimator

../_images/kde_overview.png

Lowess Regression

../_images/lowess.png
 

Generalized Linear Models#

Generalized Linear Models Overview

../_images/glm_overview.png

Using Formulas with GLMs

../_images/glm_formulas.png

Weighting Observations with GLMs

../_images/glm_weights.png

Influence Measures for GLMs

../_images/influence_glm_logit.png

Quasi-binomial regression

../_images/quasibinomial.png
 

Robust Regression#

M-estimators for Robust Regression

../_images/rlm_overview.png

Comparing OLS and RLM

../_images/rlm.png
 

Generalized Estimating Equations#

GEE Nested Covariance Structure

../_images/gee-cov-struct.png

GEE Score Tests

../_images/gee-score-tests.png
 

Statistics#

ANOVA

../_images/anova.png

Poisson rates

../_images/stats_poisson.png

Rank Comparison

../_images/rank_compare.png

Meta-Analysis in statsmodels

../_images/metaanalysis1.png

Mediation analysis with duration data

../_images/mediation_survival.png

Treatment Effect Estimation

../_images/treatment_effect.png

Copulas

../_images/copula.png
 

Time Series Analysis#

Autoregressions

../_images/autoregressions.png

Autoregressive Distributed Lag Models

../_images/autoregressive_distributed_lag.png

Deterministic Terms

../_images/deterministics.png

ARMA: Sunspots Data

../_images/tsa_arma0.png

ARMA: Artificial Data

../_images/tsa_arma1.png

Time Series Filters

../_images/tsa_filters.png

Markov switching dynamic regression

../_images/markov_regression.png

Markov switching autoregression

../_images/markov_autoregression.png

Exponential Smoothing

../_images/exponential_smoothing.png

Seasonal Decomposition

../_images/stl_decomposition.png

Multi-seasonal Decomposition

../_images/mstl_decomposition.png

Seasonal Diagnostic Plots

../_images/stl_seasonal_diagnostic_plot.png

Stationarity and detrending (ADF/KPSS)

../_images/stationarity_detrending_adf_kpss.png
 

State space models#

SARIMAX: Introduction

../_images/statespace_sarimax_stata.png

SARIMAX: Model selection, missing data

../_images/statespace_sarimax_internet.png

SARIMAX: Frequently Asked Questions (FAQ)

../_images/statespace_sarimax_faq.png

VARMAX: Introduction

../_images/statespace_varmax.png

Dynamic Factor Models: Application

../_images/statespace_dfm_coincident.png

Unobserved Components: Application

../_images/statespace_structural_harvey_jaeger.png

Trends and cycles in unemployment

../_images/statespace_cycles.png

State space modeling: Local Linear Trends

../_images/statespace_local_linear_trend.png

Statespace ARMA: Sunspots Data

../_images/statespace_arma0.png

Seasonality in Time Series Data

../_images/statespace_seasonal.png

Seasonal Differencing for ARIMA Models

../_images/arima_seasonal.png

Fixed / constrained parameters in state space models

../_images/statespace_fixed_params.png

TVP-VAR, MCMC, and sparse simulation smoothing

../_images/statespace_tvpvar_mcmc_cfa.png

SARIMAX estimation with Bayesian methods

../_images/statespace_sarimax_pymc3.png

Forecasting, updating datasets, and the “news”

../_images/statespace_news.png

Statespace: Custom Models

../_images/statespace_custom_models.png

ETS models

../_images/ets.png
 

State space models - Technical notes#

State space models: concentrating out the scale

../_images/statespace_concentrated_scale.png

State space models: Chandrasekhar recursions

../_images/statespace_chandrasekhar.png
 

Forecasting#

Forecasting using the Theta Model

../_images/theta-model.png
 

Multivariate Methods#

Principal Component Analysis

../_images/pca_fertility_factors.png

Multivariate Least Squares

../_images/multivariate_ls.png
 

User Notes#

Contrasts

../_images/contrasts.png

Formulas

../_images/formulas_intro.png

Prediction

../_images/prediction.png

Forecasting in statsmodels

../_images/statespace_forecasting.png

Generic Maximum Likelihood

../_images/generic_mle.png

Dates in Time-Series Models

../_images/tsa_dates.png

Least squares fitting of models to data

../_images/chi2_fitting.png

Distributed Estimations

../_images/distributed_estimation.png