Brief introduction of Python statistical regression in Python mathematical modeling library

Directory 1, About StatsModels2, Documentation 3, Main Features 4, Get and Install

1. About StatsModels

statsmodels (http://www. statsmodels. org) is an Python library for fitting various statistical models, performing statistical tests, and data exploration and visualization.

2. Documentation

The latest version of the document is located at:
https://www.statsmodels.org/stable/

3. Main functions

1. Linear regression model:

Ordinary minimum 2 multiplication Generalized least 2 multiplication Weighted least 2 multiplication Minimum 2 multiplication with autoregressive error Quantile regression Recursive minimum 2 multiplication

2. Mixed linear models with mixed effects and variance components

3. glm: Exponential family distribution of generalized linear model supporting all one parameter

Bayesian hybrid glm of term 4.2 and poisson

5. gee: Generalized estimation equation for unidirectional clustering or longitudinal data

6. Discrete model:

logit and probit Polynomial logit (mnlogit) Poisson and Generalized Poisson Regression Negative binomial regression Zero expansion counting model

7. rlm: Robust linear model supporting multiple m estimates.

8. Time series analysis: Time series analysis model

Complete state space modeling framework Seasonal arima and arimax models Varma and Varmax models Dynamic factor model Unobserved component model Markov switching model (MSAR), also known as hidden Markov model (HMM) Univariate time series analysis: ar, arima Vector autoregressive model, var and structure var Vector error correction model, vecm Exponential smoothing, Holtwinters Hypothesis testing of time series: unit root, cointegration, etc. Descriptive statistics and process model of time series analysis

9. Survival analysis:

Proportional hazard regression (cox model)

Survivor function estimation (kaplan-meier)

Estimation of cumulative correlation function

10. Multivariate:

Principal Component Analysis of Missing Data Rotation factor analysis Manova Canonical correlation

11. Nonparametric statistics: univariate and multivariate kernel density estimates

12. Datasets: Datasets for example and test

13. Statistics: Extensive statistical tests

Diagnostic and specification testing Goodness of fit and normality test Multiple test function Various additional statistical tests

14. Mouse interpolation, sequential statistical regression and Gaussian interpolation

15. Intermediary analysis

16. Graphics include mapping functions for visualizing analytical data and model results

17. Input/Output

A tool for reading stata. dta files, but pandas has a newer version Table outputs are ascii, latex, and html

18. Sandbox: statsmodels contains a sandbox folder containing development and tests that are not considered "production ready".

Estimator of generalized moment method (gmm) Nuclear regression Various extensions of scipy. stats. distributions Panel data model Information theory measure

4. Acquisition and Installation

pip3 install --upgrade statsmodel -i https://pypi.tsinghua.edu.cn/simple

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