StatsModel Library Tutorial (original) (raw)
Last Updated : 5 Mar, 2026
The StatsModels library in Python is a tool for statistical modeling, hypothesis testing and data analysis. It provides built-in functions for fitting different types of statistical models, performing hypothesis tests and exploring datasets.
- Used in data science, economics, finance and research fields.
- Focuses on understanding relationships between variables.
- Helps in performing statistical analysis easily and efficiently.
- Provides clear, reliable and interpretable results.
- Useful for regression, hypothesis testing and statistical modeling.
Installing and Importing StatsModels
**Installing StatsModels: To install the library, use the following command:
pip install statsmodels
**Importing StatsModels: Once installed, import it using:
import statsmodels.api as sm
import statsmodels.formula.api as smf
Regression and Linear Models
StatsModels provides several regression models to analyze relationships between variables and make predictions. These models help in understanding how independent variables influence a dependent variable.
- linear regression in Statsmodels
- Ordinary Least Squares (OLS)
- Extracting regression coefficient
- Regression model summary
- confidence and prediction interval
- Logistic regression
- Generalized Method of Moments (GMM)
- Implementing Generalized Least Squares (GLS)
- Panel Data Analysis
Once a model is built, Statsmodels provides tools to analyze data more effectively.
Descriptive Statistics
Descriptive statistics summarize the main characteristics of a dataset using statistical measures.
Hypothesis Testing
Hypothesis testing is used to determine whether a pattern or relationship in data is statistically significant
- Anova
- ANOVA Table
- McNemars test
- Breusch Test
- Omni Normality Test
- Jarque–Bera Test
- Durbin–Watson Test
Time Series Analysis
Time series analysis is used for data that changes over time like stock prices, sales or weather data. Statsmodels includes several models to handle such patterns.
- AR/MA Models
- ARIMA
- Time Series Modeling
- SARIMA for Time series Forecasting
- Exponential smoothening for Time Series
Commonly Used Models in StatsModels
| **Model Type | **Function | **Use Case |
|---|---|---|
| Linear Regression | OLS() | Predict continuous variables |
| Logistic Regression | Logit() | Classification problems |
| Generalized Linear Models | GLM() | Flexible modeling with link functions |
| Time Series Models | ARIMA(), SARIMAX() | Forecasting |
| ANOVA | anova_lm() | Comparing multiple groups |
| Mixed Linear Models | MixedLM() | Hierarchical or grouped data |