StatsModel Library - Tutorial
Last Updated :
25 Oct, 2025
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
To read more about this article refer to: Installation of Statsmodels
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 |
Regression and Linear Models
Regression helps in studying how one variable affects another. Statsmodels offers several linear models to analyze and predict such relationships.
- Linear Regression (OLS): Ordinary Least Squares (OLS) is the most basic method for linear regression in Statsmodels. It is used to model the relationship between a dependent variable and one or more independent variables.
- For example, to predict house prices based on size price is the dependent variable and size is the independent variable.
Other commonly used regression models in Statsmodels include:
Once a model is built, Statsmodels provides tools to analyze data more effectively.
1. Descriptive Statistics: These help summarize data using measures like mean, median, mode, variance and standard deviation. You can also compute robust statistics such as:
2. Hypothesis Testing: Used to verify assumptions about data. It starts with a null hypothesis (no effect) and checks whether the data supports an alternative hypothesis (a difference exists). Statsmodels supports tests like:
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:
- AR (AutoRegressive): Uses past values to predict current ones.
- MA (Moving Average): Uses past errors to improve predictions.
ARIMA: Used when data shows a trend. It removes the trend (differencing) and then applies AR and MA models for better forecasting.
For advanced forecasting, check out:
Explore
Introduction to Machine Learning
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Introduction to Statistics
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Model Evaluation and Tuning
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