StatsModel Library - Tutorial

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

StatsModels Statistical Tools

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.

• Kurtosis
• Skewness
• Expected Kurtosis
• Medcouple

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