StatsModel Library- Tutorial

Last Updated : 03 Feb, 2025

Statsmodels is a useful Python library for doing statistics and hypothesis testing. It provides tools for fitting various statistical models, performing tests and analyzing data. It is especially used for tasks in data science ,economics and other fields where understanding data is important. It is designed to make working with statistics easier and to give you clear and reliable results. This Statsmodels tutorial will cover core features and concepts from basic to advanced divided in 4 sections:

Installing and Importing StatsModels

Before you can use Statsmodels you need to install it. This is the first step in working with the library.

1.1 Installing Statsmodels: To install Statsmodels you can use Python's package manager pip. In your command prompt or terminal type the following:

pip install statsmodels

This will download and install the Statsmodels library along with any necessary dependencies

1.2. Importing Statsmodels: Once installed you can import the library into your Python script or notebook using:

import statsmodels.api as sm

Please, refer to for more understanding: Installation of Statsmodels

Regression and Linear Models

In this section we’ll explore various types of regression and linear models that Statsmodels supports. Regression is a statistical technique used to understand relationships between variables. Statsmodels provides a range of linear models that help us understand these relationships and make predictions based on data which includes:

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.
The goal of linear regression is to find the best-fitting straight line that minimizes the difference between the actual data points and the predicted values. For example, if you're predicting house prices based on the size of the house the dependent variable is the price, and the independent variable is the size.

We will discuss how to use statsmodels using Linear Regression: Linear regression in statsmodels

Other than Linear regression we have various other models which uses statsmodel for the different types of problem Like:

Statsmodels Tools and Tests

Now that we know how to load data and fit a basic model let’s look at some common tools and statistical tests that Statsmodels provides to help us understand the data better.

1. Descriptive Statistics: It help us understand data in a simple way. Instead of looking at every number we find key patterns. We check the mean, median and the most common number called as mode. To see how spread out the numbers are we use standard deviation and variance. Statsmodels allows you to easily compute these and other statistics to understand your data’s distribution.

2. Hypothesis Testing: Hypothesis testing helps us check if something is true using data. We start with a guess called the null hypothesis which means there is no change. Then we test it using different methods. If the data strongly disagrees with the null hypothesis we accept the alternative hypothesis meaning there is a difference. Statsmodels provide various tools to perform these testing like:

Anova using Stats models
McNemars test using Statsmodels
Breusch Test in Statsmodels

Time Series Analysis

Time series analysis is used to study data that changes over time like stock prices or sales figures. In Statsmodels we have several models to analyze this type of data. Common examples include stock prices, weather patterns and sales figures. we use different models based on the data. let's understand them one by one:

AR/MA Models: These are used when the data doesn’t show any clear trend or repeating pattern. Here AR (AutoRegressive) means we look at past values to predict the current one. For example today's temperature might depend on yesterday's temperature. and MA (Moving Average) looks at the past errors or mistakes and uses them to predict the current value. It helps smooth out the data.

Please refer : AR/MA Model using Statsmodel

ARIMA: This model is used when the data has a trend meaning it’s going up or down over time like sales increasing each year. ARIMA works by removing the trend first a process called differencing then using AR/MA models to understand the data better.

Please refer, for in-depth understanding: ARIMA Model for Time Series Forecasting

To understand other methods which is used in time series forecasting refer to below:

Learning Model Building in Scikit-learn

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Article Tags :

StatsModel Library- Tutorial

Installing and Importing StatsModels

Regression and Linear Models

Statsmodels Tools and Tests

Time Series Analysis

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