Course
Introduction to Regression with statsmodels in Python
IntermediateSkill Level
Updated 03/2026Start Course for Free
Included withPremium or Teams
PythonProbability & Statistics4 hr14 videos53 Exercises4,150 XP58,890Statement of Accomplishment
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Use Python statsmodels For Linear and Logistic Regression
Linear regression and logistic regression are two of the most widely used statistical models. They act like master keys, unlocking the secrets hidden in your data. In this course, you’ll gain the skills to fit simple linear and logistic regressions.Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, including motor insurance claims, Taiwan house prices, fish sizes, and more.
Discover How to Make Predictions and Assess Model Fit
You’ll start this 4-hour course by learning what regression is and how linear and logistic regression differ, learning how to apply both. Next, you’ll learn how to use linear regression models to make predictions on data while also understanding model objects.As you progress, you’ll learn how to assess the fit of your model, and how to know how well your linear regression model fits. Finally, you’ll dig deeper into logistic regression models to make predictions on real data.
Learn the Basics of Python Regression Analysis
By the end of this course, you’ll know how to make predictions from your data, quantify model performance, and diagnose problems with model fit. You’ll understand how to use Python statsmodels for regression analysis and be able to apply the skills to real-life data sets.Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Assess the accuracy and limitations of model predictions, including the effects of extrapolation and variable transformation
- Define the roles of coefficients, residuals, R-squared, residual standard error, leverage, and Cook’s distance within regression output
- Differentiate between probability, odds ratio, log-odds, and most-likely outcome when interpreting logistic regression results and confusion matrices
- Evaluate model fit by interpreting numerical metrics and diagnostic plots for both linear and logistic regression
- Identify appropriate scenarios for applying simple linear and logistic regression with statsmodels in Python
Prerequisites
Introduction to Data Visualization with SeabornIntroduction to Statistics in Python1
Simple Linear Regression Modeling
You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.
2
Predictions and model objects
In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.
3
Assessing model fit
In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand each observation's leverage and influence to create the model.
4
Simple Logistic Regression Modeling
Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds, and quantify model performance using confusion matrices.
Introduction to Regression with statsmodels in Python
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Enroll NowFAQs
What is statsmodels in Python?
Statsmodels is a Python model providing users with functions and classes for statistical computations, including estimating statistical models, and performing statistical tests. You can use statsmodels for linear and logistic regressions, for example.
Is statsmodels better than scikit-learn?
Both statsmodels and scikit-learn can be used for regression. However, statsmodels is a statistical package that is more optimized for insight, whereas scikit-learn is a machine learning package that is more optimized for prediction. Both models have a large number of use cases, so a decision to learn or apply one over other needs to be based on your needs rather than one package being better than the other overall.
How do you do regression analysis in Python?
You can start a regression analysis by analyzing data for correlation and directionality. Once you have that information, you may want to estimate the model by fitting the line, then evaluate the usefulness and validity of the model. Check out our course to learn more about statsmodels linear regression and logistic regression.
What is the regression function in Python?
We use regression when trying to find the relationship between variables. In Python, we can use regression in machine learning to determine the relationship and predict the outcome of future events.
What are the different types of regression?
There are several types of regression, including linear regression, logistic regression, ridge regression, lasso regression, and polynomial regression.
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