Skip to main content
HomePythonIntroduction to Regression with statsmodels in Python

Introduction to Regression with statsmodels in Python

4.4+
36 reviews
Intermediate

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

Start Course for Free
4 Hours14 Videos53 Exercises
33,094 LearnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
GroupTraining 2 or more people?Try DataCamp For Business

Loved by learners at thousands of companies


Course Description

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.
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.

In the following Tracks

Certification Available

Associate Data Scientist in Python

Go To Track

Statistics Fundamentals with Python

Go To Track
  1. 1

    Simple Linear Regression Modeling

    Free

    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.

    Play Chapter Now
    A tale of two variables
    50 xp
    Which one is the response variable?
    50 xp
    Visualizing two numeric variables
    100 xp
    Fitting a linear regression
    50 xp
    Estimate the intercept
    50 xp
    Estimate the slope
    50 xp
    Linear regression with ols()
    100 xp
    Categorical explanatory variables
    50 xp
    Visualizing numeric vs. categorical
    100 xp
    Calculating means by category
    100 xp
    Linear regression with a categorical explanatory variable
    100 xp
  2. 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.

    Play Chapter Now
  3. 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.

    Play Chapter Now
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Certification Available

Associate Data Scientist in Python

Go To Track

Statistics Fundamentals with Python

Go To Track

Datasets

Customer churn dataTaiwan real estate dataAd conversion dataS&P 500 dataFish measurement data

Collaborators

Collaborator's avatar
Amy Peterson
Collaborator's avatar
Maggie Matsui
Collaborator's avatar
Richie Cotton
Maarten Van den Broeck HeadshotMaarten Van den Broeck

Senior Content Developer at DataCamp

Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. He is also a certified Power BI and Tableau data analyst. After his career as a PhD researcher at KU Leuven, he wished that he had discovered DataCamp sooner. He loves to combine education and data science to develop DataCamp courses. In his spare time, he runs a symphonic orchestra.
See More

Don’t just take our word for it

*4.4
from 36 reviews
69%
17%
6%
6%
3%
Sort by
  • Sue D.
    about 1 month

    Interesting course and awesome instructor!

  • Andreas P.
    4 months

    A great course with a clear speaker and some good examples. One issue: in part 2, it became a tad more complicated, it would be better if there was more time or more examples to explain prediction model elements more clearly. I particularly enjoyed part 4, with the measures of the confusion matrix.

  • Ana U.
    5 months

    Simple regression analysis in Python is an extraordinary course. The instructor is wonderful. The exercises are challenging and created to give the student a sequence of set-by-step learning process. I learned and enjoy this course so much that I already register for the following course.

  • Laura L.
    5 months

    Inspiring

  • Idens M.
    8 months

    Good course

"Interesting course and awesome instructor!"

Sue D.

"A great course with a clear speaker and some good examples. One issue: in part 2, it became a tad more complicated, it would be better if there was more time or more examples to explain prediction model elements more clearly. I particularly enjoyed part 4, with the measures of the confusion matrix."

Andreas P.

"Simple regression analysis in Python is an extraordinary course. The instructor is wonderful. The exercises are challenging and created to give the student a sequence of set-by-step learning process. I learned and enjoy this course so much that I already register for the following course."

Ana U.

FAQs

Join over 14 million learners and start Introduction to Regression with statsmodels in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.