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Introduction to Regression with statsmodels in Python

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

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4 Hours14 Videos53 Exercises13,905 Learners4150 XPData Scientist TrackStatistics Fundamentals Track

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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.
  1. 1

    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.

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    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.

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  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.

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In the following tracks

Data Scientist Statistics Fundamentals


Richie Cotton
Maggie Matsui
Amy Peterson
Maarten Van den Broeck Headshot

Maarten 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.
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