# Introduction to Regression with statsmodels in Python

4.5+
27 reviews
Intermediate

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

4 Hours14 Videos53 Exercises
23,995 Learners

or

## 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

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.

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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.

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.

4. 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 ratios, and quantify model performance using confusion matrices.

In the following tracks

Data Scientist with PythonData Scientist Professional with PythonStatistics Fundamentals with Python

Collaborators  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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## Don’t just take our word for it

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• Noel C.
5 days

Excellent course!

3 months

Good course

• Mauro B.
3 months

• Victor C.
4 months

Models, preprocessing, transformations, visualizations, and performance. It's perfect.

• YU-PEI L.
5 months

Wonderful course

"Excellent course!"

Noel C.

"Good course"

Mauro B.