# Introduction to Regression with statsmodels in Python

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

4 Hours14 Videos53 Exercises7,281 Learners4150 XPData Scientist TrackStatistics Fundamentals Track

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## Course Description

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 you need 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. 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.

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.

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 Statistics Fundamentals

Collaborators Richie Cotton Maggie Matsui Amy Peterson #### Maarten Van den Broeck

Content Developer at DataCamp

Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. After his career as a Ph.D. 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.

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden