Skip to main content

Introduction to Regression in R

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

Start Course for Free
4 Hours14 Videos52 Exercises22,113 Learners4050 XPData Scientist TrackStatistician TrackStatistics Fundamentals Track

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

Linear regression and logistic regression are the two most widely used statistical models and act like master keys, unlocking the secrets hidden in datasets. 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


    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 variables
    100 xp
    Fitting a linear regression
    50 xp
    Estimate the intercept
    50 xp
    Estimate the slope
    50 xp
    Linear regression with lm()
    100 xp
    Categorical explanatory variables
    50 xp
    Visualizing numeric vs. categorical
    100 xp
    Calculating means by category
    100 xp
    lm() 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 the leverage and influence of each observation used to create the model.

    Play Chapter Now

In the following tracks

Data ScientistStatisticianStatistics Fundamentals


maggiematsuiMaggie MatsuiAAN94Adel Nehmeamy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson
Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp

Richie is a Learning Solutions Architect at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
See More

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
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA