Skip to main content
HomeRIntroduction to Regression in R

Introduction to Regression in R

4.6+
26 reviews
Intermediate

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

Start Course for Free
4 Hours14 Videos52 Exercises
50,725 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

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.
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 R

Go To Track

Statistician with R

Go To Track

Statistics Fundamentals with R

Go To Track
  1. 1

    Simple Linear Regression

    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 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
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 R

Go To Track

Statistician with R

Go To Track

Statistics Fundamentals with R

Go To Track

Datasets

Taiwan Real EstateAd conversion dataChurn data

Collaborators

Collaborator's avatar
Maggie Matsui
Collaborator's avatar
Amy Peterson
Collaborator's avatar
Adel Nehme
Richie Cotton HeadshotRichie Cotton

Data Evangelist at DataCamp

Richie is a Data Evangelist 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

Don’t just take our word for it

*4.6
from 26 reviews
77%
19%
0%
0%
4%
Sort by
  • Lorenzo A.
    3 months

    Good

  • Louis B.
    5 months

    This covered the basics well. The instructor seemed interested in the material.

  • Edmundo M.
    6 months

    I am amazed of the ability of the instructor to pack fundamentals of regression, R and related packages (broom) in just four hours of instruction. This course if phenomenal.

  • Héctor P.
    6 months

    It’s a great course statistic. A good combination of theory and practice.

  • Thomas H.
    6 months

    Well-designed, well-taught.

"Good"

Lorenzo A.

"This covered the basics well. The instructor seemed interested in the material."

Louis B.

"I am amazed of the ability of the instructor to pack fundamentals of regression, R and related packages (broom) in just four hours of instruction. This course if phenomenal."

Edmundo M.

Join over 13 million learners and start Introduction to Regression in R 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.