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Intermediate Regression in R

4.4+
21 reviews
Intermediate

Learn to perform linear and logistic regression with multiple explanatory variables.

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4 Hours14 Videos50 Exercises
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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. This course builds on the skills you gained in "Introduction to Regression in R", covering linear and logistic regression with multiple explanatory variables. Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, Taiwan house prices and customer churn modeling, and more. By the end of this course, you’ll know how to include multiple explanatory variables in a model, understand how interactions between variables affect predictions, and understand how linear and logistic regression work.
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In the following Tracks

Certification Available

Associate Data Scientist in R

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Machine Learning Scientist with R

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Statistician with R

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  1. 1

    Parallel Slopes

    Free

    Extend your linear regression skills to "parallel slopes" regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.

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    Parallel slopes linear regression
    50 xp
    Fitting a parallel slopes linear regression
    100 xp
    Interpreting parallel slopes coefficients
    100 xp
    Visualizing each explanatory variable
    100 xp
    Visualizing parallel slopes
    100 xp
    Predicting parallel slopes
    50 xp
    Predicting with a parallel slopes model
    100 xp
    Manually calculating predictions
    100 xp
    Assessing model performance
    50 xp
    Comparing coefficients of determination
    100 xp
    Comparing residual standard error
    100 xp
  2. 3

    Multiple Linear Regression

    See how modeling, and linear regression in particular, makes it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.

    Play Chapter Now
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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

Machine Learning Scientist with R

Go To Track

Statistician with R

Go To Track

In other tracks

Statistics Fundamentals with RSupervised Machine Learning in R

Collaborators

Collaborator's avatar
Maggie Matsui
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.
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Don’t just take our word for it

*4.4
from 21 reviews
62%
24%
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  • Li D.
    8 months

    Great course for beginners

  • Jamie S.
    about 1 year

    Excellent regression practice! Now if only my stats knowledge were up to date :(

  • Seung-Woo J.
    about 1 year

    Quality lecture and activities!

  • Nicolas F.
    about 1 year

    This was such a helpful class in tandem with the introduction course. I have two graduate degrees and this was just a wonderful set of trainings in R with sound mathematical / statistical content.

  • Assif F.
    over 1 year

    i LIKED IT

"Great course for beginners"

Li D.

"Excellent regression practice! Now if only my stats knowledge were up to date :("

Jamie S.

"Quality lecture and activities!"

Seung-Woo J.

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