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Intermediate Regression in R

4.3+
23 reviews
Intermediate

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

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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 in R

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Statistician in 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.

    Play Chapter Now
    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. 2

    Interactions

    Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.

    Play Chapter Now
    Models for each category
    50 xp
    One model per category
    100 xp
    Predicting multiple models
    100 xp
    Visualizing multiple models
    100 xp
    Assessing model performance
    100 xp
    One model with an interaction
    50 xp
    Specifying an interaction
    100 xp
    Interactions with understandable coeffs
    100 xp
    Making predictions with interactions
    50 xp
    Predicting with interactions
    100 xp
    Manually calculating predictions with interactions
    100 xp
    Simpson's Paradox
    50 xp
    Modeling eBay auctions
    100 xp
    Modeling each auction type
    100 xp
  3. 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
    Two numeric explanatory variables
    50 xp
    3D visualizations
    100 xp
    Modeling 2 numeric explanatory variables
    100 xp
    Including an interaction
    100 xp
    More than 2 explanatory variables
    50 xp
    Visualizing many variables
    100 xp
    Different levels of interaction
    100 xp
    Predicting again
    100 xp
    How linear regression works
    50 xp
    The sum of squares
    50 xp
    Linear regression algorithm
    100 xp
  4. 4

    Multiple Logistic Regression

    Extend your logistic regression skills to multiple explanatory variables. Understand the logistic distribution, which underpins this form of regression. Finally, implement your own logistic regression algorithm.

    Play Chapter Now
    Multiple logistic regression
    50 xp
    Visualizing multiple explanatory variables
    100 xp
    Logistic regression with 2 explanatory variables
    100 xp
    Logistic regression prediction
    100 xp
    Confusion matrix
    100 xp
    The logistic distribution
    50 xp
    Cumulative distribution function
    100 xp
    Inverse cumulative distribution function
    100 xp
    binomial family argument
    100 xp
    Logistic distribution parameters
    50 xp
    How logistic regression works
    50 xp
    Likelihood & log-likelihood
    50 xp
    Logistic regression algorithm
    100 xp
    Congratulations
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Certification Available

Associate Data Scientist in R

Go To Track

Machine Learning Scientist in R

Go To Track

Statistician in R

Go To Track

In other tracks

Statistics Fundamentals in RSupervised Machine Learning in R

datasets

Taiwan real estate priceseBay Palm Pilot auctionsBank churn

collaborators

Collaborator's avatar
Maggie Matsui

audio recorded by

Richie Cotton's avatar
Richie Cotton

prerequisites

Introduction to Regression in R
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

*4.3
from 23 reviews
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  • Dan L.
    3 months

    I had 1 thin in mind about lm(or glm) function: for complex interactions such as "lm(mass_g ~ length_cm * height_cm * species *0, data = fish) " for eample, how can I manully calculate the predictions or get a function ( exact or aproximation ) for the predictions?

  • Vern V.
    7 months

    Like most of the Datacamp short courses, this one is concise and clear. The course covers a lot of ground on Linear and Logistic Regression, but if one has some background then one learns how to get it done with R.

  • Héctor P.
    7 months

    "The course is very interesting. The instructor develops the topics very well and the exercises are very challenging."

  • Li D.
    about 1 year

    Great course for beginners

  • Rudrajit G.
    about 1 year

    It's fun & interesting

"I had 1 thin in mind about lm(or glm) function: for complex interactions such as "lm(mass_g ~ length_cm * height_cm * species *0, data = fish) " for eample, how can I manully calculate the predictions or get a function ( exact or aproximation ) for the predictions?"

Dan L.

"Like most of the Datacamp short courses, this one is concise and clear. The course covers a lot of ground on Linear and Logistic Regression, but if one has some background then one learns how to get it done with R."

Vern V.

""The course is very interesting. The instructor develops the topics very well and the exercises are very challenging.""

Héctor P.

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