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Introduction to Regression in R

14 reviews

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

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4 Hours14 Videos52 Exercises36,471 Learners

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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. 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.

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    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.

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  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.

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In the following tracks

Data Scientist with RData Scientist Professional with RStatistician with RStatistics Fundamentals with R


Maggie Matsui
Amy Peterson
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.
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Don’t just take our word for it

from 14 reviews
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  • Marcos N.
    28 days

    Great intro course

  • Kaisar B.
    3 months

    Awesome! :D

  • Nicolas F.
    3 months

    It is always so helpful and concise with Mr. Cotton.

  • Cyntia P.
    3 months

    It's a must !

  • Edwin A.
    6 months

    This is a great introductory course for learn about Regression using R.

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"Great intro course"

Marcos N.

"Awesome! :D"

Kaisar B.

"It is always so helpful and concise with Mr. Cotton."

Nicolas F.

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