# Introduction to Regression in R

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

4 Hours14 Videos52 Exercises22,113 Learners4050 XPData Scientist TrackStatistician TrackStatistics Fundamentals Track

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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

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.

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.

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.

4. 4

### Simple logistic regression

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

In the following tracks

Data ScientistStatisticianStatistics Fundamentals

Collaborators Maggie Matsui Adel Nehme Amy Peterson #### 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.

## 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