# Correlation and Regression in R

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
4 Hours18 Videos58 Exercises73,951 Learners
4200 XP

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## Course Description

Ultimately, data analysis is about understanding relationships among variables. Exploring data with multiple variables requires new, more complex tools, but enables a richer set of comparisons. In this course, you will learn how to describe relationships between two numerical quantities. You will characterize these relationships graphically, in the form of summary statistics, and through simple linear regression models.

1. 1

### Visualizing two variables

Free
In this chapter, you will learn techniques for exploring bivariate relationships.
2. 2

### Correlation

This chapter introduces correlation as a means of quantifying bivariate relationships.
3. 3

### Simple linear regression

With the notion of correlation under your belt, we'll now turn our attention to simple linear models in this chapter.
4. 4

### Interpreting regression models

This chapter looks at how to interpret the coefficients in a regression model.
5. 5

### Model Fit

In this final chapter, you'll learn how to assess the "fit" of a simple linear regression model.
Collaborators
Nick CarchediTom Jeon
Prerequisites
Exploratory Data Analysis in R

#### Ben Baumer

Assistant Professor at Smith College
Ben is an Assistant Professor in the Statistical & Data Sciences Program at Smith College. He completed his Ph.D. in Mathematics at the Graduate Center of the City University of New York in 2012. He is an Accredited Professional Statistician™ by the American Statistical Association and was previously the Statistical Analyst for the Baseball Operations department of the New York Mets.

## 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