Inference for Numerical Data in R

In this course you'll learn techniques for performing statistical inference on numerical data.
Start Course for Free
4 Hours15 Videos49 Exercises6,449 Learners
3650 XP

Create Your Free Account

GoogleLinkedInFacebook
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of companies


Course Description

In this course, you'll learn how to use statistical techniques to make inferences and estimations using numerical data. This course uses two approaches to these common tasks. The first makes use of bootstrapping and permutation to create resample based tests and confidence intervals. The second uses theoretical results and the t-distribution to achieve the same result. You'll learn how (and when) to perform a t-test, create a confidence interval, and do an ANOVA!

  1. 1

    Bootstrapping for estimating a parameter

    Free
    In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.
    Play Chapter Now
  2. 2

    Introducing the t-distribution

    In this chapter you'll use Central Limit Theorem based techniques to estimate a single parameter from a numerical distribution. You will do this using the t-distribution.
    Play Chapter Now
  3. 3

    Inference for difference in two parameters

    In this chapter you'll extend what you have learned so far to use both simulation and CLT based techniques for inference on the difference between two parameters from two independent numerical distributions.
    Play Chapter Now
  4. 4

    Comparing many means

    In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.
    Play Chapter Now
In the following tracks
Statistical Inference
Collaborators
Nick CarchediNick Solomon
Mine Cetinkaya-Rundel Headshot

Mine Cetinkaya-Rundel

Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio
Mine is the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University as well as a Professional Educator at RStudio. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and teaches the popular Statistics with R MOOC on Coursera.
See More

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
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA