Interactive Course

Inference for Numerical Data

In this course you'll learn techniques for performing statistical inference on numerical data.

  • 4 hours
  • 15 Videos
  • 49 Exercises
  • 3,150 Participants
  • 3,650 XP

Loved by learners at thousands of top companies:

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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.

  2. 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.

  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.

  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.

  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.

  4. Comparing many means

    In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.

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Mine Cetinkaya-Rundel
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.

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