# Inference for Numerical Data in R

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

4 Hours15 Videos49 Exercises9,959 Learners3650 XPStatistical Inference Track

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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.

Welcome to the course!
50 xp
Generate bootstrap distribution for median
100 xp
Review percentile and standard error methods
50 xp
Calculate bootstrap interval using both methods
100 xp
Which method more appropriate: percentile or SE?
50 xp
Doctor visits during pregnancy
50 xp
Average number of doctor's visits
100 xp
SD of number of doctor's visits
100 xp
Re-centering a bootstrap distribution
50 xp
Test for median price of 1 BR apartments in Manhattan
100 xp
Conclude the hypothesis test on median
50 xp
Test for average weight of babies
100 xp
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.

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.

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.

In the following tracks

Statistical Inference

Collaborators

Nick CarchediNick Solomon

#### 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.

## 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