HomeRInference for Numerical Data in R

# Inference for Numerical Data in R

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

4 hours15 videos49 exercises

or

Training 2 or more people?Try DataCamp For Business

## 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!

### .css-1goj2uy{margin-right:8px;}Group.css-gnv7tt{font-size:20px;font-weight:700;white-space:nowrap;}.css-12nwtlk{box-sizing:border-box;margin:0;min-width:0;color:#05192D;font-size:16px;line-height:1.5;font-size:20px;font-weight:700;white-space:nowrap;}Training 2 or more people?

Try DataCamp for BusinessFor a bespoke solution book a demo.

Go To Track
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
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 with R

Go To Track

datasets

Chp1-vid1-boot-dist-noaxes-paranthesesChp1-vid1-bootsamp-bootpop.001Chp1-vid1-manhattan-rentsChp1-vid2-boot-dist-withaxesChp1-vid2-perc-method.001Chp1-vid2-perc-method.002Chp1-vid3-boot-test.001Chp3-vid3-hrly-rate-citizen-smallerChp3-vid3-hrly-rate-citizenChp4-vid1-class-barChp4-vid1-wodrsum-histGss moredaysGSS dataManhattan rent dataRunners.001Tdistcomparetonormaldist

collaborators

Mine Cetinkaya-Rundel

Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio

See More