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Sampling in R

Master sampling to get more accurate statistics with less data.

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4 Hours15 Videos51 Exercises7,774 Learners4000 XPData Analyst TrackData Scientist TrackStatistician TrackStatistics Fundamentals Track

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Course Description

Sampling is a cornerstone of inference statistics and hypothesis testing. It's tremendously important in survey analysis and experimental design. This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling. Later, the course covers estimating population statistics, and quantifying uncertainty in your estimates by generating sampling distributions and bootstrap distributions. Throughout the course, you'll explore real-world datasets on coffee ratings, Spotify songs, and employee attrition.
  1. 1

    Introduction to Sampling

    Free

    Learn what sampling is and why it is useful, understand the problems caused by convenience sampling, and learn about the differences between true randomness and pseudo-randomness.

    Play Chapter Now
    Sampling and point estimates
    50 xp
    Reasons for sampling
    50 xp
    Simple sampling with dplyr
    100 xp
    Simple sampling with base-R
    100 xp
    Convenience sampling
    50 xp
    Are findings from the sample generalizable?
    100 xp
    Are these findings generalizable?
    100 xp
    Pseudo-random number generation
    50 xp
    Generating random numbers
    100 xp
    Understanding random seeds
    100 xp
  2. 2

    Sampling Methods

    Learn how to and when to perform the four methods of random sampling: simple, systematic, stratified, and cluster.

    Play Chapter Now
    Simple random and systematic sampling
    50 xp
    Simple random sampling
    100 xp
    Systematic sampling
    100 xp
    Is systematic sampling OK?
    100 xp
    Stratified and weighted random sampling
    50 xp
    Which sampling method?
    100 xp
    Proportional stratified sampling
    100 xp
    Equal counts stratified sampling
    100 xp
    Weighted sampling
    100 xp
    Cluster sampling
    50 xp
    Benefits of clustering
    50 xp
    Performing cluster sampling
    100 xp
    Comparing sampling methods
    50 xp
    3 kinds of sampling
    100 xp
    Summary statistics on different kinds of sample
    100 xp
  3. 3

    Sampling Distributions

    Learn how to quantify the accuracy of sample statistics using relative errors, and measure variation in your estimates by generating sampling distributions.

    Play Chapter Now
    Relative error of point estimates
    50 xp
    Calculating relative errors
    100 xp
    Relative error vs. sample size
    50 xp
    Creating a sampling distribution
    50 xp
    Replicating samples
    100 xp
    Replication parameters
    50 xp
    Approximate sampling distributions
    50 xp
    Exact sampling distribution
    100 xp
    Approximate sampling distribution
    100 xp
    Exact vs. approximate
    50 xp
    Standard errors and the Central Limit Theorem
    50 xp
    Population & sampling distribution means
    100 xp
    Population and sampling distribution variation
    100 xp
  4. 4

    Bootstrap Distributions

    Learn how to use resampling to perform bootstrapping, used to estimate variation in an unknown population. Understand the difference between sampling distributions and bootstrap distributions.

    Play Chapter Now
    Introduction to bootstrapping
    50 xp
    Principles of bootstrapping
    100 xp
    With or without replacement
    100 xp
    Generating a bootstrap distribution
    100 xp
    Comparing sampling and bootstrap distributions
    50 xp
    Bootstrap statistics and population statistics
    50 xp
    Sampling distribution vs. bootstrap distribution
    100 xp
    Compare sampling and bootstrap means
    100 xp
    Compare sampling and bootstrap standard deviations
    100 xp
    Confidence intervals
    50 xp
    Confidence interval interpretation
    50 xp
    Calculating confidence intervals
    100 xp
    Congratulations!
    50 xp

In the following tracks

Data Analyst Data ScientistStatisticianStatistics Fundamentals

Datasets

Coffee ratingsSpotify song attributesEmployee attrition

Collaborators

chesterismay
Dr. Chester Ismay

Prerequisites

Introduction to Statistics in R
Richie Cotton Headshot

Richie Cotton

Data Evangelist at DataCamp

Richie is a Data Evangelist at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
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