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Sampling in R

IntermediateSkill Level
4.7+
788 reviews
Updated 08/2024
Master sampling to get more accurate statistics with less data.
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RProbability & Statistics
4 hr
15 videos
51 Exercises
4,000 XP
24,523
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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.

Prerequisites

Introduction to Statistics in R
1

Introduction to Sampling

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.
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2

Sampling Methods

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

What sampling methods does this course cover?

You will learn four methods: simple random sampling, systematic sampling, stratified sampling, and cluster sampling, along with when each method is most appropriate.

What datasets are used for the exercises?

You will work with real-world datasets on coffee ratings, Spotify songs, and employee attrition to practice different sampling techniques and estimate population statistics.

Does this course explain bootstrap distributions?

Yes. The final chapter teaches resampling-based bootstrapping to estimate variation in an unknown population and explains how bootstrap distributions differ from sampling distributions.

What R packages will I use?

You will use dplyr for data manipulation along with base R and tidyverse tools. Prerequisites include Introduction to Statistics in R, so familiarity with basic statistical functions is expected.

How does the course measure the accuracy of sample statistics?

Chapter 3 teaches you to quantify accuracy using relative errors and to generate sampling distributions that show how much variation exists across repeated samples from the same population.

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