Sampling in R
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Learn to draw random samples in R, compare four sampling designs, and measure estimate uncertainty with sampling and bootstrap distributions.
Sampling sits at the heart of inference statistics and hypothesis testing, and how a sample is drawn decides whether its findings generalize. You draw random samples in R, see the problems convenience samples cause, and compare simple, systematic, stratified and cluster designs. The later work asks how close an estimate lands to the population value, using relative error, the Central Limit Theorem, resampling and confidence intervals, with data on coffee ratings, Spotify songs and employee attrition.
What You Will Achieve
- Draw simple, systematic, stratified and cluster samples in R and judge which design suits a dataset
- Spot when a convenience sample makes findings hard to generalize, and set random seeds so draws can be repeated
- Quantify how accurate a point estimate is with relative error and replicated samples of different sizes
- Generate a bootstrap distribution by resampling with replacement and calculate a confidence interval from it
Program Overview
Why Sample and How Randomness Works
Sets out why analysts work with samples, how to draw them in R, and why random draws beat convenient ones.
- Point estimates and reasons for sampling
- Simple sampling with dplyr and base R
- Convenience sampling and generalizable findings
- Pseudo-random numbers and random seeds
Four Random Sampling Methods
Compares the main random designs and the summary statistics each kind of sample produces.
- Simple random and systematic sampling
- Proportional and equal counts stratified sampling
- Weighted sampling
- Cluster sampling and comparing methods
Sampling Distributions and Bootstrapping
Measures how much estimates vary, first by replicating samples and then by resampling a single sample when the population is unknown.
- Relative error and sample size
- Exact and approximate sampling distributions
- Standard errors and the Central Limit Theorem
- Bootstrap distributions and confidence intervals
Certificate Awarded (Paid)
Job Outlook
Sampling and resampling skills apply in survey analysis, experimental design and everyday data analysis, wherever an estimate drawn from part of the data has to stand for the whole.
Good for: Analysts working in R who need to estimate population figures from samples and report how uncertain those estimates are.
Keep in mind: The chapters center on drawing samples and measuring estimate error, ending with bootstrap confidence intervals; survey analysis and experimental design are where these skills apply, not topics of their own.
Bottom line: It moves in a clear line through sample design, error measurement and bootstrapping, with every idea practiced in R code.
Frequently Asked Questions
Which R tools do the exercises use?
The lessons draw samples both with dplyr and with base R, so the two approaches appear side by side. Later lessons replicate samples and resample with replacement to build sampling and bootstrap distributions.
Which datasets act as populations in the exercises?
Practice uses real-world datasets on coffee ratings, Spotify songs and employee attrition, which serve as populations for drawing samples and checking how well sample statistics match population statistics.
How does a bootstrap distribution differ from a sampling distribution?
A sampling distribution comes from drawing many samples out of a known population. A bootstrap distribution resamples one sample with replacement to estimate variation when the population is unknown. The lessons compare the means and standard deviations each one gives.
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