Congratulations!
Sampling in R
Richie Cotton
Data Evangelist at DataCamp
Recap
Chapter 1
- Sampling basics
- Selection bias
- Pseudo-random sampling
Chapter 3
- Sample size and population parameters
- Creating sampling distributions
- Approximate vs. actual sampling dist'ns
- Central limit theorem
Chapter 2
- Simple random sampling
- Systematic sampling
- Stratified sampling
- Cluster sampling
Chapter 4
- Bootstrapping from a single sample
- Standard error
- Confidence intervals
The most important things
- The standard deviation of the sampling distribution (a.k.a. the standard error) of a statistic is well-approximated by the standard deviation of the bootstrap distribution of a statistic.
- When calculating confidence intervals, it's OK to assume that bootstrap distributions are approximately normally distributed.
Let's practice!
Sampling in R
