课程

Sampling in R

中级技能水平

更新时间 2024年8月

Master sampling to get more accurate statistics with less data.

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RProbability & Statistics4 小时15 视频51 练习4,000 经验值24,404成就声明

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课程描述

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.

先决条件

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.

Sampling and point estimates

50 经验值

Reasons for sampling

50 经验值

Simple sampling with dplyr

100 经验值

Simple sampling with base-R

100 经验值

Convenience sampling

50 经验值

Are findings from the sample generalizable?

100 经验值

Are these findings generalizable?

100 经验值

Pseudo-random number generation

50 经验值

Generating random numbers

100 经验值

Understanding random seeds

100 经验值

2

Sampling Methods

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

Simple random and systematic sampling

50 经验值

Simple random sampling

100 经验值

Systematic sampling

100 经验值

Is systematic sampling OK?

100 经验值

Stratified and weighted random sampling

50 经验值

Which sampling method?

100 经验值

Proportional stratified sampling

100 经验值

Equal counts stratified sampling

100 经验值

Weighted sampling

100 经验值

Cluster sampling

50 经验值

Benefits of clustering

50 经验值

Performing cluster sampling

100 经验值

Comparing sampling methods

50 经验值

3 kinds of sampling

100 经验值

Summary statistics on different kinds of sample

100 经验值

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.

Relative error of point estimates

50 经验值

Calculating relative errors

100 经验值

Relative error vs. sample size

50 经验值

Creating a sampling distribution

50 经验值

Replicating samples

100 经验值

Replication parameters

50 经验值

Approximate sampling distributions

50 经验值

Exact sampling distribution

100 经验值

Approximate sampling distribution

100 经验值

Exact vs. approximate

50 经验值

Standard errors and the Central Limit Theorem

50 经验值

Population & sampling distribution means

100 经验值

Population and sampling distribution variation

100 经验值

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.

Introduction to bootstrapping

50 经验值

Principles of bootstrapping

100 经验值

With or without replacement

100 经验值

Generating a bootstrap distribution

100 经验值

Comparing sampling and bootstrap distributions

50 经验值

Bootstrap statistics and population statistics

50 经验值

Sampling distribution vs. bootstrap distribution

100 经验值

Compare sampling and bootstrap means

100 经验值

Compare sampling and bootstrap standard deviations

100 经验值

Confidence intervals

50 经验值

Confidence interval interpretation

50 经验值

Calculating confidence intervals

100 经验值

Congratulations!

50 经验值

Sampling in R

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