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

Reasons for sampling

Simple sampling with dplyr

Simple sampling with base-R

Convenience sampling

Are findings from the sample generalizable?

Are these findings generalizable?

Pseudo-random number generation

Generating random numbers

Understanding random seeds

Introduction to Sampling

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

Simple random and systematic sampling

Simple random sampling

Systematic sampling

Is systematic sampling OK?

Stratified and weighted random sampling

Which sampling method?

Proportional stratified sampling

Equal counts stratified sampling

Weighted sampling

Cluster sampling

Benefits of clustering

Performing cluster sampling

Comparing sampling methods

3 kinds of sampling

Summary statistics on different kinds of sample

Sampling Methods

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

Calculating relative errors

Relative error vs. sample size

Creating a sampling distribution

Replicating samples

Replication parameters

Approximate sampling distributions

Exact sampling distribution

Approximate sampling distribution

Exact vs. approximate

Standard errors and the Central Limit Theorem

Population & sampling distribution means

Population and sampling distribution variation

Sampling 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.

Pengantar bootstrapping

Bootstrap benar atau salah

Benar

Salah

Prinsip-prinsip bootstrapping

Dengan atau tanpa pengembalian

Dengan pengembalian

Tanpa pengembalian

Membuat distribusi bootstrap

Membandingkan sebaran sampling dan bootstrap

Statistik bootstrap dan statistik populasi

Distribusi sampling vs. distribusi bootstrap

Bandingkan mean sampling dan bootstrap

Bandingkan simpangan baku sampling dan bootstrap

Interval kepercayaan

Interpretasi interval kepercayaan

Menghitung interval kepercayaan

Selamat!

Bootstrap Distributions

Coffee ratings

Spotify song attributes

Employee attrition

Pengambilan sampel adalah dasar dari statistika inferensi dan pengujian hipotesis. Topik ini sangat penting dalam analisis survei dan perancangan eksperimen. Kursus ini menjelaskan kapan dan mengapa pengambilan sampel itu penting, mengajarkan Anda cara melakukan jenis pengambilan sampel yang umum, dari simple random sampling hingga metode yang lebih kompleks seperti stratified dan cluster sampling. Selanjutnya, kursus membahas cara memperkirakan statistik populasi, serta mengukur ketidakpastian dalam estimasi Anda dengan membuat sampling distributions dan bootstrap distributions. Sepanjang kursus, Anda akan mengeksplorasi himpunan data dunia nyata tentang penilaian kopi, lagu Spotify, dan turnover karyawan.

Introduction to Statistics in R

Pelajari cara melakukan sampling di R, termasuk sampling acak sederhana, stratifikasi, dan klaster.

Sampling di R

Menguasai teknik sampling untuk mendapatkan statistik yang lebih akurat dengan data yang lebih sedikit.

Associate Data Scientist di R

Analis Data  di R

Ahli Statistik di R

Dasar-Dasar Statistik di R

Interpretasi interval kepercayaan

Sampling di R

Latihan interaktif praktis