Learn what sampling is and why it is so powerful. You’ll also learn about the problems caused by convenience sampling and the differences between true randomness and pseudo-randomness.

Sampling and point estimates

Reasons for sampling

Simple sampling with pandas

Simple sampling and calculating with NumPy

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

It’s time to get hands-on and perform the four random sampling methods in Python: 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

Comparing point estimates

Sampling Methods

Let’s test your sampling. In this chapter, you’ll discover 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

Generating an approximate sampling distribution

Exact vs. approximate

Standard errors and the Central Limit Theorem

Population & sampling distribution means

Population & sampling distribution variation

Sampling Distributions

You’ll get to grips with resampling to perform bootstrapping and estimate variation in an unknown population. You’ll learn the difference between sampling distributions and bootstrap distributions using resampling.

Pengantar bootstrapping

Opsi

Benar

Salah

Prinsip-prinsip bootstrapping

Dengan atau tanpa pengembalian

Dengan pengembalian

Tanpa pengembalian

Dengan atau tanpa pengembalian?

Membuat distribusi bootstrap

Membandingkan distribusi sampel dan bootstrap

Statistik bootstrap dan statistik populasi

Distribusi sampling vs. distribusi bootstrap

Bandingkan rataan 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

Sampling di Python adalah dasar dari statistika inferensial dan pengujian hipotesis. Ini merupakan keterampilan yang ampuh dalam analisis survei dan desain eksperimen untuk menarik kesimpulan tanpa mensurvei seluruh populasi. Dalam kursus Sampling di Python ini, Anda akan mempelajari kapan menggunakan sampling dan bagaimana melakukan jenis-jenis sampling umum—mulai dari sampling acak sederhana hingga metode yang lebih kompleks seperti sampling berstrata dan sampling klaster. Dengan menggunakan himpunan data dunia nyata, termasuk penilaian kopi, lagu Spotify, dan attrition karyawan, Anda akan belajar memperkirakan statistik populasi dan mengukur ketidakpastian pada perkiraan Anda dengan membuat distribusi sampling dan distribusi bootstrap.

Introduction to Statistics in Python

Belajar menarik kesimpulan dari data terbatas dengan Python dan statistik, termasuk berbagai teknik sampling.

Sampling di Python

Associate Data Scientist  dalam Python

Analis Data  dalam Python

Dasar-Dasar Statistik  dalam Python

Interpretasi interval kepercayaan

Sampling di Python

Latihan interaktif praktis