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Kursus

Sampling di Python

MenengahTingkat Keterampilan

Diperbarui 01/2025

Mulai Kursus Gratis

PythonProbability & Statistics

4 jam

15 videos

51 Latihan

4,000 XP

53,713

Pernyataan Pencapaian

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Deskripsi Kursus

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.

Persyaratan

Introduction to Statistics in Python

1

Introduction to Sampling

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

2

Sampling Methods

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

3

Sampling Distributions

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

4

Bootstrap 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.

Introduction to bootstrapping

Principles of bootstrapping

With or without replacement?

Generating a bootstrap distribution

Comparing sampling and bootstrap distributions

Bootstrap statistics and population statistics

Sampling distribution vs. bootstrap distribution

Compare sampling and bootstrap means

Compare sampling and bootstrap standard deviations

Confidence intervals

Confidence interval interpretation

Calculating confidence intervals

Congratulations!

Sampling di Python

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