Learn why hypothesis testing is useful, and step through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-p-values, and false negative and false positive errors. The Stack Overflow survey and late medical shipments datasets are introduced.

Hypothesis tests and z-scores

Uses of A/B testing

Calculating the sample mean

Calculating a z-score

p-values

Criminal trials and hypothesis tests

Left tail, right tail, two tails

Calculating p-values

Statistical significance

Decisions from p-values

Calculating confidence intervals

Type I and type II errors

Introduction to Hypothesis Testing

Learn how to test for differences in means between two groups using t-tests, and how to extend this to more than two groups using ANOVA and pairwise t-tests.

Melakukan uji t

Alur kerja pengujian hipotesis

Statistik uji rata-rata dua sampel

Menghitung p-value dari statistik t

Mengapa t diperlukan?

Distribusi t

Dari t ke p

Uji t berpasangan

Uji t berpasangan vs. tidak berpasangan

Berpasangan

Tidak berpasangan

Apakah perlu berpasangan?

Memvisualisasikan perbedaannya

Menggunakan t.test()

Uji ANOVA

Memvisualisasikan banyak kategori

Melakukan uji ANOVA

Two-Sample and ANOVA Tests

Learn how to test for differences in proportions between two groups using proportion tests, extended it to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.

One-sample proportion tests

t for proportions?

Test for single proportions

Two-sample proportion tests

Test of two proportions

prop_test() for two samples

Chi-square test of independence

The chi-square distribution

How many tails for chi-square tests?

Performing a chi-square test

Chi-square goodness of fit tests

Visualizing goodness of fit

Performing a goodness of fit test

Proportion Tests

Learn about the assumptions made by parametric hypothesis tests and see how simulation-based and rank-based non-parametric tests can be used when those assumptions aren't met.

Assumptions in hypothesis testing

Common assumptions of hypothesis tests

Testing sample size

The "There is only one test" framework

There is only one test

Specifying and hypothesizing

Continuing the infer pipeline

Generating & calculating

Observed statistic and p-value

Non-parametric ANOVA and unpaired t-tests

Simulation-based t-test

Rank sum tests

Congratulations!

Non-Parametric Tests

Late Shipments

Late shipments Bootstrap Distribution

Democratic Presidential Candidates by County

StackOverflow Survey

Late Shipments w/ Freight Cost Group

Pengujian hipotesis memungkinkan Anda mengajukan pertanyaan tentang himpunan data Anda dan menjawabnya secara ketat secara statistik. Dalam kursus ini Anda akan mempelajari cara dan waktu menggunakan uji umum seperti uji t, uji proporsi, dan uji chi-square. Anda akan mendapatkan pemahaman mendalam tentang cara kerjanya dan asumsi yang mendasarinya. Anda juga akan mempelajari bagaimana berbagai uji hipotesis saling berkaitan menggunakan kerangka "Hanya ada satu uji", serta menggunakan uji nonparametrik yang memungkinkan Anda menghindari persyaratan uji hipotesis tradisional. Sepanjang kursus, Anda akan mengeksplorasi survei pengguna Stack Overflow dan himpunan data keterlambatan pengiriman perlengkapan medis.

Sampling in R

Pelajari uji hipotesis di R, termasuk kapan dan cara menggunakan uji t, uji proporsi, dan uji chi-kuadrat.

Pengujian Hipotesis di R

Associate Data Scientist di R

Analis Data  di R

Ahli Statistik di R

Dasar-Dasar Statistik di R

Identifikasi parameter populasi yang menjadi subjek hipotesis.

Tentukan statistik uji (terstandarisasi) dan sebaran nol yang sesuai.

Ukur bukti terhadap hipotesis nol dan bandingkan dengan tingkat signifikansi.

Tafsirkan hasilnya dalam konteks permasalahan awal.

Alur kerja pengujian hipotesis

Pengujian Hipotesis di R

Latihan interaktif praktis