In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

Welcome to the course!

Generate bootstrap distribution for median

Review percentile and standard error methods

Calculate bootstrap interval using both methods

Which method more appropriate: percentile or SE?

Doctor visits during pregnancy

Average number of doctor's visits

SD of number of doctor's visits

Re-centering a bootstrap distribution

Test for median price of 1 BR apartments in Manhattan

Conclude the hypothesis test on median

Test for average weight of babies

Bootstrapping for estimating a parameter

In this chapter you'll use Central Limit Theorem based techniques to estimate a single parameter from a numerical distribution. You will do this using the t-distribution.

t-distribution

When to t?

Probabilities under the t-distribution

Cutoffs under the t-distribution

Estimating a mean with a t-interval

Average commute time of Americans

Average number of hours worked

t-interval for paired data

t-interval at various levels

Understanding confidence intervals

Testing a mean with a t-test

Estimate the median difference in textbook prices

Test for a difference in median test scores

Interpret the p-value

Introducing the t-distribution

In this chapter you'll extend what you have learned so far to use both simulation and CLT based techniques for inference on the difference between two parameters from two independent numerical distributions.

Hypothesis testing for comparing two means

Evaluating the effectiveness of stem cell treatment

Evaluating the effectiveness of stem cell treatment (cont.)

Conclusion of the hypothesis test

Evaluating the relationship between smoking during pregnancy and birth weight

Bootstrap CI for difference in two means

Quantifying the relationship between smoking during pregnancy and birth weight

Median lengths of pregnancies for smoking and non-smoking mothers

Comparing means with a t-test

Hourly pay vs. citizenship status

Estimating the difference of two means using a t-interval

Inference for difference in two parameters

In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.

Perbandingan skor kosakata antar kelas sosial (diidentifikasi sendiri)

EDA untuk skor kosakata vs. kelas sosial

Membandingkan banyak mean, secara visual

ANOVA

ANOVA untuk skor kosakata vs. kelas sosial (diidentifikasi sendiri)

Kondisi untuk ANOVA

Memeriksa kondisi kenormalan

Memeriksa kondisi varians konstan

Uji post-hoc

Hitung alpha*

Bandingkan rerata berpasangan

Selamat!

Comparing many means

Chp1-vid1-boot-dist-noaxes-parantheses

Chp1-vid1-bootsamp-bootpop.001

Chp1-vid1-manhattan-rents

Chp1-vid2-boot-dist-withaxes

Chp1-vid2-perc-method.001

Chp1-vid2-perc-method.002

Chp1-vid3-boot-test.001

Chp3-vid3-hrly-rate-citizen-smaller

Chp3-vid3-hrly-rate-citizen

Chp4-vid1-class-bar

Chp4-vid1-wodrsum-hist

Gss moredays

GSS data

Manhattan rent data

Runners.001

Tdistcomparetonormaldist

Di kursus ini, Anda akan mempelajari cara menggunakan teknik statistik untuk melakukan inferensi dan estimasi menggunakan data numerik. Kursus ini menggunakan dua pendekatan untuk tugas-tugas umum tersebut. Pendekatan pertama memanfaatkan bootstrapping dan permutasi untuk membuat uji dan interval kepercayaan berbasis pengambilan ulang sampel. Pendekatan kedua menggunakan hasil teoretis dan sebaran t untuk mencapai hasil yang sama. Anda akan mempelajari cara (dan kapan) melakukan uji t, membuat interval kepercayaan, dan melakukan ANOVA!

Foundations of Inference in R

Pelajari teknik bootstrapping dan distribusi-t untuk inferensi statistik pada data numerik.

Inferensi untuk Data Numerik di R

Dalam kursus ini, Anda akan mempelajari teknik-teknik untuk melakukan inferensi statistik pada data numerik.

Penarikan Kesimpulan Statistik di R

Memeriksa kondisi kenormalan

Inferensi untuk Data Numerik di R

Latihan interaktif praktis