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.

Vocabulaire-score varieert tussen vs. (zelf geïdentificeerde) sociale klasse

EDA voor woordenschat-score vs. sociale klasse

Veel gemiddelden visueel vergelijken

ANOVA

ANOVA voor woordenschatscore vs. (zelf geïdentificeerde) sociale klasse

Voorwaarden voor ANOVA

De normaliteitsvoorwaarde controleren

De aanname van constante variantie controleren

Post-hoc-toetsing

Bereken alpha*

Vergelijk gemiddelden per paar

Gefeliciteerd!

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

In deze cursus leer je statistische technieken gebruiken om op basis van numerieke data conclusies te trekken en schattingen te maken. We behandelen twee benaderingen voor deze veelvoorkomende taken. De eerste gebruikt bootstrapping en permutatie om op steekproeven gebaseerde toetsen en betrouwbaarheidsintervallen te maken. De tweede gebruikt theoretische resultaten en de t-verdeling om hetzelfde te bereiken. Je leert hoe (en wanneer) je een t-toets uitvoert, een betrouwbaarheidsinterval opstelt en een ANOVA doet!

Foundations of Inference in R

Leer technieken zoals bootstrapping en t-verdeling voor statistische inferenties op numerieke data.

Inferentie voor numerieke data in R

In deze cursus leer je manieren om statistische conclusies te trekken uit cijfermatige gegevens.

Statistische conclusies in R

De normaliteitsvoorwaarde controleren

Inferentie voor numerieke data in R

Praktische interactieve oefening