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

Benvenuto al corso!

Genera una distribuzione bootstrap per la mediana

Rivedi i metodi del percentile e dell'errore standard

Calcola l'intervallo bootstrap usando entrambi i metodi

Quale metodo è più appropriato: percentile o SE?

Visite mediche durante la gravidanza

Numero medio di visite mediche

Deviazione standard del numero di visite mediche

Riposizionare una distribuzione bootstrap

Test per il prezzo mediano degli appartamenti 1 BR a Manhattan

Concludi il test d'ipotesi sulla mediana

Test per il peso medio dei neonati

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.

Vocabulary score vary between vs. (self identified) social class

EDA for vocabulary score vs. social class

Comparing many means, visually

ANOVA

ANOVA for vocabulary score vs. (self identified) social class

Conditions for ANOVA

Checking the normality condition

Checking the constant variance condition

Post-hoc testing

Calculate alpha*

Compare pairwise means

Congratulations!

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 questo corso imparerai a usare tecniche statistiche per trarre inferenze e fare stime a partire da dati numerici. Useremo due approcci per affrontare questi compiti comuni. Il primo sfrutta bootstrapping e permutazioni per creare test e intervalli di confidenza basati su ri-campionamenti. Il secondo utilizza risultati teorici e la distribuzione t per ottenere lo stesso obiettivo. Imparerai come (e quando) eseguire un t-test, costruire un intervallo di confidenza e fare un'ANOVA!

Foundations of Inference in R

Impara tecniche come il bootstrapping e la distribuzione t per inferenze statistiche su dati numerici.

Inferenza per dati numerici in R

In questo corso imparerai le tecniche per fare inferenze statistiche sui dati numerici.

Inferenza statistica in R

Quale metodo è più appropriato: percentile o SE?

Inferenza per dati numerici in R

Esercizio pratico interattivo