This preview shows pages 1–7. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Outline Explaining CIs CIs and Tests for Proportions CIs, Hypothesis Tests for the Mean, and PIs CIs, Hypothesis testing, and PIs in Regression Supplement to Reading Data in R Lab5: CIs, PIs, and Hypothesis Testing for: Proportions, Means and Linear Regression M. George Akritas M. George Akritas Lab5: CIs, PIs, and Hypothesis Testing for: Proportions, Means Outline Explaining CIs CIs and Tests for Proportions CIs, Hypothesis Tests for the Mean, and PIs CIs, Hypothesis testing, and PIs in Regression Supplement to Reading Data in R Explaining CIs CIs and Tests for Proportions Homework Lab Activity CIs, Hypothesis Tests for the Mean, and PIs Homework Lab Activity CIs, Hypothesis testing, and PIs in Regression Homework Lab Activity Supplement to Reading Data in R M. George Akritas Lab5: CIs, PIs, and Hypothesis Testing for: Proportions, Means Outline Explaining CIs CIs and Tests for Proportions CIs, Hypothesis Tests for the Mean, and PIs CIs, Hypothesis testing, and PIs in Regression Supplement to Reading Data in R I Each CI is a Bernoulli trial: It either contains the true parameter value or not. I After the CI interval is constructed, we talk about how confident we are, or what the chances are, for it to contain the true value. The following commands generate the figure in the next slide: > m = 50; n=20; p = .5; # toss 20 coins 50 times > phat = rbinom(m,n,p)/n # divide by n for proportions > SE = sqrt(phat*(1phat)/n) # compute SE > alpha = 0.10; zstar = qnorm(1alpha/2) # compute z / 2 > matplot(rbind(phat  zstar*SE, phat + zstar*SE), + rbind(1:m,1:m),type=l,lty=1) > abline(v=p) # draw vertical line at p=0.5 M. George Akritas Lab5: CIs, PIs, and Hypothesis Testing for: Proportions, Means Outline Explaining CIs CIs and Tests for Proportions CIs, Hypothesis Tests for the Mean, and PIs CIs, Hypothesis testing, and PIs in Regression Supplement to Reading Data in R 0.2 0.4 0.6 0.8 10 20 30 40 50 End points of CIs CI count M. George Akritas Lab5: CIs, PIs, and Hypothesis Testing for: Proportions, Means Outline Explaining CIs CIs and Tests for Proportions CIs, Hypothesis Tests for the Mean, and PIs CIs, Hypothesis testing, and PIs in Regression Supplement to Reading Data in R Homework Lab Activity R command for Zintervals for a proportion I With T being the number of successes in n trials, set phat=T/n and use the commands phat qnorm(0.975)*sqrt(phat*(1phat)/n) to obtain the 95% CI for p , i.e. the pair of values b p z . 025 q b p (1 b p ) n . I To obtain 90% or other CIs, adjust the 0.975 in the above command accordingly. M. George Akritas Lab5: CIs, PIs, and Hypothesis Testing for: Proportions, Means Outline Explaining CIs CIs and Tests for Proportions CIs, Hypothesis Tests for the Mean, and PIs CIs, Hypothesis testing, and PIs in Regression Supplement to Reading Data in R Homework Lab Activity Alternative CIs and Hypothesis Testing I With x being the binomial count, the command prop.test(x,n) is equivalent to the following:prop....
View
Full
Document
This note was uploaded on 01/11/2012 for the course STAT 401 taught by Professor Akritas during the Fall '00 term at Pennsylvania State University, University Park.
 Fall '00
 Akritas

Click to edit the document details