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| ## true effect size: | |
| D<-seq(1,250,by=1) | |
| ## SE from a study: | |
| se<-46 | |
| ## sample size | |
| n<-37 | |
| ## typical SD: | |
| stddev<-se*sqrt(n) | |
| ## rejection value (absolute) under null: |
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| \documentclass[10pt,a4paper]{article} | |
| %% packages | |
| \usepackage{a4wide,verbatim,Sweave,url} | |
| %% new environments | |
| \newenvironment{question}{\item}{} | |
| \newenvironment{solution}{\comment}{\endcomment} | |
| \newenvironment{answerlist}{\renewcommand{\labelenumi}{(\alph{enumi})}\begin{enumerate}}{\end{enumerate}} |
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| \documentclass[10pt,a4paper]{article} | |
| %% packages | |
| \usepackage{a4wide,verbatim,Sweave,url} | |
| %% new environments | |
| \newenvironment{question}{\item \textbf{Problem}\newline}{} | |
| \newenvironment{solution}{\textbf{Solution}\newline}{} | |
| \newenvironment{answerlist}{\renewcommand{\labelenumi}{(\alph{enumi})}\begin{enumerate}}{\end{enumerate}} |
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| <<echo=FALSE, results=hide>>= | |
| ## DATA GENERATION | |
| mean.val<-round(rnorm(1,mean=100,sd=100),digits=0) | |
| sd.val<-round(rnorm(1,mean=100,sd=10),digits=0) | |
| upper<-round(rnorm(1,mean=100,sd=100)+50,digits=0) | |
| lower<-round(rnorm(1,mean=100,sd=100)-100,digits=0) | |
| sol<-pnorm(upper,mean=abs(mean.val),sd=abs(sd.val))-pnorm(lower,mean=abs(mean.val),sd=abs(sd.val)) | |
| sol<-round(sol,digits=3) |
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| ## Load library: | |
| library("exams") | |
| ## exam questions: | |
| myexamlist<-list("pnorm1","sesamplesize1multiplechoice") | |
| ## output directory | |
| ## create new test dir if one does not exist: | |
| files.list<-system("ls",intern=TRUE) |
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| <<echo=FALSE, results=hide>>= | |
| ## DATA GENERATION | |
| mean.val<-round(rnorm(1,mean=100,sd=10),digits=0) | |
| sd.val<-round(abs(rnorm(1,mean=10,sd=10)),digits=0) | |
| n<-round(abs(rnorm(1,mean=100,sd=10)),digits=0)+1 | |
| se1<-round(sd.val/sqrt(n),digits=3) | |
| se2<-round(sd.val/sqrt(n^2),digits=3) | |
| questions <- character(5) |
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| library(lme4) | |
| ## load data | |
| hindi10<-read.table("datacode/hindi10a.txt",header=TRUE) | |
| ## skipping: 1 if word is skipped, 0 otherwise | |
| skip<-ifelse(hindi10$TFT==0,1,0) | |
| hindi10$skip<-skip | |
| summary(hindi10$word_complex) | |
| ## make a sum contrast for illustration |
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| nsim<-100000 | |
| sd_est<-width<-rep(NA,nsim) | |
| for(i in 1:nsim){ | |
| y<-rnorm(10) | |
| sd_est[i]<-sd(y) | |
| ci<-t.test(y)$conf.int | |
| width[i]<-ci[2]-ci[1] | |
| } | |
| plot(width,sd_est) |
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