vasishth

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)

vasishth

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

vasishth

<<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)

vasishth

## 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)

vasishth

<<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)

vasishth

\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}}

vasishth

\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}}

vasishth

## 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:

vasishth

## probable effect size derived from past studies:
D<-15
## SE from the study of interest:
se<-46
stddev<-se*sqrt(37)
nsim<-10000
drep<-rep(NA,nsim)
for(i in 1:nsim){
drep[i]<-mean(rnorm(37,mean=D,sd=stddev))
}

vasishth

“He seems to have a solid stats background back in the day”

A lot of people claim a lot of things about themselves. I have met a lot of people who characterize themselves as “fluent in Japanese” (oddly, these are always Americans), where their actual on-the-ground fluency level is pretty laughable.

I couldn’t find any clear statements about what his educational background is. He says on his linkedin page: “Facultés universitaires ‘Notre-Dame de la Paix’ Ph.D., Statistics, Mathematics, Science, 1983 – 1993″, Then he lists two courses he did there, “Stochastic Geometry, Markov Processes.” I find it odd that a guy does a PhD somewhere, over 10 years, and lists two courses under that PhD.

Also, I searched for this mysterious uni I have never heard of: Facultés universitaires ‘Notre-Dame de la Paix.

I ended up at a weird Jesuits page in Belgium: