x<-0:9
x

sample(x)

mean(x)

mean(sample(x))

sample(rep(c("A","B","C","D"),5))

sample(x,replace=T)

table(sample(x,replace=T))

table(sample(x,replace=T))

for (i in 1:6) print (mean(sample(x,replace=T)))

xmeans<-numeric(10000)

for (i in 1:10000) xmeans[i]<-mean(sample(x,replace=T))

mean(xmeans)

hist(xmeans)

quantile(xmeans,c(.025,.975))

se<-sqrt(var(x)/length(x))

tvalue<-qt(.975,length(x))

ci<-tvalue*se
ci

mean(x)+ci

mean(x)-ci

attach(Light)
names(Light)

mean(speed)

d<-numeric(10000)
for (i in 1:10000) d[i]<-mean(sample(speed,replace=T))

hist(d)
quantile(d,.975)

bootstrap(speed,mean)

sqrt(var(speed)/length(speed))

bs<-bootstrap(speed,mean)
plot(bs)
summary(bs)

jk<-numeric(20)
for (i in 1:20) jk[i]<-mean(speed[-i])
hist(jk)

jackknife(speed,mean)

attach(regression)
names(regression)

bs<-bootstrap(regression,coef(lm(growth~tannin)))
bs

jack.after.bootstrap(bs)

attach(slugsurvey)
names(slugsurvey)

levels(field)

tapply(slugs,field,mean)

field

sample(field)

diff<-numeric(10000)
for (i in 1:10000) {
fieldname<-sample(field)
diff[i]<-tapply(slugs,fieldname,mean)[2]-tapply(slugs,fieldname,mean)[1] }

quantile(diff,0.975)

quantile(diff,0.995)
nursery<-slugs[field=="Nursery"]
rookery<-slugs[field=="Rookery"]
mean(nursery)

mean(rookery)

nmeans<-numeric(10000)
for (i in 1:10000) nmeans[i]<-mean(sample(nursery,replace=T))
quantile(nmeans,c(.025,.975))

rmeans<-numeric(10000)
for (i in 1:10000) rmeans[i]<-mean(sample(rookery,replace=T))
quantile(rmeans,c(.025,.975))

attach(factories)
names(factories)

tapply(rate,factory,mean)

tapply(rate,treatment,mean)

model<-aov(rate~treatment+Error(factory))
summary(model)

treats<-NULL
for(i in 1:7) treats<-c(treats,sample(levels(treatment)))

treats<-factor(treats)

summary(model)[[2]]$"F Value"[1]

d<-numeric(1000)
for(j in 1:1000) {
treats<-NULL
for(i in 1:7) treats<-c(treats,sample(levels(treatment)))
treats<-factor(treats)
d[j]<-summary(aov(rate~treats))$"F Value"[1] }

hist(d)

max(d)

quantile(d,0.95)

qf(.95,4,24)

