P-Value
Central Lmit Theorem(CLT)
mean(null>diff)
hist(null)
qqnorm(null)
qqline(null)
pops<-read.cssv("mice_pheno.csv")
hed(pops)
hf<- pops[popsSDiet=="hf"&popsSSex=="F",3]
chow<-pops[popsSDiet=="chow"&popsSSex=="F",3]
mean(hf)-mean(chow)
x<- sample(hf,12)
y<-sample(chow,12)
mean(x)_mean(y)
Ns<-c(3,5,10,25)
B<-10000
res<-sapply(Ns,funtion(n){sapply(1:8,function(j){mean(sample(hf,n))})})
lbrary(rafalib)
mypar2(2,2)
未完
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sample variance
sample standard deviations
confidence intervals
t-statics:
开始编辑
dat<-read.csv("femaleMiceWeights.csv")
dat
control <- dat[1:12,2]
treatment<-dat[12+1:12,2]
diff <- mean(treatment)-mean(control)
print(diff)
t.test(treatment,control)
sd(control)
sd(control)/sqrt(length(control))
se <- sqrt(var(treatment)/length(treatment)+var(control)/length(control))
tstat <- diff/se
1-pnorm(tstat)+pnorm(-tstat)
qqnorm(treatment)
qqline(treatment)
t.test(treatment,control)