x.temp = 0:9
mass= matrix(dbinom(x.temp,9,0.5),nrow=1)
colnames(mass) <- paste(0:9)
barplot(mass)

sum(dbinom(7:9,9,0.5))
2*(0.5-pnorm(750,800,40))
pnorm(700,800,40)
qnorm(c(0.025, 0.975),800,40)
qnorm(0.99,800,40)

zA=(165-150)/(sqrt(15^2/10))
1-pnorm(zA)
(1-pnorm(zA))*2

dat<-read.csv("http://www.matsuka.info/data_folder/datWA01.txt")
mean.M <-mean(dat$h[dat$gender=="M"])
sigma = 10
n.M = length(dat$h[dat$gender=="M"])
z.value=(mean.M-171)/(sqrt(sigma))


ssize = c(24,25,26,23.5,25,27,24,22,27.5,28)
ssize.mean = mean(ssize)
ssize.var = var(ssize)
N = 10
t.value=(ssize.mean-24)/(sqrt(ssize.var/N))
(1-pt(abs(t.value),df=9))*2
t.test(ssize, mu=24)

dat<- read.csv("http://www.matsuka.info/data_folder/datWA01.txt")
plot(dat$shoesize, dat$h, main="Relationship b/w shoesize and height",
     xlab = "shoesize", ylab="height", pch=19, col="red")
txt = paste("r =", round(cor(dat$shoesize,dat$h), 4))
text(22, 175, txt, cex = 1.5)
abline(h = mean(dat$h), col='blue');
abline(v = mean(dat$shoesize), col='darkgreen');
text(x = 21, y = mean(dat$h)+3,
        paste("mean height =", round(mean(dat$h),2)),
        col="blue",adj = 0)

text(x = mean(dat$shoesize)+0.2, y = 145,
        paste("mean shoesize =", round(mean(dat$shoesize),2)),
        col="darkgreen",adj = 0)

abline(lm(dat$h~dat$shoesize), lty=2, lwd=2)

plot(dat[dat$gender=='F',]$shoesize, dat[dat$gender=='F',]$h,
     main="Relationship b/w shoesize and height", xlab='shoesize', ylab='height',
     cex.lab=1.5, pch=19, col='blue', xlim=c(20,29), ylim=c(140,190))
plot(dat[dat$gender=='M',]$shoesize, dat[dat$gender=='M',]$h,
     main="Relationship b/w shoesize and height", xlab='shoesize', ylab='height',
     cex.lab=1.5, pch=15, col='green', xlim=c(20,29), ylim=c(140,190))

plot(dat[dat$gender=='F',]$shoesize, dat[dat$gender=='F',]$h,
     main="Relationship b/w shoesize and height", xlab='shoesize', ylab='height',
     cex.lab=1.5, pch=19, col='blue', xlim=c(20,29), ylim=c(140,190))
points(dat[dat$gender=='M',]$shoesize,dat[dat$gender=='M',]$h,
       pch = 15, col = 'green')
legend("topleft", c('Female','Male'), pch =c(19,15),
       col = c('blue','green'), cex = 1.5)

dat<-read.csv("http://www.matsuka.info/data_folder/tdkReg01.csv")
plot(dat, pch=20, col='blue')

dat.pca<-read.table("http://www.matsuka.info/data_folder/tdkPCA01.txt")
plot(dat.pca, pch = rownames(dat.pca), cex = 1.7, col = "blue")

dat<-read.table("http://www.matsuka.info/data_folder/aov01.txt",header=T)
summary(dat)
dat.meter = dat[,1:2]*0.01
dat.h.ext5 = dat$h+5
hANDshoe = dat$h+dat$shoesize
dat.h.meter.ext5 = dat$h*0.01+0.05

v1 = seq(-3,3,0.1)
v2 = v1^2
plot(v1, v2, col = "blue", type = "o", lty = 2, pch = 19,
     cex.lab = 1.5, lwd = 3, main = "Y=X*X", xlab = "X",
     ylab="X*X", xlim=c(-3.5,3.5), ylim=c(-0.5, 10))

# histogram
dat<- read.csv("http://www.matsuka.info/data_folder/datWA01.txt")
hist(dat$h)
hist(dat$h, breaks = 20, main = "Histogram of Height",
     xlab = "Height", col = 'blue', xlim = c(140, 190))
dens<-density(dat$h);
hist(dat$h, main = "Histogram of Height", xlab = "Height",
     xlim = c(140,190), probability = T)
lines(dens, lwd = 2, col = "red", lty=2)
plot(v1, v2, col = "blue", type = "l", pch = 19, cex.lab = 1.5,
     lwd = 3, xlab = "X", ylab="f(X)", xlim=c(-3.5,3.5), ylim=c(-0.5, 10))
lines(v1, v1^3, col='red',lwd = 3)
legend("bottomright", c("x^2","x^3"), col=c('blue','red'), lwd=2)
legend(-3,0.5, c("x^2","x^3"), col=c('blue','red'), lwd=2)

boxplot(dat$h,main="Boxplot of Height", ylab="Height", col='cyan', ylim=c(140,190))
boxplot(dat$h,main="Boxplot of Height", xlab="Height", col="orange", horizontal=T)

boxplot(dat$h ~ dat$gender,
        main="Distribution of Height by Gender",
        ylab="Gender", xlab="Height", col=c('blue','cyan'),
        ylim=c(140,190), horizontal=T)

dat<-read.table("http://www.matsuka.info/data_folder/aov01.txt")
boxplot(dat$h ~ dat$gender + dat$affil,
        main="Distribution of  Height by Gender and Affiliation",
        ylab="Gender x Affiliation", xlab="Height",
       col=c('blue','cyan','red','magenta'), ylim=c(140,190),horizontal=T)

interaction.plot(dat$gender,
                 dat$affil,
                 dat$h,
                 pch=c(20,20),
                 col=c("skyblue","orange"),
                 xlab="gender", ylab="height",
                 lwd=3,type='b',cex=2,
                 trace.label="Affiliation")

par(mfrow=c(1,2))
hist(dat[dat$gender=="F",]$h, main="Dist. of Height for Female Participants",
     xlab="Height", xlim=c(140,190), probability=T)

dens.F = density(dat[dat$gender=='F',]$h)

lines(dens.F, col='blue',lwd=2)

#２つ目のhistogram
hist(dat[dat$gender=="M",]$h, main="Dist. of Height for Male
     Participants", xlab="Height", xlim=c(140,190), probability=T,ylim=c(0,0.08))

dens.M = density(dat[dat$gender=='M',]$h)

lines(dens.M, col='green', lwd=2)

par(mfrow=c(1,1))
plot(dens.F,col='blue',lwd=2, ylab='density', xlim=c(140,190),
     main="Dist. of Height by gender",xlab='Height')
lines(dens.M,col='green',lwd=2)
legend("topleft", c('Female','Male'), col=c('blue','green'), cex=1.5,lwd=2)
text(157.5, 0.04, 'Female', col='blue', cex=2)
text(170, 0.04,'Male', col='green', cex=2)
plot(dat$shoesize, dat$h, main="Relationship b/w shoesize and height",
   xlab = "shoesize", ylab="height", pch=19, col="red")

txt = paste("r =", round(cor(dat$shoesize,dat$h), 3))
text(22, 175, txt, cex = 1.5)

plot(dat[dat$gender=='F',]$shoesize, dat[dat$gender=='F',]$h,
     main="Relationship b/w shoesize and height", xlab='shoesize', ylab='height',
     cex.lab=1.5, pch=19, col='blue', xlim=c(20,29), ylim=c(140,190))
lines(dat[dat$gender=='M',]$shoesize,dat[dat$gender=='M',]$h,
      type = 'p', pch = 15, col = 'green')
legend("topleft", c('Female','Male'), pch =c(19,15),
       col = c('blue','green'), cex = 1.5)