#1 Modestas Sienauskas v = 8, n = 10
#1)
v = 8
n = 10
p = n/(v+n)

x = 0:10

sk <- dbinom(x, n, p)

vid <- mean(sk)
sn <- sd(sk)

#2)

F <- pbinom(x, n, p)

#3)

par(mfrow=c(2,1))

x <- seq(0, n, 1)

plot(x, sk, type="l", lwd = 1, main="Skirstinio daugiakampis")
plot(x, F, type="l", lwd = 1, main="Paskirstymo funkcija")

#4)

k <- (n*p+p) - (n*p-p)
k
labiausiaiTiketinas <-dbinom(n-2,n,p) + dbinom(n-1,n,p) + dbinom(n, n, p)
labiausiaiTiketinas

#2 uzduotis -----------------------------------------

x2 <- seq(0, 9, 1)
plotf <- dnorm(x2, 8, 10/10)

plot(x2, plotf, type="l", lwd = 1, ylab="p(x)", xlab="x")
p1 <- c(5, 5, 6, 7, 8, 8)
p2 <- c(0, plotf[6], plotf[7], plotf[8], plotf[9], 0)

polygon(p1, p2, col="yellow", border = "blue")

pIntervale <- pnorm(8, 6, 1.2) - pnorm(5, 6, 1.2)

#3 uzduotis --------------------------------------
#1)

y <- rbinom(n*50,(v*2), p)

barplot(table(y)/(v*2))

#2)

vid <- v
st <- abs(v-2.5)
y1 <- rnorm(n*50, vid, st)

hist(y1, freq=FALSE)

#3)

st <- st*st
y2 <- pnorm(n*50, vid, st)
mean(y2)
var(y2)

#4----------------------------------------------
duomenys <- read.table("liquor5.csv", sep=",", dec=".", header = T)

#1 Kintamuju tipu nustatymas
str(duomenys)

#2
v2 <- duomenys[,2];v2
v3 <- duomenys[,3];v3

#3
table(v2)

par(mfrow=c(1,2))

barplot(table(v2), xlab="Metai", ylab="Kiekis", horiz=FALSE, main="Stulpeline diagrama", )

pie(table(v2), main="Skrituline diagrama")

Mode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}

Mode(v2)

#4 int- kiekybinins diskretus, num - kiekybinis tolydus, factor - kokybinis kint

par(mfrow=c(2,1))

hist(v3, freq=FALSE, main="Histograma")

boxplot(v3, horizontal = TRUE, main="Staciakampiu diagrama")


#5



#6