x <- vector(length = 100)
x1 <- vector(length = 100)
x2 <- vector(length = 100)
x3 <- vector(length = 100)
x4 <- vector(length = 100)
x4 <- vector(length = 100)

#нормальное
n_mean <- vector (length = 1000)
n_mad <- vector (length = 1000)
n_med <- vector (length = 1000)
n_sd <- vector(length = 1000)
n_iqr <- vector(length = 1000)
n_uq <- vector(length = 1000)
n_lq <- vector(length = 1000)
n_count_mean <- 0
n_count_med <- 0
n_count_tukey <- 0
for(i in 1:1000)
{
  x2<-rnorm(100, mean = 0, sd =1)
  x2<-sort(x2)
  n_mean[i] <- mean(x2)
  n_med[i] <- median(x2)
  n_sd[i] <- sd(x2)
  n_mad[i] <- mad(x2, center = median(x2))
  n_iqr[i] <- IQR(x2)
  n_lq[i] <- x2[25]
  n_uq[i] <- x2[75]
  for (j in 1:100){
    if (((abs(x2[j] - n_mean[i]) / n_sd[i]) > 3)){
      n_count_mean = n_count_mean + 1
    }
    if (((abs(x2[j] - n_med[i]) / n_mad[i]) > 3)){
      n_count_med = n_count_med + 1
    }
    if ((x2[j] > (n_uq[i] + 1.5*n_iqr[i])) | (x2[j] < (n_lq[i] - 1.5*n_iqr[i])) ) {
      n_count_tukey = n_count_tukey + 1
    }
  }

}
boxplot(x2, horizontal = TRUE)

#равномерное
u_mean <- vector (length = 1000)
u_mad <- vector (length = 1000)
u_med <- vector (length = 1000)
u_sd <- vector(length = 1000)
u_iqr <- vector(length = 1000)
u_uq <- vector(length = 1000)
u_lq <- vector(length = 1000)
u_count_mean <- 0
u_count_med <- 0
u_count_tukey <- 0
for(i in 1:1000)
{
  x5<-runif(100, min = -sqrt(3), max = sqrt(3))
  x5<-sort(x)
  u_mean[i] <- mean(x5)
  u_med[i] <- median(x5)
  u_sd[i] <- sd(x5)
  u_mad[i] <- mad(x5, center = median(x5))
  u_iqr[i] <- IQR(x5)
  u_lq[i] <- x5[25]
  u_uq[i] <- x5[75]
  for (j in 1:100){
    if (((abs(x5[j] - u_mean[i]) / u_sd[i]) > 3)){
      u_count_mean = u_count_mean + 1
    }
    if (((abs(x5[j] - u_med[i]) / u_mad[i]) > 3)){
      u_count_med = u_count_med + 1
    }
    if ((x5[j] > (u_uq[i] + 1.5*u_iqr[i])) || (x5[j] < (u_lq[i] - 1.5*u_iqr[i])) ) {
      u_count_tukey = u_count_tukey + 1
    }
  }
}

#лаплас
l_mean <- vector (length = 1000)
l_mad <- vector (length = 1000)
l_med <- vector (length = 1000)
l_sd <- vector(length = 1000)
l_iqr <- vector(length = 1000)
l_uq <- vector(length = 1000)
l_lq <- vector(length = 1000)
l_count_mean <- 0
l_count_med <- 0
l_count_tukey <- 0
for(i in 1:1000)
{
  x4<-rlaplace(100,0,1/sqrt(2))
  x4<-sort(x4)
  l_mean[i] <- mean(x4)
  l_med[i] <- median(x4)
  l_sd[i] <- sd(x4)
  l_mad[i] <- mad(x4, center = median(x4))
  l_iqr[i] <- IQR(x4)
  l_lq[i] <- x4[25]
  l_uq[i] <- x4[75]
  for (j in 1:100){
    if (((abs(x4[j] - l_mean[i]) / l_sd[i]) > 3)){
      l_count_mean = l_count_mean + 1
    }
    if (((abs(x4[j] - l_med[i]) / l_mad[i]) > 3)){
      l_count_med = l_count_med + 1
    }
    if ((x4[j] > (l_uq[i] + 1.5*l_iqr[i])) | (x4[j] < (l_lq[i] - 1.5*l_iqr[i])) ) {
      l_count_tukey = l_count_tukey + 1
    }
  }

}

#коши
c_mean <- vector (length = 1000)
c_mad <- vector (length = 1000)
c_med <- vector (length = 1000)
c_sd <- vector(length = 1000)
c_iqr <- vector(length = 1000)
c_uq <- vector(length = 1000)
c_lq <- vector(length = 1000)
c_count_mean <- 0
c_count_med <- 0
c_count_tukey <- 0
for(i in 1:1000)
{
  x3<-rcauchy(100, location = 0, scale = 1)
  x3<-sort(x3)
  c_mean[i] <- mean(x3)
  c_med[i] <- median(x3)
  c_sd[i] <- sd(x3)
  c_mad[i] <- mad(x3, center = median(x3))
  c_iqr[i] <- IQR(x3)
  c_lq[i] <- x3[25]
  c_uq[i] <- x3[75]
  for (j in 1:100){
    if (((abs(x3[j] - c_mean[i]) / c_sd[i]) > 3)){
      c_count_mean = c_count_mean + 1
    }
    if (((abs(x3[j] - c_med[i]) / c_mad[i]) > 3)){
      c_count_med = c_count_med + 1
    }
    if ((x3[j] > (c_uq[i] + 1.5*c_iqr[i])) | (x3[j] < (c_lq[i] - 1.5*c_iqr[i])) ) {
      c_count_tukey = c_count_tukey + 1
    }
  }
}

#gross error model
g_mean <- vector (length = 1000)
g_mad <- vector (length = 1000)
g_med <- vector (length = 1000)
g_sd <- vector(length = 1000)
g_iqr <- vector(length = 1000)
g_uq <- vector(length = 1000)
g_lq <- vector(length = 1000)
g_count_mean <- 0
g_count_med <- 0
g_count_tukey <- 0
for (i in 1:1000) {
  for (j in 1:100) {
    alpha <- runif(1,0,1)
    if (alpha > 0.1) {
      x[j] <- rnorm(1,0,1)
    }
    else {
      x[j] <- rnorm(1,0,10)
      x1[j] <- rnorm(1,0,10)
    }
  }
  x<-sort(x)
  x1<-sort(x1)
  g_mean[i] <- mean(x)
  g_med[i] <- median(x)
  g_sd[i] <- sd(x)
  g_mad[i] <- mad(x, center = median(x))
  g_iqr[i] <- IQR(x)
  g_lq[i] <- x[25]
  g_uq[i] <- x[75]
  for (k in 1:100){
    if (((abs(x1[k] - g_mean[i]) / g_sd[i]) > 3)){
      g_count_mean = g_count_mean + 1
    }
    if (((abs(x1[k] - g_med[i]) / g_mad[i]) > 3)){
      g_count_med = g_count_med + 1
    }
    if ((x1[k] > (g_uq[i] + 1.5*g_iqr[i])) | (x1[k] < (g_lq[i] - 1.5*g_iqr[i])) ) {
      g_count_tukey = g_count_tukey + 1
    }
  }
}

boxplot(x, x2, x3, x4, x5, horizontal = TRUE)