Att

ℝ> dput(dados)

structure(list(X4 = c(3L, 2L, 4L, 2L, 4L, 5L, 1L, 5L, 5L, 3L,
2L, 2L, 7L, 5L, 3L, 1L, 4L, 5L, 4L, 3L), X5 = c(4L, 5L, 5L, 6L,
3L, 5L, 7L, 7L, 6L, 6L, 2L, 3L, 7L, 7L, 6L, 2L, 5L, 6L, 3L, 3L
), X15 = c(3L, 5L, 4L, 4L, 1L, 6L, 4L, 6L, 4L, 2L, 3L, 2L, 4L,
6L, 5L, 1L, 2L, 5L, 5L, 3L), X6 = c(2L, 5L, 3L, 4L, 3L, 5L, 5L,
5L, 3L, 5L, 2L, 2L, 3L, 6L, 6L, 4L, 4L, 5L, 3L, 4L), X11 = c(3L,
3L, 5L, 2L, 1L, 5L, 7L, 4L, 5L, 5L, 2L, 3L, 3L, 7L, 5L, 1L, 3L,
3L, 1L, 4L), X12 = c(4L, 5L, 5L, 6L, 4L, 6L, 6L, 6L, 5L, 6L,
4L, 2L, 6L, 6L, 6L, 5L, 4L, 5L, 5L, 3L), X13 = c(4L, 5L, 5L,
6L, 5L, 6L, 7L, 6L, 5L, 6L, 4L, 5L, 7L, 6L, 6L, 2L, 3L, 5L, 4L,
4L), X16 = c(2L, 4L, 4L, 4L, 3L, 6L, 5L, 3L, 4L, 6L, 4L, 3L,
6L, 7L, 5L, 2L, 5L, 6L, 2L, 2L), X17 = c(3L, 5L, 5L, 4L, 3L,
5L, 4L, 7L, 4L, 4L, 3L, 2L, 6L, 7L, 6L, 3L, 5L, 7L, 3L, 2L),
X18 = c(3L, 4L, 6L, 3L, 1L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 6L,
6L, 6L, 6L, 4L, 6L, 4L, 3L), X19 = c(3L, 5L, 6L, 6L, 3L,
6L, 5L, 4L, 3L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 3L, 6L, 4L, 3L
), X20 = c(5L, 4L, 5L, 6L, 1L, 4L, 3L, 3L, 2L, 5L, 4L, 2L,
6L, 5L, 5L, 4L, 3L, 5L, 5L, 3L), X25 = c(3L, 4L, 4L, 3L,
2L, 5L, 4L, 5L, 2L, 2L, 5L, 4L, 4L, 3L, 4L, 1L, 2L, 4L, 1L,
2L), X26 = c(4L, 3L, 4L, 4L, 1L, 5L, 4L, 7L, 5L, 2L, 3L,
5L, 4L, 4L, 4L, 2L, 4L, 3L, 4L, 3L), X27 = c(3L, 5L, 5L,
4L, 2L, 5L, 7L, 5L, 2L, 6L, 4L, 3L, 6L, 6L, 6L, 1L, 3L, 6L,
2L, 1L), X29 = c(3L, 3L, 4L, 2L, 1L, 4L, 6L, 6L, 5L, 3L,
3L, 2L, 5L, 6L, 6L, 1L, 4L, 6L, 5L, 2L)), .Names = c("X4",
"X5", "X15", "X6", "X11", "X12", "X13", "X16", "X17", "X18",
"X19", "X20", "X25", "X26", "X27", "X29"), class = "data.frame", row.names = c(NA,
-20L))

ℝ> obs <- function(x){
+ results <- c()
+ for(i in 1:500){
+ results[i] <- mean( sample(x,replace=TRUE))
+ }
+ return(results)
+ }
ℝ> resultado <- apply(dados,2,obs)
ℝ> resultado
X4 X5 X15 X6 X11 X12 X13 X16 X17 X18 X19 X20 X25 X26 X27 X29
[1,] 3.95 5.05 3.65 3.75 3.20 5.15 5.10 3.95 4.30 4.80 4.70 4.35 3.05 3.65 4.45 3.60
[2,] 3.65 5.05 3.45 4.15 3.25 4.60 4.75 4.70 4.45 4.80 4.55 4.25 3.65 3.20 3.60 4.15
[3,] 3.95 4.85 3.65 4.10 4.30 4.80 4.90 4.00 4.05 4.50 4.30 4.70 3.30 4.05 4.10 4.65
[4,] 3.50 4.75 3.55 4.10 3.05 4.55 4.70 3.60 4.40 4.85 4.85 4.60 3.15 4.20 4.35 4.15
[5,] 3.05 5.45 3.05 3.60 3.50 5.10 5.20 4.70 4.30 4.30 4.30 3.60 3.40 3.95 4.15 4.15
[6,] 2.80 5.05 3.85 4.15 4.10 5.05 5.00 3.60 4.80 4.15 4.70 4.15 3.15 3.35 3.90 3.80
[7,] 3.80 5.00 4.00 3.60 3.15 4.70 5.10 4.10 4.70 4.45 4.90 3.35 3.30 3.95 3.75 3.70
[8,] 3.85 4.80 4.05 3.60 3.80 5.10 4.95 4.25 4.75 4.25 4.90 4.20 3.10 3.85 4.30 3.35
[9,] 3.10 4.85 3.75 3.90 3.60 5.00 5.30 4.15 4.80 5.00 4.65 4.15 3.25 3.65 4.40 3.70
[10,] 3.20 5.15 3.30 4.20 4.40 5.20 5.10 4.85 4.40 4.05 4.55 4.45 3.90 3.60 5.40 4.25
[11,] 3.10 5.25 4.10 3.65 3.50 5.00 5.05 4.00 4.25 3.75 4.70 3.50 3.15 3.65 4.45 4.05
[12,] 3.50 4.45 3.60 4.15 3.65 5.10 4.75 3.55 4.50 4.95 4.40 4.40 3.65 3.85 3.95 2.90
[13,] 3.10 5.05 3.60 4.30 4.15 5.05 4.45 4.60 4.80 4.40 5.05 4.05 3.05 3.65 3.60 4.05
[14,] 3.00 5.10 3.90 4.20 4.10 4.75 5.45 4.10 4.25 4.55 4.75 4.70 3.65 3.65 4.25 4.10
[15,] 3.30 4.70 3.15 4.35 3.55 4.50 4.95 4.50 4.10 4.45 4.75 4.30 2.75 3.90 3.90 3.40
[16,] 3.70 4.80 4.00 3.45 3.30 4.70 5.55 4.20 4.30 4.50 4.60 3.05 3.20 3.75 3.40 3.20
[17,] 3.40 4.80 3.65 3.85 3.70 5.10 5.15 3.90 3.90 4.35 4.75 3.60 3.25 3.15 4.60 4.10
[18,] 3.40 4.90 3.70 4.00 2.75 4.70 5.00 4.30 4.25 4.50 4.35 4.05 2.95 3.75 4.45 3.50
[19,] 2.95 4.10 3.45 3.80 4.15 5.25 5.20 4.50 3.80 5.10 4.75 3.70 2.95 3.60 4.55 3.95
[20,] 3.15 5.30 3.45 4.05 3.20 5.40 4.85 4.40 4.65 4.75 4.65 3.70 3.20 3.60 4.00 3.75
[21,] 3.75 4.80 4.05 3.90 3.65 4.85 5.35 4.30 3.80 5.00 4.85 3.85 2.70 3.40 3.55 3.95
[22,] 3.35 4.70 3.80 4.10 3.85 5.25 4.95 4.25 4.35 4.40 4.65 4.00 3.45 3.75 3.60 3.45
[23,] 3.60 4.90 3.80 4.05 3.55 5.05 4.90 4.20 4.25 4.40 4.75 4.05 2.95 3.80 4.15 3.50
[24,] 3.75 4.30 4.05 4.20 2.80 4.90 5.30 4.65 4.55 4.30 4.95 4.70 3.10 3.85 4.20 4.40
[25,] 3.50 4.55 3.90 4.40 4.00 4.65 5.30 3.55 4.55 4.35 4.70 4.10 2.95 3.60 3.85 3.50
[26,] 4.35 4.40 3.55 4.45 3.55 5.05 5.20 4.70 4.35 4.55 4.50 4.40 3.40 3.85 4.25 3.35
[27,] 3.70 5.30 3.45 4.55 3.70 5.10 4.70 4.20 4.00 4.65 4.90 3.70 2.80 3.80 4.85 3.15
[28,] 3.70 4.85 3.80 3.30 3.85 5.25 4.65 4.30 4.80 4.55 4.60 3.90 3.10 3.45 4.15 4.30
[29,] 3.95 4.30 3.60 3.70 3.45 5.35 4.90 3.95 4.85 4.45 4.60 3.80 3.55 3.90 4.30 4.50
[30,] 3.15 4.95 4.10 4.10 3.50 4.95 4.90 4.65 4.75 4.35 4.45 4.25 3.00 3.70 4.20 3.65
[31,] 2.75 5.50 3.90 3.80 3.65 5.40 5.15 3.95 5.00 4.15 4.20 4.30 3.10 3.90 4.50 4.35
[32,] 3.65 5.10 3.40 3.45 3.95 4.65 5.55 4.10 4.25 4.70 4.95 3.80 3.40 3.60 4.25 3.45
[33,] 3.60 5.05 3.75 4.20 3.85 4.80 5.30 4.50 3.95 4.55 4.50 3.75 2.60 3.85 4.35 4.05
[34,] 3.35 4.90 3.50 3.95 3.30 5.00 4.85 4.25 4.70 4.75 4.65 3.95 2.85 3.55 3.90 3.75
[35,] 3.45 4.55 3.75 3.85 3.15 4.80 5.00 4.40 3.70 4.65 4.85 3.80 3.10 3.85 4.40 3.40
[36,] 3.95 4.85 3.70 3.85 3.50 5.10 5.10 3.65 4.70 4.25 4.65 4.10 3.35 3.40 5.10 3.40
[37,] 3.45 4.65 3.70 4.05 3.85 4.70 5.05 4.50 3.90 4.20 5.10 3.85 2.90 3.35 4.05 4.45
[38,] 3.05 5.05 3.55 4.25 3.50 4.85 5.10 4.00 3.70 4.60 4.85 4.20 2.55 3.70 4.15 4.05
[39,] 3.45 4.85 3.45 4.15 3.60 4.90 5.35 4.50 4.25 4.55 4.95 3.85 3.25 3.80 4.40 4.15
[40,] 3.20 4.80 3.45 4.15 3.90 5.30 5.35 4.30 4.95 4.20 4.65 4.20 3.00 3.30 4.35 4.00
[41,] 3.55 5.30 3.80 3.85 3.15 5.15 5.50 4.20 4.90 4.50 4.70 4.10 3.25 3.40 4.05 4.25
[42,] 3.40 4.45 3.65 3.65 3.20 5.20 5.35 4.90 4.10 4.35 4.50 4.50 3.05 3.70 3.65 3.95
[43,] 2.95 4.40 3.70 3.90 3.45 4.55 5.25 4.15 4.30 3.90 4.85 3.25 3.40 3.40 3.85 4.10
[44,] 3.90 5.30 3.80 4.25 4.55 4.80 5.55 4.40 4.35 4.40 4.95 3.75 2.85 3.30 4.80 3.60
[45,] 3.10 4.05 3.60 3.65 3.80 4.60 4.75 3.40 4.65 5.00 4.90 3.50 3.15 3.80 3.95 4.20
[46,] 3.10 4.90 3.60 3.80 2.75 4.40 4.35 4.50 4.75 4.25 5.00 4.30 3.00 3.95 3.90 3.85
[47,] 3.05 4.55 3.90 4.35 3.85 4.75 5.15 4.90 3.90 4.35 4.65 3.75 3.15 3.90 4.40 3.50
[48,] 3.80 5.05 3.85 4.00 3.75 4.90 5.65 4.25 4.10 4.45 4.80 3.80 2.85 3.60 3.90 3.75
[49,] 3.65 4.65 3.55 3.85 3.70 4.75 4.90 3.70 4.40 5.00 5.00 3.80 3.10 4.25 4.40 3.20
[50,] 3.05 5.20 3.60 4.25 3.95 5.05 5.05 4.25 4.35 4.10 4.40 4.15 3.15 3.65 4.25 4.10
[51,] 3.45 5.65 3.45 3.80 3.30 4.90 5.35 4.70 4.60 4.20 4.85 3.60 3.30 3.45 4.15 4.15
[52,] 3.60 5.25 3.10 3.90 4.05 4.95 4.20 4.30 4.25 4.45 4.70 4.50 2.95 3.95 4.75 3.80
[53,] 2.90 5.05 3.95 3.80 3.50 4.70 4.45 3.75 4.50 5.15 4.75 3.90 3.25 3.95 4.35 4.00
[54,] 3.20 4.80 3.70 3.90 3.95 4.85 5.05 4.40 4.20 3.90 4.55 4.00 3.30 4.25 4.50 4.00
[55,] 3.40 5.00 3.85 4.15 4.45 5.20 4.55 3.70 4.30 4.55 4.70 3.80 3.10 3.65 4.30 3.65
[56,] 3.70 5.30 3.40 4.00 3.35 5.45 4.90 4.05 4.20 4.50 4.50 4.00 2.95 3.50 4.55 3.65
[57,] 3.65 5.30 3.80 3.95 3.30 4.55 4.75 3.50 4.25 4.90 4.90 4.20 2.90 3.50 3.95 3.45
[58,] 4.20 4.75 3.40 3.90 4.20 5.20 4.70 4.40 4.35 4.50 4.60 4.10 3.40 3.80 4.30 3.50
[59,] 3.50 4.80 3.25 3.90 3.75 4.85 5.05 4.35 3.90 4.40 4.40 3.90 3.30 4.10 3.60 3.95
[60,] 3.15 4.85 3.95 3.70 3.60 5.25 5.20 4.35 4.25 4.90 5.10 3.80 3.30 3.55 5.15 4.15
[61,] 3.30 5.30 4.10 3.55 3.40 5.05 4.60 3.85 4.30 4.00 4.80 4.40 3.10 3.50 4.20 3.95
[62,] 2.90 4.80 3.20 3.55 4.20 4.80 5.10 4.45 4.65 4.00 4.60 4.00 3.25 3.75 3.80 3.90
[63,] 3.60 4.75 3.00 4.05 3.55 5.40 4.95 4.00 3.65 4.50 5.00 4.40 3.50 3.70 4.25 4.10
[64,] 3.00 5.70 3.75 3.65 4.10 4.80 4.80 3.50 4.60 4.40 4.85 4.20 3.05 3.75 4.05 4.25
[65,] 4.05 4.75 3.55 4.05 3.75 4.80 4.30 4.40 4.65 4.65 4.80 4.00 2.95 3.85 4.15 4.35
[66,] 3.75 4.10 4.00 3.40 2.75 5.00 4.80 4.35 4.05 4.65 4.65 3.80 3.75 3.50 4.65 5.35
[67,] 4.15 4.95 3.90 4.00 3.35 4.80 4.65 3.80 4.80 4.50 5.05 4.30 2.80 3.90 3.95 3.85
[68,] 4.00 5.15 4.10 3.95 3.40 4.75 4.85 3.80 4.25 4.50 4.75 3.70 3.15 3.60 3.70 4.30
[69,] 3.55 4.90 3.65 3.80 3.55 5.20 5.55 4.20 4.70 4.65 5.05 4.35 3.30 3.70 4.05 3.75
[70,] 3.70 4.75 4.10 3.95 3.70 4.70 4.95 4.20 4.25 4.05 4.90 4.50 3.25 3.40 3.80 3.90
[71,] 3.70 5.15 3.30 3.45 3.50 5.15 5.30 4.40 4.50 4.10 4.75 4.25 2.60 3.45 4.30 3.25
[72,] 3.80 4.65 3.70 4.25 3.70 4.65 5.00 4.50 4.20 4.65 5.00 3.85 2.75 3.65 3.90 4.30
[73,] 3.55 5.00 3.85 3.75 4.20 5.00 4.80 4.65 3.85 3.90 4.75 4.40 3.45 3.75 3.90 3.20
[74,] 3.25 5.15 3.80 3.90 3.70 4.65 5.15 3.95 4.60 4.65 4.35 4.10 2.95 3.70 4.35 4.10
[75,] 3.20 5.15 3.90 3.65 3.00 5.10 4.80 3.60 4.20 4.05 4.50 4.00 3.70 3.00 4.40 4.20
[76,] 4.05 5.15 3.60 4.10 3.80 4.90 4.80 3.90 4.80 4.20 4.80 4.45 3.50 3.80 4.15 3.60
[77,] 3.60 5.15 4.35 4.20 4.40 4.60 5.25 4.65 4.65 4.55 4.70 4.05 2.85 4.05 3.80 3.55
[78,] 3.35 5.45 3.80 3.45 3.15 5.05 5.15 4.20 4.10 4.35 4.90 3.95 3.25 3.65 3.70 4.05
[79,] 3.00 5.65 3.60 4.10 4.00 4.90 4.90 4.15 3.75 4.10 5.10 3.55 2.95 4.15 2.90 4.40
[80,] 3.85 5.70 4.20 4.05 4.30 5.10 4.90 4.45 4.25 4.55 4.80 4.55 2.85 3.65 3.90 3.10
[81,] 3.50 4.25 3.50 4.50 3.10 4.95 5.05 4.30 4.85 4.85 4.90 4.10 3.25 3.60 3.90 4.15
[82,] 3.05 4.65 3.55 4.35 3.25 4.90 5.00 3.40 4.40 4.55 4.95 4.35 3.15 4.25 4.30 3.75
[83,] 4.55 4.95 3.40 3.80 2.85 5.15 4.95 4.55 4.70 3.90 4.50 3.90 2.95 3.85 4.20 4.10
[84,] 4.05 4.65 4.05 3.85 3.50 4.75 5.50 4.25 4.05 4.85 4.00 3.75 3.00 3.70 4.70 3.65
[85,] 3.25 4.55 3.90 3.80 4.15 4.75 4.80 4.10 4.70 4.85 4.75 3.95 3.25 3.75 3.80 4.90
[86,] 3.80 4.60 4.30 4.20 2.95 4.65 5.20 4.15 4.20 4.45 5.00 4.95 3.40 3.55 4.35 4.35
[87,] 3.40 4.70 3.90 3.95 3.55 4.70 5.05 4.70 4.45 4.25 4.60 3.85 3.20 4.55 4.05 4.50
[88,] 3.35 4.00 3.65 3.70 3.35 4.65 4.90 4.70 4.30 4.35 4.40 3.55 3.30 3.90 4.50 4.05
[89,] 3.80 4.50 3.50 3.85 3.60 4.85 5.55 4.60 3.95 4.15 5.00 4.10 3.25 3.55 3.60 3.70
[90,] 3.25 4.85 3.80 3.80 4.25 5.00 5.10 4.30 4.10 4.40 4.75 4.20 2.95 3.60 3.80 3.60
[91,] 3.65 4.80 4.00 4.00 3.80 5.10 4.50 4.50 4.30 4.85 4.45 4.00 4.00 4.20 3.60 3.45
[92,] 3.35 4.75 3.75 3.70 3.70 5.15 5.40 4.20 3.75 4.25 4.85 3.75 3.10 4.40 4.20 4.05
[93,] 3.35 5.45 3.80 3.85 3.70 5.30 5.25 3.75 5.00 4.25 4.75 4.30 3.15 3.70 4.10 3.65
[94,] 3.35 5.65 3.65 4.20 3.00 4.55 4.80 3.60 3.65 4.55 4.85 3.50 3.35 3.95 4.10 3.55
[95,] 3.25 4.05 3.65 4.00 3.65 4.90 5.00 4.35 4.25 4.95 4.75 4.30 3.00 4.45 4.30 4.15
[96,] 3.40 4.95 3.85 3.90 3.70 4.85 5.20 3.70 5.35 4.70 4.75 3.65 3.70 4.15 4.30 4.00
[97,] 3.25 4.80 3.20 3.65 3.85 5.00 5.10 3.95 3.85 4.35 4.90 3.90 3.20 3.60 4.25 3.50
[98,] 4.30 4.55 3.00 3.35 4.25 5.05 5.30 4.15 4.50 4.75 4.75 3.95 2.85 3.80 3.95 3.40
[99,] 3.70 4.75 3.80 4.30 3.90 5.25 5.35 4.20 4.85 4.60 4.90 4.05 3.30 3.55 4.25 3.35
[100,] 3.20 5.45 3.55 3.90 3.00 4.75 5.20 4.40 4.25 4.15 4.55 4.20 3.25 4.30 4.50 4.40
[101,] 3.35 5.05 3.75 3.75 4.20 5.05 5.35 4.00 3.80 5.10 4.25 3.65 3.00 3.95 3.40 3.40
[102,] 3.95 4.90 4.00 3.80 3.00 4.95 5.10 4.05 4.20 4.60 4.75 4.60 3.30 3.50 4.05 3.75
[103,] 3.00 5.30 3.65 4.15 3.50 4.95 5.00 3.80 4.05 4.15 4.80 4.25 2.40 3.20 4.70 3.55
[104,] 3.75 5.65 3.90 3.95 2.80 5.15 4.35 4.05 4.45 4.05 4.95 4.70 3.15 4.00 3.50 3.60
[105,] 3.40 5.10 3.55 4.05 3.65 4.70 4.95 3.80 4.15 4.60 4.65 4.40 3.15 3.90 3.65 3.65
[106,] 3.55 4.80 3.50 3.20 4.00 4.45 5.05 4.40 4.40 4.65 4.80 4.05 2.80 3.85 4.60 3.70
[107,] 3.50 4.55 3.80 4.20 3.85 5.00 5.30 3.75 4.60 4.10 4.35 4.05 2.75 3.85 4.00 4.70
[108,] 3.10 4.75 3.35 3.90 3.00 4.80 4.90 4.30 4.40 5.20 4.15 3.75 2.80 4.15 4.85 3.60
[109,] 3.65 4.50 3.85 3.55 3.65 5.10 5.05 4.40 4.20 4.70 4.35 4.30 3.05 3.35 4.00 3.60
[110,] 3.80 5.25 3.90 4.25 3.80 5.20 5.10 4.30 4.45 4.45 4.75 4.15 3.80 3.55 4.35 4.25
[111,] 3.25 5.25 3.70 3.65 4.15 5.05 4.80 4.45 4.65 4.20 4.90 3.95 3.95 3.50 4.05 3.65
[112,] 3.30 4.40 3.80 3.25 4.10 5.10 4.75 4.65 4.15 4.35 4.20 4.05 3.75 4.05 4.45 3.65
[113,] 3.30 5.10 4.15 4.20 4.55 4.75 4.90 4.05 4.20 4.50 4.90 4.10 3.40 3.55 3.60 3.90
[114,] 3.15 4.90 3.95 4.05 3.50 5.00 4.50 4.50 4.75 5.15 4.10 3.50 2.80 3.90 4.05 4.15
[115,] 3.80 5.25 4.10 4.00 4.30 4.95 5.15 3.90 3.95 4.15 4.85 3.65 3.75 3.70 3.50 3.50
[116,] 4.45 5.50 3.75 4.00 3.35 4.05 4.75 4.15 4.45 4.65 4.70 3.90 2.90 3.60 4.35 4.25
[117,] 4.00 4.65 4.15 4.35 3.50 5.30 5.00 3.55 4.60 3.80 4.70 3.85 2.95 3.30 3.40 4.10
[118,] 3.20 4.70 3.50 3.50 3.30 5.10 5.20 3.35 4.45 4.50 4.70 3.75 3.00 3.85 4.25 4.25
[119,] 3.35 5.30 4.35 4.05 3.70 5.15 4.60 4.20 5.05 4.35 4.80 3.85 3.45 3.75 3.90 4.15
[120,] 3.40 5.90 4.55 3.45 4.05 5.20 4.75 3.70 4.10 4.65 4.35 4.10 3.20 3.55 3.65 3.80
[121,] 3.50 4.80 4.10 4.05 4.20 4.70 5.00 4.20 4.65 4.60 4.40 3.85 3.00 3.85 4.35 3.80
[122,] 3.70 4.60 4.15 3.55 3.30 4.60 4.90 4.45 4.50 4.10 4.65 4.10 3.15 3.40 3.85 4.30
[123,] 3.85 4.50 3.75 3.90 3.95 4.80 5.15 4.35 4.65 4.20 4.35 3.95 3.00 3.75 3.45 3.80
[124,] 3.65 4.75 4.20 3.75 3.15 4.80 5.05 4.55 4.35 4.75 4.80 4.15 3.25 4.15 3.45 4.10
[125,] 3.55 5.10 3.80 4.10 4.05 4.95 4.60 4.30 4.20 4.85 4.85 3.35 3.20 3.35 3.70 2.80
[126,] 3.20 5.10 3.90 3.90 3.50 5.00 5.45 4.25 4.60 4.10 4.50 3.70 3.30 3.75 4.75 3.80
[127,] 3.55 4.65 3.90 4.05 3.60 4.95 4.95 4.60 4.45 4.55 4.70 3.65 3.40 3.45 3.50 3.70
[128,] 3.70 5.15 3.90 4.10 4.05 5.20 4.85 4.35 4.80 4.45 4.55 4.05 3.40 3.55 4.10 3.65
[129,] 3.05 4.35 4.00 4.20 3.85 4.70 4.85 4.35 4.65 4.50 4.70 3.85 3.30 3.65 3.95 3.85
[130,] 3.80 5.10 2.90 4.25 3.45 4.85 5.00 4.35 3.95 3.90 4.85 3.65 3.15 4.30 3.90 4.10
[131,] 3.60 4.80 3.50 4.00 4.30 4.50 5.15 4.15 4.60 4.55 4.80 4.45 3.10 4.00 4.20 3.35
[132,] 3.10 5.35 4.20 4.55 3.75 5.15 5.10 4.35 4.60 4.20 4.65 3.70 3.15 3.90 4.00 3.65
[133,] 3.65 5.00 4.15 3.85 3.50 5.20 4.80 4.20 4.75 4.90 4.60 4.35 3.00 3.55 4.20 3.70
[134,] 3.55 5.30 3.75 4.25 4.05 5.05 4.70 3.75 4.70 4.60 4.65 3.70 3.20 4.00 4.00 3.80
[135,] 3.80 4.95 3.75 3.80 3.00 4.95 5.60 3.70 4.20 4.65 4.50 3.95 3.00 4.00 4.10 4.00
[136,] 2.70 4.35 4.00 3.80 4.15 5.20 4.90 4.00 4.30 4.95 4.60 4.10 3.15 3.55 4.50 4.05
[137,] 3.20 4.30 3.90 3.95 3.50 4.95 5.30 4.00 4.15 4.15 4.20 3.95 3.80 3.10 3.95 4.00
[138,] 3.15 4.95 3.20 3.95 3.50 5.20 4.70 3.90 4.65 4.00 4.65 3.50 3.35 3.95 4.15 3.25
[139,] 3.50 4.60 4.45 3.95 4.35 5.20 4.70 4.70 4.75 4.10 4.45 3.75 3.40 3.20 3.75 4.35
[140,] 3.20 4.75 3.60 3.70 3.50 4.90 5.05 3.85 4.60 4.55 4.65 4.05 3.25 3.90 4.85 3.55
[141,] 3.80 4.95 4.05 4.35 3.65 5.05 5.05 3.65 3.80 4.60 4.30 4.10 3.10 3.60 4.25 3.85
[142,] 3.90 5.00 2.85 3.45 2.95 5.05 5.10 4.45 4.90 4.45 4.70 4.25 2.95 3.55 3.90 3.25
[143,] 3.95 4.85 3.65 3.85 2.95 5.10 5.60 4.65 4.30 4.20 4.35 4.50 3.45 3.80 4.55 3.90
[144,] 3.40 5.55 3.45 3.85 3.65 5.15 5.10 4.45 4.75 4.10 4.25 3.50 3.45 3.90 4.35 3.35
[145,] 3.35 4.20 3.95 3.85 3.65 4.95 4.80 4.20 5.15 4.60 4.55 3.85 3.10 3.75 3.70 3.45
[146,] 3.55 5.30 3.55 3.85 2.85 4.65 5.60 4.30 4.75 4.45 4.75 3.55 3.10 3.75 4.45 3.95
[147,] 3.20 5.55 3.50 3.90 3.55 4.75 5.20 4.65 4.25 4.10 4.75 3.80 3.40 3.90 4.10 4.10
[148,] 3.30 5.80 3.80 4.35 2.95 5.05 5.35 4.25 4.65 4.45 4.30 3.65 3.70 3.70 4.45 3.70
[149,] 3.55 4.70 3.75 3.45 3.80 5.00 5.30 4.45 4.05 4.50 4.80 4.55 3.60 3.80 4.10 4.05
[150,] 4.25 4.75 4.00 4.00 3.20 4.85 5.50 3.75 4.55 4.55 4.55 3.60 2.90 4.15 3.95 3.30
[151,] 3.55 5.25 3.75 4.25 3.10 4.70 5.30 3.95 4.30 5.20 5.00 3.75 3.65 3.60 4.55 3.65
[152,] 3.60 4.75 4.10 4.10 3.90 5.20 5.05 3.50 4.55 4.30 4.50 3.90 2.70 3.85 4.30 3.65
[153,] 4.10 4.90 3.80 3.85 3.20 5.60 5.20 3.65 4.30 4.15 5.10 3.90 3.20 3.55 3.60 4.05
[154,] 3.60 5.20 3.60 3.95 3.30 4.65 4.80 4.50 4.35 4.20 5.00 4.40 3.35 3.25 3.85 4.15
[155,] 3.05 4.85 3.85 4.45 4.20 4.80 4.80 4.35 4.95 4.65 4.90 3.65 3.35 3.70 3.65 3.30
[156,] 3.15 4.50 4.15 4.15 3.90 4.65 5.35 4.20 4.30 4.15 4.95 3.85 3.35 3.60 4.00 4.10
[157,] 3.50 4.65 3.50 4.15 4.30 4.60 4.60 4.50 4.75 3.85 4.85 3.50 3.25 4.20 3.55 4.15
[158,] 3.30 4.65 4.10 4.10 3.30 4.60 5.00 4.40 4.80 5.10 4.40 3.95 3.45 4.30 4.15 3.40
[159,] 3.10 5.40 3.45 3.60 4.55 5.40 5.35 3.20 4.60 4.40 4.95 4.30 3.25 3.80 4.10 4.20
[160,] 3.85 5.20 3.90 4.25 3.55 5.40 4.85 4.15 3.95 4.50 4.25 3.95 2.80 3.95 4.70 3.85
[161,] 3.20 5.05 3.35 3.80 3.45 5.15 5.30 4.50 4.65 4.15 4.75 4.40 3.95 3.95 4.60 3.50
[162,] 3.15 4.00 3.75 4.15 3.25 4.40 4.75 3.60 5.05 4.05 4.40 4.30 3.05 4.00 3.75 3.30
[163,] 3.15 4.30 3.70 3.70 3.40 4.95 4.70 4.70 4.45 4.45 5.10 4.10 3.20 3.05 4.55 3.90
[164,] 3.80 5.15 3.95 3.90 4.30 5.40 5.15 4.05 4.50 3.90 4.20 3.95 2.85 4.30 4.30 3.70
[165,] 3.60 4.55 3.75 4.15 4.25 4.60 4.70 4.45 4.45 4.65 4.30 4.00 3.30 3.50 4.50 3.65
[166,] 3.65 5.35 4.35 4.05 3.55 4.75 4.95 3.80 4.60 4.80 4.95 4.00 3.55 3.95 3.85 4.15
[167,] 3.60 4.70 3.65 4.20 3.95 5.10 4.80 4.05 4.20 4.00 4.50 4.20 3.45 3.90 4.50 3.85
[168,] 3.75 4.70 4.40 3.90 3.15 4.75 4.90 4.40 4.55 4.10 4.90 4.25 3.20 3.60 4.00 4.15
[169,] 3.15 4.50 3.75 3.95 4.05 5.10 4.90 3.95 4.25 4.20 4.40 4.00 2.90 3.30 3.70 4.85
[170,] 4.20 5.15 3.65 3.95 2.85 4.95 4.35 4.25 4.70 4.30 4.45 4.10 3.00 3.90 3.75 3.55
[171,] 3.80 5.25 3.85 4.00 4.40 4.20 5.40 4.35 4.40 4.05 4.80 3.35 3.40 3.65 4.35 4.25
[172,] 3.80 4.45 3.45 4.05 4.05 4.95 5.20 4.25 4.65 4.10 4.55 3.85 3.35 3.60 4.70 3.50
[173,] 3.60 4.80 4.25 4.30 3.75 5.35 4.30 3.55 5.05 4.25 4.20 4.10 3.15 3.80 3.90 3.90
[174,] 3.40 5.60 3.75 4.10 4.10 5.25 5.05 4.20 4.20 4.40 4.85 4.10 3.45 3.75 4.10 3.20
[175,] 3.20 4.30 3.75 3.90 2.80 4.95 5.45 4.65 4.20 4.40 4.55 3.45 2.90 3.65 3.90 3.55
[176,] 3.10 5.50 4.10 4.10 3.55 4.80 5.30 4.65 4.55 4.20 4.65 4.05 3.25 3.75 4.25 4.15
[177,] 3.75 4.50 3.65 4.30 3.30 4.55 4.80 4.45 4.50 4.40 4.85 3.75 3.50 3.75 4.35 3.70
[178,] 3.85 4.90 3.55 4.05 3.80 4.80 4.85 4.15 4.55 4.50 4.85 4.00 3.15 4.05 3.35 3.90
[179,] 3.60 5.25 3.70 3.65 3.25 5.25 4.85 4.20 4.30 4.50 4.85 4.45 3.30 3.45 4.00 3.65
[180,] 2.95 5.00 3.60 3.75 3.95 5.10 4.80 3.60 4.75 4.60 4.80 4.25 2.90 3.85 4.65 3.65
[181,] 3.65 5.05 3.35 3.80 3.45 5.10 5.25 3.95 4.15 4.05 4.50 3.20 2.95 3.30 4.35 3.65
[182,] 4.05 5.40 3.90 4.00 3.40 5.10 5.25 4.20 4.20 4.25 4.70 4.25 3.10 4.10 3.35 3.20
[183,] 3.80 4.70 4.10 3.60 4.05 5.05 5.15 4.85 4.65 4.70 4.60 4.15 3.10 3.90 3.50 3.35
[184,] 3.35 5.50 3.10 3.65 3.50 5.05 5.10 3.70 5.30 4.15 4.90 4.05 3.20 4.30 3.95 4.15
[185,] 3.50 5.05 3.70 3.65 3.15 5.10 5.20 3.90 4.10 4.65 4.80 4.40 3.20 3.70 3.80 3.60
[186,] 3.75 4.80 3.95 4.85 3.65 5.15 4.60 3.80 4.00 4.90 4.55 3.55 3.70 3.45 4.60 4.30
[187,] 3.50 4.75 2.85 4.15 3.50 4.70 5.25 4.75 4.95 4.65 4.90 3.95 3.15 3.70 3.95 3.95
[188,] 3.20 4.35 4.10 4.05 3.40 5.00 5.15 4.20 4.25 4.20 4.30 4.20 3.30 3.70 4.00 4.20
[189,] 3.05 4.95 3.95 4.05 4.05 5.05 4.75 3.85 4.45 3.95 4.80 3.90 3.10 3.20 4.25 4.30
[190,] 3.40 4.10 4.15 3.80 4.35 4.55 5.30 3.85 3.80 4.85 4.95 3.70 3.65 4.05 4.30 3.75
[191,] 3.70 5.30 4.35 3.95 3.85 5.10 4.55 4.55 4.40 4.95 4.80 4.45 3.40 3.50 4.55 3.90
[192,] 3.45 5.35 3.95 4.50 3.90 5.10 4.50 4.35 4.20 4.25 4.15 4.05 3.25 3.25 4.75 4.15
[193,] 3.75 5.10 3.75 3.90 3.65 4.85 5.25 4.15 4.35 4.40 4.85 4.10 3.10 3.70 4.50 4.50
[194,] 3.60 4.85 3.60 4.60 3.95 5.05 5.20 4.55 4.55 4.10 4.80 3.85 3.45 3.70 4.15 3.85
[195,] 3.35 5.05 3.45 3.90 3.55 5.10 5.40 4.25 3.80 3.90 4.90 4.00 3.85 3.70 4.30 3.45
[196,] 3.50 5.20 4.20 4.00 4.00 5.00 4.50 3.90 4.60 4.30 4.55 3.95 3.05 3.85 4.05 3.55
[197,] 3.60 4.60 4.35 3.95 3.45 4.85 5.05 4.40 3.70 4.45 5.00 3.60 2.95 3.85 4.45 3.85
[198,] 3.55 5.20 3.75 4.10 3.75 4.85 5.00 3.90 4.30 4.35 5.00 4.00 3.20 3.95 4.15 3.95
[199,] 3.70 4.45 4.00 4.20 3.40 5.20 4.75 3.85 4.80 4.35 4.35 3.80 3.00 3.85 3.90 3.90
[200,] 3.25 5.30 4.00 4.85 3.75 4.20 4.80 4.75 4.55 4.50 4.35 4.30 3.30 3.60 4.15 3.95
[201,] 3.30 5.10 3.70 3.95 3.60 5.25 4.95 3.70 4.45 4.05 4.60 4.30 2.75 3.70 4.15 4.35
[202,] 3.15 4.75 3.95 4.05 3.05 4.85 5.15 3.75 4.25 4.75 4.85 3.90 3.20 3.75 4.60 4.20
[203,] 3.40 4.55 4.00 3.60 3.35 4.70 5.35 4.85 4.95 4.60 4.95 4.15 3.35 3.50 3.90 4.25
[204,] 3.75 4.60 3.65 3.80 3.45 4.60 4.90 3.90 4.65 4.00 4.40 3.70 3.15 3.30 4.15 3.85
[205,] 3.75 4.95 3.40 4.15 3.45 5.25 4.75 3.75 4.05 4.45 4.90 4.35 3.35 4.20 4.70 4.10
[206,] 3.05 5.00 3.95 4.30 3.55 5.20 5.40 4.25 4.45 4.45 4.50 4.55 3.25 3.55 4.50 4.25
[207,] 3.95 5.20 3.55 3.75 3.45 4.90 5.15 4.20 4.60 4.45 4.60 3.25 3.55 3.65 4.55 4.00
[208,] 3.50 5.50 3.90 3.85 4.05 5.05 5.25 3.90 4.40 4.95 4.50 4.30 3.20 3.65 3.70 3.90
[209,] 4.05 5.30 3.05 4.20 3.35 5.25 4.90 4.05 4.20 4.75 4.45 3.55 3.55 3.90 4.55 4.20
[210,] 3.15 4.70 3.25 3.45 3.40 4.35 5.00 4.75 4.45 4.60 4.05 3.90 3.10 3.60 4.45 3.90
[211,] 4.05 4.75 4.20 3.85 3.10 5.15 4.55 4.25 3.65 4.70 4.60 4.20 2.90 4.05 3.60 3.55
[212,] 3.85 4.90 4.00 4.15 4.30 4.80 5.00 4.00 3.95 4.90 4.40 4.60 2.90 3.70 4.10 4.00
[213,] 4.20 5.20 3.90 3.70 3.80 4.75 5.35 5.00 4.40 4.60 4.45 4.10 3.05 3.45 3.80 4.20
[214,] 4.00 4.45 3.10 3.50 3.80 5.15 4.95 4.10 4.65 4.60 4.55 4.25 3.15 3.85 3.50 3.45
[215,] 3.75 5.55 3.65 3.85 3.55 4.95 5.00 4.15 4.10 4.10 4.75 4.35 2.85 3.50 3.45 4.05
[216,] 3.70 4.70 3.80 3.60 3.70 5.45 5.10 4.25 4.75 4.35 4.65 4.10 3.30 3.65 4.85 3.60
[217,] 3.50 4.75 4.30 4.00 3.65 5.10 5.20 4.00 3.85 4.45 4.50 3.90 3.25 3.70 3.60 3.65
[218,] 3.35 4.60 3.95 4.15 3.65 4.75 4.80 4.00 4.55 4.15 4.45 4.30 3.15 3.45 4.35 3.60
[219,] 3.95 5.75 4.00 4.00 4.05 4.70 5.30 4.60 3.95 4.60 4.55 4.35 3.55 4.35 4.55 4.30
[220,] 3.05 5.15 4.75 3.95 3.50 4.40 4.90 4.25 4.20 4.10 4.85 3.70 3.40 3.75 3.85 3.80
[221,] 3.65 5.10 3.35 3.95 4.15 4.95 5.05 4.10 3.90 4.70 4.95 4.10 2.90 3.85 4.25 3.90
[222,] 3.40 5.05 3.60 3.40 2.90 5.10 5.20 3.95 4.35 4.55 4.65 3.80 2.65 3.75 4.25 3.65
[223,] 3.00 4.60 3.85 3.90 4.45 5.20 5.05 4.40 4.15 4.80 4.80 3.95 2.65 3.65 3.75 4.15
[224,] 3.25 4.60 4.55 4.35 3.85 4.75 5.10 4.10 4.55 4.35 4.55 3.90 3.35 3.60 3.90 4.25
[225,] 2.60 4.90 4.20 3.85 2.80 4.95 5.05 4.05 4.10 4.90 4.55 3.55 3.15 3.40 3.95 3.75
[226,] 2.90 4.85 3.75 4.10 4.00 5.15 5.25 4.05 4.40 4.15 4.10 3.60 3.05 3.70 4.00 3.40
[227,] 3.10 4.45 3.75 4.25 3.25 4.65 4.70 4.20 4.40 4.35 4.40 3.80 3.15 3.95 4.35 3.65
[228,] 3.45 4.40 3.55 4.15 3.10 4.70 5.00 3.55 4.85 4.35 4.50 3.60 3.60 3.80 4.20 3.45
[229,] 3.35 4.80 3.75 4.30 3.55 5.40 5.75 4.80 4.20 4.75 4.55 3.85 3.45 3.65 3.90 4.05
[230,] 3.10 4.95 3.70 3.85 3.90 5.00 5.10 4.05 4.60 4.40 4.80 4.00 2.35 4.05 4.35 4.15
[231,] 3.50 5.35 3.40 4.05 4.10 4.65 4.80 4.15 4.60 4.75 4.90 3.85 3.35 3.90 4.00 3.35
[232,] 3.75 4.25 3.40 3.75 3.65 4.95 4.85 4.30 4.35 4.35 4.55 4.05 3.20 3.85 3.95 3.60
[233,] 3.45 4.95 4.00 3.60 4.10 5.30 5.30 4.05 4.55 4.85 4.75 4.40 3.25 3.60 4.30 3.40
[234,] 3.35 4.95 3.50 4.25 3.65 4.55 5.45 4.10 4.75 4.25 5.10 4.45 3.15 4.50 4.55 3.95
[235,] 3.35 5.75 3.35 3.50 3.60 4.80 5.10 4.30 4.35 4.25 4.85 3.45 3.05 3.50 4.55 3.85
[236,] 3.65 4.70 3.80 4.15 3.60 4.80 5.00 4.20 4.05 4.75 4.55 3.60 3.20 3.40 3.75 3.40
[237,] 4.15 4.90 3.60 3.85 3.85 4.50 5.05 4.00 4.40 4.20 4.30 4.00 2.80 3.75 3.90 3.30
[238,] 3.10 5.20 3.55 3.80 4.25 5.20 4.70 4.30 4.95 4.85 4.80 4.15 2.80 3.85 3.65 3.80
[239,] 3.20 4.75 3.95 4.05 3.85 5.05 5.35 4.40 4.25 4.60 5.20 4.05 3.70 3.75 3.95 4.15
[240,] 3.60 4.85 3.55 4.00 3.70 5.10 5.15 4.65 4.70 4.55 4.95 4.20 3.45 4.05 4.20 4.00
[241,] 3.75 4.90 4.05 4.10 3.50 4.70 4.80 4.40 4.25 4.35 4.85 4.10 2.85 3.40 4.05 3.75
[242,] 4.00 5.05 3.55 3.80 3.70 5.25 5.15 3.70 4.25 4.40 5.00 3.80 3.30 3.40 4.20 3.70
[243,] 3.50 4.70 3.35 4.05 4.10 4.60 4.95 3.75 4.05 4.45 4.45 4.50 3.20 4.20 4.35 3.40
[244,] 3.10 5.20 3.65 3.80 4.15 5.10 4.95 3.85 4.35 4.35 5.00 3.45 3.35 3.55 4.85 4.05
[245,] 3.20 4.55 3.60 4.05 3.25 4.95 5.10 4.00 4.10 3.90 4.50 4.75 3.45 4.10 4.00 4.10
[246,] 3.35 4.35 3.20 3.95 3.95 4.85 4.50 4.10 3.70 4.60 4.75 4.00 3.70 3.50 4.15 3.90
[247,] 3.55 4.60 3.75 4.35 3.30 4.85 4.85 3.80 4.05 4.80 4.55 4.40 3.55 3.20 4.10 3.30
[248,] 3.70 5.10 4.00 3.70 4.20 4.65 4.80 4.35 4.45 4.65 4.70 3.60 3.00 3.15 4.55 4.30
[249,] 3.80 4.05 4.00 4.50 3.55 5.00 5.55 4.05 4.00 4.00 4.45 4.45 3.05 3.60 4.45 3.55
[250,] 3.30 4.60 3.95 3.90 3.30 5.10 4.95 3.75 4.20 4.45 4.70 4.45 3.10 3.60 4.15 4.05
[251,] 3.35 5.00 3.75 3.70 3.50 4.70 5.10 4.25 4.45 4.30 4.85 3.70 3.20 3.40 4.25 4.45
[252,] 3.20 5.25 4.05 3.35 3.90 5.15 5.25 4.55 4.15 4.50 4.50 3.85 3.05 4.15 4.05 3.95
[253,] 3.90 5.75 3.95 3.80 4.10 4.90 5.05 3.40 4.40 4.65 4.60 4.35 3.60 3.95 4.05 3.05
[254,] 3.50 4.80 3.15 4.05 4.20 4.95 5.60 4.40 4.25 4.20 4.95 4.35 3.50 3.40 4.45 3.90
[255,] 3.55 5.40 2.85 3.85 4.25 4.95 5.45 3.75 4.15 4.50 4.90 4.20 3.70 4.10 4.65 4.00
[256,] 3.85 5.15 4.10 4.20 4.15 4.80 4.50 4.35 4.85 4.90 4.90 4.05 3.50 3.70 4.35 3.65
[257,] 3.40 5.10 3.80 3.90 3.05 4.75 4.85 3.95 4.65 4.35 4.40 4.25 3.25 3.75 3.75 3.55
[258,] 3.75 4.80 3.15 3.75 3.40 4.90 4.75 4.45 4.35 4.50 4.40 4.35 3.40 3.60 4.25 2.45
[259,] 3.85 5.05 3.30 4.10 3.05 4.95 4.60 4.05 4.35 4.20 4.70 3.55 2.75 3.55 4.65 4.50
[260,] 3.15 4.60 3.85 4.50 2.60 5.15 5.35 4.10 4.20 4.15 4.60 4.40 3.10 3.30 4.20 3.45
[261,] 3.60 4.75 3.45 4.25 2.90 4.85 5.00 3.85 4.25 4.35 4.60 4.25 3.60 3.95 3.90 4.15
[262,] 2.75 5.60 3.60 4.05 3.35 5.05 4.95 4.60 4.45 4.80 4.95 4.10 3.30 3.95 4.00 3.55
[263,] 2.95 4.85 3.35 4.05 3.25 4.95 5.55 4.35 4.05 4.85 4.90 4.00 3.15 3.75 4.05 3.90
[264,] 3.85 4.95 3.75 3.85 4.20 4.70 5.10 3.45 4.40 4.10 4.30 4.25 3.45 4.05 4.25 3.65
[265,] 3.50 4.45 4.05 3.55 4.00 4.80 4.95 4.15 4.70 4.95 4.45 3.95 3.70 3.75 4.80 3.85
[266,] 3.45 4.95 3.40 3.55 3.70 5.20 5.40 4.05 4.70 4.15 4.55 4.00 3.30 4.05 4.55 4.25
[267,] 3.20 4.10 3.70 3.95 3.40 5.05 5.35 4.20 4.30 4.65 5.40 3.75 3.45 3.75 3.95 3.75
[268,] 3.70 5.00 4.10 3.90 3.85 5.00 5.35 4.25 4.25 4.50 4.85 3.80 2.95 3.40 4.35 4.10
[269,] 4.00 4.85 3.80 3.90 3.15 4.80 5.25 3.80 4.75 4.65 4.40 4.00 3.00 3.25 3.85 3.80
[270,] 3.45 5.20 3.90 4.05 3.40 5.10 5.15 3.40 4.10 4.10 4.85 3.50 3.45 4.00 3.90 3.50
[271,] 3.85 5.30 3.75 3.95 3.00 5.15 5.00 4.00 4.75 4.00 4.80 3.60 3.00 3.50 4.15 4.30
[272,] 3.80 4.75 3.80 3.65 3.20 5.30 5.00 4.35 4.40 4.65 4.80 3.95 3.65 3.75 3.75 3.75
[273,] 3.10 5.45 3.45 3.85 3.75 4.85 5.25 4.35 4.10 4.80 4.25 4.05 3.15 3.45 4.05 3.55
[274,] 3.90 5.05 4.25 4.35 3.85 5.15 5.05 4.45 4.45 4.25 4.50 4.00 3.25 4.20 4.80 4.35
[275,] 2.95 5.15 4.00 4.10 3.45 5.20 4.95 4.10 4.70 4.65 4.25 3.35 3.35 4.10 4.60 4.05
[276,] 3.40 5.60 3.60 4.00 4.50 4.95 4.70 4.75 4.30 3.95 4.95 3.80 3.20 3.85 3.80 3.60
[277,] 3.25 5.10 4.00 3.65 3.75 4.90 5.10 4.20 4.85 4.05 4.75 4.40 3.15 3.30 4.15 4.15
[278,] 3.95 5.05 3.45 3.55 3.60 5.35 5.35 4.00 4.60 4.70 4.80 3.80 3.20 4.05 4.60 3.75
[279,] 3.20 4.85 3.95 3.90 4.35 4.95 4.95 3.85 4.40 4.30 4.55 3.95 3.25 4.00 3.40 3.40
[280,] 3.10 4.80 4.00 3.70 3.50 4.45 4.65 3.95 4.30 4.35 4.50 4.45 3.20 3.75 4.50 3.15
[281,] 3.00 6.00 3.75 3.60 4.10 4.85 4.30 4.15 4.45 4.85 4.45 4.35 3.30 4.60 4.35 3.95
[282,] 3.25 4.60 3.80 3.05 3.00 4.80 5.50 3.70 4.15 4.85 4.65 3.80 3.70 3.30 4.05 3.65
[283,] 3.20 5.05 4.30 3.95 3.25 5.05 4.85 4.45 4.00 4.25 4.40 3.85 2.55 3.45 3.80 4.60
[284,] 3.60 4.70 3.90 4.15 3.55 5.15 4.90 3.65 4.95 4.65 4.70 4.35 2.80 4.20 4.25 3.90
[285,] 3.80 5.10 4.25 3.65 3.30 4.90 4.75 3.55 4.50 4.25 4.60 4.15 3.50 3.90 4.20 3.65
[286,] 3.50 4.20 3.60 3.95 2.90 4.95 4.85 4.80 4.50 4.20 4.30 3.70 3.25 3.60 4.45 4.10
[287,] 3.30 4.60 3.50 4.40 3.35 4.45 4.85 4.35 4.10 4.45 5.05 4.60 3.10 4.05 3.25 4.15
[288,] 2.70 4.75 3.75 3.50 3.50 5.05 5.40 4.75 4.65 4.80 4.35 3.75 3.50 3.85 4.20 4.50
[289,] 3.95 4.95 3.70 3.70 4.30 4.80 5.55 3.40 3.90 4.45 4.95 4.25 3.00 3.85 4.15 4.15
[290,] 2.90 5.00 3.60 3.95 4.00 4.80 5.25 3.95 4.65 4.75 4.90 4.40 3.00 3.80 3.70 3.40
[291,] 2.95 5.40 3.20 3.80 3.15 5.15 5.00 4.20 4.30 3.90 4.60 3.75 3.50 3.10 4.45 4.30
[292,] 3.45 5.15 3.75 4.45 3.50 5.30 4.70 3.75 5.05 4.75 5.25 4.60 3.40 3.85 4.20 4.05
[293,] 3.40 5.20 3.70 4.15 3.15 4.90 4.90 4.25 4.05 4.55 4.50 3.55 2.95 4.25 4.20 3.75
[294,] 3.85 4.75 3.90 3.80 3.70 5.40 5.20 4.00 3.95 4.45 4.90 4.30 2.75 3.75 3.75 4.20
[295,] 3.80 4.60 4.10 3.40 3.90 4.75 5.25 4.90 4.00 4.30 5.10 4.25 3.65 3.70 3.75 2.90
[296,] 3.65 5.05 4.10 3.80 3.50 5.10 5.40 4.10 4.45 4.05 4.50 4.55 3.60 3.50 4.70 4.20
[297,] 4.20 4.85 3.85 4.15 3.65 4.75 5.15 5.10 4.70 4.45 4.75 4.10 2.85 3.65 4.20 3.25
[298,] 2.85 4.25 3.90 4.55 4.05 5.05 5.35 4.40 4.15 4.55 4.85 4.40 3.25 3.70 5.00 3.65
[299,] 3.45 5.55 3.35 3.95 4.00 5.20 4.75 4.15 4.75 3.80 4.55 3.90 3.20 3.60 4.00 3.85
[300,] 3.85 5.50 3.50 3.60 4.10 4.90 4.85 3.75 4.20 4.25 5.00 3.95 3.60 3.70 3.85 3.35
[301,] 3.45 4.95 3.80 4.30 3.90 5.10 5.30 4.20 4.00 4.60 4.15 3.85 3.20 3.35 4.80 3.00
[302,] 3.10 5.15 4.35 4.00 3.70 4.35 5.05 4.25 4.90 4.55 4.40 4.20 3.20 4.30 4.45 4.85
[303,] 3.60 5.10 4.00 3.70 3.80 4.75 4.50 4.05 4.90 4.40 4.45 4.30 2.60 3.00 4.40 4.25
[304,] 3.30 5.25 3.05 3.80 3.15 5.05 4.85 4.15 4.85 4.70 4.90 4.10 3.45 3.95 4.10 3.55
[305,] 3.90 5.20 4.15 4.30 3.45 5.20 4.85 3.90 4.65 4.20 5.35 4.10 3.25 3.50 3.95 3.65
[306,] 3.75 4.85 4.15 4.20 3.70 5.05 5.25 3.70 4.70 4.75 4.70 4.10 3.50 3.75 4.30 3.60
[307,] 3.30 4.30 3.65 4.30 3.55 5.20 4.85 4.00 4.65 5.15 4.80 4.10 3.30 3.90 4.05 4.20
[308,] 3.60 4.95 3.35 3.80 4.15 5.05 5.00 4.65 4.55 4.25 4.55 4.00 3.40 3.80 3.80 2.95
[309,] 3.95 5.00 3.90 4.10 3.65 4.85 5.10 4.20 4.95 4.30 4.40 4.20 3.65 4.20 4.00 3.45
[310,] 3.45 4.80 4.05 3.95 3.75 4.70 4.90 3.70 4.40 3.95 4.25 3.85 3.35 3.10 3.65 4.75
[311,] 3.10 5.00 3.65 4.30 3.90 5.50 5.05 4.20 4.55 4.75 4.60 3.95 3.35 4.05 3.90 3.35
[312,] 3.75 5.30 3.85 3.80 3.40 5.50 5.55 4.20 3.70 4.50 4.60 4.05 3.05 3.25 4.35 4.30
[313,] 3.30 4.60 3.85 3.65 3.65 5.05 5.10 4.30 4.35 4.45 4.80 4.45 3.15 3.35 4.35 4.25
[314,] 3.30 4.40 3.60 3.75 3.60 4.95 5.00 3.55 4.35 4.35 4.85 3.45 3.10 3.50 3.75 4.35
[315,] 3.40 4.30 4.30 4.60 3.70 4.50 4.70 4.30 4.35 4.60 4.95 4.20 3.25 4.05 3.50 3.85
[316,] 3.95 4.55 4.00 4.10 3.50 5.10 5.15 4.45 4.35 4.40 4.50 4.05 2.90 3.80 3.85 4.00
[317,] 3.50 5.30 4.25 4.00 3.60 4.70 5.35 3.80 5.05 4.25 4.70 3.95 3.35 4.20 4.25 4.25
[318,] 3.55 4.45 3.60 3.95 3.55 5.00 4.80 4.35 4.35 4.10 4.95 4.05 3.10 4.10 4.00 3.75
[319,] 3.10 4.40 4.10 4.00 4.35 4.95 5.00 4.15 4.95 4.40 4.85 3.95 3.05 3.55 4.30 3.55
[320,] 3.50 4.85 3.70 3.85 4.00 4.80 5.55 4.00 3.65 4.60 4.80 3.90 3.40 3.30 3.60 4.10
[321,] 3.10 4.35 3.15 3.80 3.55 4.65 4.85 3.75 4.35 4.95 4.45 4.00 3.90 3.65 4.35 4.25
[322,] 3.70 4.80 3.35 4.15 3.45 4.90 5.15 4.30 4.55 3.95 4.65 3.90 2.80 3.25 4.15 4.20
[323,] 3.25 4.95 3.55 4.45 3.20 4.50 5.00 4.80 3.90 4.30 4.95 4.35 3.25 3.70 3.95 3.95
[324,] 3.60 5.55 3.85 3.80 2.95 4.70 5.15 4.25 3.50 4.50 4.50 4.05 3.00 3.65 4.15 3.55
[325,] 3.40 4.60 3.80 3.75 3.35 4.95 4.85 4.25 4.05 4.15 4.25 4.30 2.85 4.05 4.95 3.85
[326,] 3.50 5.55 3.30 4.30 4.00 5.05 4.95 4.25 5.15 5.00 4.50 3.30 3.70 3.55 3.60 3.45
[327,] 3.15 4.70 3.85 4.20 4.35 5.25 5.05 4.70 4.35 4.10 4.20 3.95 3.25 3.55 4.25 4.25
[328,] 3.60 4.55 4.35 4.50 2.95 4.65 4.60 3.60 4.30 4.45 4.85 4.60 3.65 3.85 3.95 4.15
[329,] 3.30 4.65 3.65 3.85 3.35 5.10 5.40 4.40 3.95 4.70 4.60 3.65 3.10 3.35 4.20 3.85
[330,] 3.65 5.20 3.45 4.25 3.10 5.20 5.25 4.80 4.10 4.20 5.10 4.55 3.40 3.85 3.70 4.20
[331,] 3.50 4.95 3.95 4.15 3.65 4.80 4.80 4.20 4.30 4.50 4.45 4.05 3.15 3.55 3.90 3.30
[332,] 3.35 4.60 4.05 4.10 3.45 5.10 5.40 4.30 4.85 4.60 4.30 4.15 3.20 3.95 4.45 3.50
[333,] 4.10 4.75 4.15 3.65 3.50 5.35 5.15 4.20 4.75 4.55 4.55 4.05 3.10 3.25 3.50 3.75
[334,] 3.45 4.90 4.05 4.05 4.80 4.85 5.35 3.80 4.05 4.15 4.40 3.70 3.35 3.60 3.90 4.10
[335,] 3.75 5.35 3.35 3.80 3.95 5.25 5.15 3.65 4.35 4.65 4.40 4.05 2.55 4.00 4.95 3.30
[336,] 3.65 5.55 3.65 3.70 3.20 5.15 4.70 4.15 4.60 4.05 4.65 4.40 3.15 3.80 3.85 3.95
[337,] 3.95 4.95 3.80 3.90 3.15 4.90 5.65 4.50 4.40 4.45 5.05 3.70 2.95 4.25 4.15 4.70
[338,] 3.95 4.50 4.10 3.95 3.20 5.50 4.90 4.55 4.35 5.10 4.35 3.70 3.35 4.05 4.40 4.25
[339,] 4.20 5.40 3.85 3.30 3.65 4.90 5.35 4.60 4.40 4.65 5.00 3.40 3.40 3.85 4.90 3.70
[340,] 3.75 4.35 4.85 3.85 3.70 4.75 5.25 4.35 4.35 4.10 4.90 3.95 3.70 4.05 3.90 4.40
[341,] 3.05 5.95 3.65 4.10 3.50 4.35 5.10 4.45 4.10 4.25 4.55 3.90 3.25 3.85 3.90 3.75
[342,] 3.30 5.10 3.65 4.20 3.75 4.50 5.45 4.00 4.60 4.50 5.20 3.50 3.40 3.75 4.30 3.55
[343,] 3.35 4.85 3.90 4.10 3.40 5.35 5.05 3.95 4.20 4.70 4.75 4.05 2.65 3.90 4.15 3.60
[344,] 3.75 5.00 2.95 4.00 3.80 5.00 5.35 3.80 4.60 4.45 4.60 4.20 3.15 4.15 3.85 3.80
[345,] 3.75 5.15 3.95 4.10 2.75 5.25 5.30 3.50 4.35 4.25 4.80 4.20 3.05 3.30 3.45 3.20
[346,] 3.85 5.30 4.00 4.05 2.95 5.25 4.85 4.35 4.35 4.20 4.50 3.75 3.40 3.60 4.10 3.65
[347,] 3.30 4.90 3.95 4.00 3.25 5.15 5.05 3.95 4.20 4.35 4.75 4.40 2.80 3.90 4.55 3.60
[348,] 3.70 4.65 3.95 3.90 3.55 5.05 4.90 4.55 4.45 4.25 4.85 4.25 3.40 3.40 3.60 3.95
[349,] 3.65 4.60 4.20 4.05 4.00 5.05 5.50 3.65 4.30 4.55 4.70 3.85 3.10 3.80 4.05 4.10
[350,] 3.45 4.80 3.85 3.35 3.75 4.65 4.85 4.55 3.95 4.40 4.65 4.35 3.25 4.20 4.45 3.65
[351,] 3.25 4.85 4.00 4.25 3.30 4.95 4.90 4.35 4.75 4.60 4.45 3.75 3.45 4.40 4.30 3.75
[352,] 3.65 5.15 3.80 3.75 3.90 4.75 5.30 4.25 4.30 4.40 4.35 3.75 2.60 4.35 4.00 4.30
[353,] 3.10 5.50 3.60 3.90 3.95 4.85 5.10 4.40 4.30 4.40 4.95 4.20 3.35 3.50 3.60 3.60
[354,] 3.45 5.00 4.05 3.95 3.60 4.95 4.95 4.45 4.30 4.05 5.05 4.15 3.25 3.65 4.15 4.10
[355,] 3.15 5.05 3.95 3.90 3.55 5.05 5.05 4.60 4.25 4.15 4.65 4.25 3.45 4.00 3.90 4.10
[356,] 3.65 5.15 4.10 4.20 3.70 4.70 4.60 3.40 4.85 4.35 4.60 4.00 3.30 3.50 4.10 3.80
[357,] 3.45 5.60 3.45 3.95 3.60 5.10 5.25 3.25 4.90 4.45 4.70 4.10 3.40 2.65 3.90 4.45
[358,] 3.70 4.70 3.95 3.80 3.40 4.75 4.80 3.80 4.40 4.65 4.85 4.15 3.55 3.45 3.65 4.20
[359,] 3.75 5.15 3.30 3.55 3.60 5.00 5.10 4.00 3.70 4.20 5.00 4.15 3.25 4.05 4.20 3.95
[360,] 3.50 4.95 3.95 3.90 3.80 4.85 4.95 4.05 4.50 4.30 4.45 4.20 2.85 3.70 4.00 4.15
[361,] 3.30 4.65 3.70 4.25 3.75 5.40 5.35 3.70 4.00 4.30 4.55 4.45 2.25 3.65 4.40 3.95
[362,] 2.95 5.00 3.55 4.20 3.70 5.15 5.10 4.80 4.30 4.15 4.45 3.75 3.30 3.90 4.35 4.15
[363,] 3.50 5.25 3.05 4.35 3.45 4.85 4.85 3.85 4.35 4.55 4.75 4.05 3.65 3.65 5.05 4.20
[364,] 3.25 5.20 3.50 3.85 4.05 4.90 5.15 4.10 5.20 4.90 5.15 4.40 3.45 3.65 4.35 3.85
[365,] 3.05 4.30 3.95 4.05 3.60 4.40 5.25 4.10 4.20 4.65 4.60 4.15 3.35 3.75 4.55 3.85
[366,] 3.30 5.05 3.95 4.25 3.50 5.20 5.30 4.50 4.40 4.55 4.20 4.10 3.25 3.95 3.25 3.60
[367,] 4.05 4.60 4.00 3.90 4.00 4.85 5.40 3.85 4.00 4.25 4.75 4.20 2.95 3.45 4.25 3.95
[368,] 3.30 4.90 4.05 4.20 4.55 4.55 4.85 3.85 4.75 4.60 4.80 3.95 3.35 3.35 4.20 3.75
[369,] 4.55 5.40 4.05 3.65 3.90 5.15 5.35 3.85 4.55 4.45 4.35 3.85 2.65 3.90 3.90 4.40
[370,] 3.90 4.95 4.15 4.15 3.70 4.85 5.60 4.20 4.65 4.50 4.40 3.45 2.65 3.70 4.95 3.45
[371,] 2.75 4.80 4.10 3.55 3.35 5.25 5.30 4.55 3.75 4.95 4.60 4.35 3.25 3.20 4.05 4.45
[372,] 3.65 4.65 3.60 3.60 3.15 4.55 5.15 4.25 4.85 4.65 5.15 4.60 3.60 3.50 3.95 3.70
[373,] 4.00 5.20 3.85 4.10 3.85 4.95 5.40 4.35 4.50 4.10 4.50 3.55 3.50 4.05 3.95 3.60
[374,] 3.45 4.85 4.30 3.80 3.90 5.25 5.30 3.60 4.25 4.60 4.80 3.90 2.85 3.80 4.35 4.15
[375,] 3.00 4.90 3.60 3.65 4.00 5.35 5.00 4.55 4.15 4.35 4.70 3.95 3.15 3.70 3.90 3.25
[376,] 3.45 4.35 3.75 3.80 4.10 5.00 5.15 4.40 4.10 4.35 4.05 4.00 3.20 3.50 4.30 3.80
[377,] 3.85 4.50 4.45 3.35 3.50 4.95 5.00 4.95 3.90 4.10 4.80 4.10 3.70 3.50 3.45 3.75
[378,] 3.60 4.80 3.80 3.50 3.35 4.55 4.75 4.65 4.55 4.25 4.50 3.90 3.15 3.45 4.45 4.30
[379,] 3.10 4.20 4.15 4.00 3.40 4.70 4.60 4.20 4.15 4.65 4.75 4.10 3.65 4.30 5.05 3.70
[380,] 3.15 4.90 3.10 3.45 3.50 4.85 4.90 3.65 4.05 4.85 4.35 4.05 3.30 3.10 4.50 3.85
[381,] 3.50 5.00 3.75 3.85 3.95 4.75 5.60 4.15 5.00 4.50 4.10 3.60 2.90 3.65 4.30 3.50
[382,] 4.10 4.95 4.10 4.20 4.10 4.95 5.00 4.85 5.00 4.65 4.50 3.70 3.90 4.00 4.50 4.20
[383,] 3.65 4.35 3.60 3.85 3.40 5.00 5.05 4.45 3.75 4.45 4.55 4.60 3.15 3.20 3.90 3.45
[384,] 3.10 4.85 3.60 4.35 3.80 5.05 4.75 4.20 4.35 4.65 4.55 4.00 3.35 3.95 3.65 4.35
[385,] 3.15 4.45 4.25 3.80 3.45 5.00 5.25 4.30 4.05 4.70 4.70 4.10 3.05 4.00 3.90 4.30
[386,] 3.25 4.80 4.05 3.90 3.55 4.55 5.25 4.50 4.35 4.40 4.50 3.90 3.35 4.10 3.70 3.55
[387,] 3.45 5.25 3.75 3.70 3.80 5.10 4.95 3.95 4.65 4.45 4.50 4.30 3.05 4.00 4.45 2.75
[388,] 3.25 4.75 3.50 4.40 4.30 4.35 5.45 4.45 4.90 4.50 4.80 4.25 3.50 3.75 4.75 3.15
[389,] 2.90 4.95 3.95 3.10 3.85 4.90 5.05 4.25 4.55 4.80 4.95 4.00 4.05 4.10 4.45 4.65
[390,] 3.80 5.10 3.60 3.75 3.25 5.20 5.05 4.30 3.95 4.25 5.15 4.10 3.15 3.75 4.25 4.10
[391,] 3.80 4.80 4.00 4.30 3.45 5.20 5.55 4.30 4.65 4.35 4.70 3.55 2.85 3.80 3.85 4.40
[392,] 3.40 4.20 3.75 3.85 3.45 4.90 4.55 3.85 3.85 4.35 4.55 4.25 2.70 3.80 4.40 3.70
[393,] 3.25 4.85 3.80 3.85 3.10 4.55 5.15 4.60 4.40 4.70 4.85 4.35 3.25 3.30 3.50 3.75
[394,] 3.80 4.95 4.10 3.70 3.25 4.85 4.75 4.25 4.00 4.75 4.30 3.70 3.90 3.75 3.40 3.60
[395,] 3.65 5.25 4.00 3.60 3.00 4.65 4.70 4.70 4.40 4.35 4.60 4.00 3.35 3.55 3.85 4.20
[396,] 3.80 4.45 3.65 3.50 3.50 4.65 5.15 4.55 4.30 4.50 4.60 4.55 3.00 3.25 4.00 3.60
[397,] 3.35 5.15 4.20 4.40 3.60 5.15 4.90 4.10 4.65 4.55 4.90 4.05 3.30 3.45 4.00 3.85
[398,] 3.35 4.50 4.25 4.10 3.10 5.30 4.35 3.85 4.35 4.55 4.60 4.35 3.25 4.25 4.60 4.05
[399,] 3.40 4.90 3.85 3.90 3.75 5.00 4.90 4.60 4.50 4.20 5.00 4.20 3.35 3.85 4.30 4.15
[400,] 4.40 5.35 3.45 3.65 3.80 4.95 5.25 3.75 4.40 4.55 4.80 3.95 3.05 4.15 4.30 3.40
[401,] 3.70 5.00 3.65 4.05 3.25 4.70 4.90 4.85 4.30 4.90 4.70 3.70 3.05 3.95 4.10 3.60
[402,] 4.00 4.65 3.75 4.00 3.30 5.50 5.00 3.60 4.50 3.70 4.95 4.65 4.10 3.15 4.95 3.90
[403,] 3.55 4.45 3.90 4.10 4.00 5.00 5.25 4.00 4.45 4.75 4.60 3.80 3.15 3.60 3.95 3.90
[404,] 3.35 4.75 3.55 4.30 4.00 5.05 5.25 4.20 4.20 4.55 4.85 3.65 3.35 3.65 4.35 4.30
[405,] 3.40 5.15 3.90 3.65 3.70 5.10 4.80 4.05 4.90 4.55 4.60 3.45 3.05 3.75 4.55 3.80
[406,] 3.85 4.50 3.65 4.45 3.10 5.20 5.25 3.60 4.85 4.50 4.60 3.95 3.45 3.60 4.00 3.85
[407,] 3.35 5.20 4.30 4.00 3.65 5.05 5.30 5.05 4.45 4.25 4.80 4.00 3.05 4.00 4.40 3.75
[408,] 3.55 4.00 3.10 4.05 3.00 4.80 5.00 3.95 3.60 5.10 4.60 3.70 2.85 3.80 3.55 4.30
[409,] 3.25 5.50 3.05 3.70 3.40 4.70 4.60 4.45 4.75 4.25 4.55 3.85 3.20 3.80 4.25 4.15
[410,] 3.15 4.35 3.90 4.30 3.60 5.20 4.65 4.05 3.95 4.50 4.85 4.20 3.20 4.10 4.30 3.95
[411,] 3.30 5.30 3.40 3.60 3.85 5.35 4.95 3.55 4.50 4.25 4.70 4.20 3.20 3.95 4.10 4.00
[412,] 3.40 5.85 3.50 4.30 4.30 5.10 4.55 3.65 4.50 4.90 4.60 4.10 3.45 3.90 4.50 3.90
[413,] 3.20 5.10 3.75 4.10 3.55 4.80 4.45 4.75 4.95 4.60 4.15 4.30 2.95 3.10 3.95 3.85
[414,] 3.65 4.75 3.70 4.35 4.15 5.00 5.35 4.30 4.00 4.40 4.70 4.35 3.20 3.50 4.70 4.05
[415,] 3.25 4.60 3.70 3.70 3.55 5.00 5.10 3.80 4.35 4.35 4.80 3.90 3.30 3.60 3.75 3.60
[416,] 3.60 5.00 3.30 3.90 3.65 5.05 5.15 4.00 4.35 4.25 4.75 4.00 3.35 3.80 3.90 3.35
[417,] 3.05 4.60 4.45 3.80 3.75 4.70 5.00 4.65 4.90 4.55 4.50 4.35 2.90 3.80 3.75 4.50
[418,] 3.30 5.20 3.85 4.00 3.20 4.65 4.80 4.25 4.15 4.50 4.45 3.85 2.45 3.55 4.45 4.85
[419,] 3.80 4.70 3.90 3.95 4.00 5.20 5.00 4.55 4.40 4.15 4.80 4.20 2.90 4.75 4.45 3.60
[420,] 3.75 5.00 3.35 3.55 3.75 4.70 5.00 4.00 4.50 4.40 4.90 4.35 3.00 4.45 3.85 3.05
[421,] 3.25 5.40 3.80 4.10 3.55 4.70 5.35 4.25 4.50 4.35 4.60 3.95 3.40 3.35 4.25 3.45
[422,] 3.75 4.70 4.60 3.90 3.90 5.10 5.35 3.85 4.25 4.35 4.80 4.05 3.00 3.90 3.95 3.60
[423,] 3.30 5.25 3.50 4.30 3.75 5.20 4.95 4.40 3.90 4.30 4.95 4.05 3.65 3.55 4.50 3.30
[424,] 3.60 4.85 3.85 4.10 3.45 4.90 5.40 4.50 5.05 4.45 4.85 3.25 3.40 3.35 3.75 4.00
[425,] 4.15 5.65 3.75 3.90 4.00 5.10 5.20 4.75 4.80 4.55 5.10 4.05 3.20 3.55 4.15 4.25
[426,] 3.10 4.40 3.45 3.80 3.85 4.95 4.85 4.05 5.05 4.00 4.65 4.05 3.35 3.70 4.10 4.00
[427,] 3.35 4.60 4.20 4.10 3.55 5.25 5.20 3.80 4.30 4.35 4.20 3.60 2.95 3.50 3.75 3.60
[428,] 3.55 4.90 4.35 3.80 3.85 5.15 5.40 4.15 4.60 4.60 4.50 3.90 3.40 3.65 4.40 4.20
[429,] 3.45 5.70 3.40 4.15 3.75 5.10 4.60 3.85 4.05 4.60 4.60 4.10 3.15 3.40 4.70 3.70
[430,] 3.80 4.45 3.45 3.85 3.85 5.10 4.70 4.55 4.00 4.15 4.90 4.50 3.25 3.70 4.00 3.75
[431,] 3.35 5.60 3.20 4.10 4.10 5.00 5.30 3.95 4.50 4.45 4.65 4.10 2.75 3.65 4.05 4.35
[432,] 3.75 4.40 3.70 3.75 4.00 4.85 4.55 4.40 5.25 4.30 4.40 3.85 3.20 4.00 4.05 3.40
[433,] 3.35 4.85 3.95 4.10 3.75 5.15 4.90 4.55 4.30 4.65 4.70 4.30 3.35 3.60 4.60 4.00
[434,] 3.15 4.80 4.20 3.90 3.10 4.95 5.00 4.10 4.25 4.45 4.55 4.30 2.65 4.00 4.85 3.85
[435,] 4.10 5.15 3.60 3.95 3.95 5.30 5.15 3.85 4.30 4.35 4.55 4.00 3.10 3.60 4.10 3.90
[436,] 3.05 5.15 4.05 4.25 3.80 4.85 5.30 4.35 4.50 4.45 4.80 4.05 3.05 3.70 4.25 4.10
[437,] 3.30 5.35 3.30 4.65 2.85 4.90 5.15 4.70 3.95 4.65 4.40 4.30 3.10 4.25 4.40 3.20
[438,] 3.85 4.90 3.85 3.85 3.20 4.95 5.00 3.85 4.30 4.60 4.55 4.30 2.95 3.30 3.80 3.95
[439,] 3.25 5.15 4.35 4.20 3.60 5.05 4.60 4.55 4.70 4.40 4.95 3.85 3.30 3.90 3.95 3.80
[440,] 3.60 5.00 3.90 3.75 3.65 5.15 4.80 3.75 4.70 4.90 4.75 4.00 3.45 3.55 4.75 4.35
[441,] 3.20 5.00 3.50 3.70 3.05 4.90 5.25 3.65 4.80 4.55 4.60 4.30 3.15 4.15 4.25 3.70
[442,] 3.40 5.20 4.00 3.45 2.95 5.00 5.15 4.00 4.50 4.75 4.65 4.00 3.50 3.95 4.15 4.35
[443,] 3.35 4.20 4.05 3.90 3.15 4.70 5.05 4.20 5.10 4.30 4.40 4.30 3.50 3.30 4.30 3.35
[444,] 3.10 4.80 3.80 3.95 3.45 4.70 5.25 4.30 4.80 4.40 4.45 3.70 2.75 4.40 3.30 4.05
[445,] 3.30 5.65 3.55 4.20 3.60 5.40 5.30 4.20 4.55 4.90 4.60 3.95 3.50 3.60 4.05 4.05
[446,] 3.25 5.10 3.80 4.15 4.15 5.15 5.15 3.75 4.30 4.30 4.55 3.55 3.35 4.15 3.35 3.50
[447,] 3.60 5.85 3.75 4.35 3.75 5.05 4.90 4.60 4.90 4.40 4.55 4.30 2.95 3.95 3.90 4.05
[448,] 3.30 5.05 3.85 3.95 3.35 4.95 5.00 3.75 4.25 4.70 4.55 3.95 3.25 3.80 4.00 3.60
[449,] 3.10 4.65 3.25 3.95 3.85 5.15 5.20 4.10 4.55 4.45 5.15 4.10 3.05 3.95 4.30 4.15
[450,] 3.40 4.70 3.45 4.00 3.95 4.65 5.50 3.85 4.50 4.80 5.05 4.60 2.85 3.50 3.80 4.50
[451,] 3.50 4.90 3.40 4.15 3.25 5.35 5.55 3.80 4.30 4.35 4.85 4.10 3.25 3.70 4.15 3.30
[452,] 4.05 5.05 3.95 3.85 4.20 4.95 5.45 4.35 3.55 4.90 4.80 4.40 3.25 3.90 4.35 3.90
[453,] 3.40 4.95 3.65 3.75 3.55 4.70 5.10 4.15 4.65 4.40 4.55 3.60 3.55 3.70 4.60 3.75
[454,] 3.15 4.65 3.60 4.10 3.50 4.85 5.00 4.00 4.20 4.60 4.45 3.75 2.85 3.70 3.70 4.05
[455,] 3.45 4.80 3.60 4.35 3.95 5.00 5.00 3.45 4.15 4.40 4.55 3.65 3.30 3.00 4.10 4.05
[456,] 3.90 5.25 3.50 3.50 3.80 4.70 4.70 4.95 4.85 4.15 4.60 3.65 3.05 3.95 4.85 3.05
[457,] 3.90 4.55 4.10 3.60 4.15 4.65 4.85 4.35 4.05 5.00 4.80 4.10 3.30 3.55 4.90 3.40
[458,] 3.35 4.05 3.85 3.75 4.05 5.00 5.05 3.85 4.75 4.40 4.70 3.75 3.65 3.60 4.00 3.70
[459,] 3.35 4.45 3.35 3.85 3.95 4.75 4.90 3.55 4.35 4.25 4.85 4.20 3.50 3.90 4.10 3.70
[460,] 4.40 4.55 3.85 3.60 3.20 5.15 5.25 3.80 4.30 4.95 4.65 3.95 2.80 3.80 4.50 3.55
[461,] 3.05 4.25 3.70 4.05 3.50 4.80 5.40 3.85 4.50 4.10 4.10 4.10 3.75 3.65 4.60 3.60
[462,] 2.75 5.00 3.35 4.00 3.95 5.20 4.75 3.70 4.40 3.80 4.50 3.85 3.40 3.70 4.30 3.60
[463,] 2.95 5.20 3.55 3.60 3.35 5.10 5.05 4.05 4.15 4.40 4.65 4.05 3.20 3.60 4.10 3.65
[464,] 3.70 4.85 3.25 3.75 3.10 4.95 5.20 3.80 4.75 4.20 4.80 4.25 3.70 3.85 3.75 4.65
[465,] 3.75 4.80 4.05 4.20 3.35 4.60 5.00 4.00 4.40 4.30 4.95 3.95 3.00 4.50 4.25 4.15
[466,] 3.25 5.30 3.35 3.50 2.45 4.95 5.40 3.60 4.35 4.30 4.60 3.80 3.15 3.45 4.20 3.95
[467,] 3.25 4.70 4.10 4.05 3.35 5.20 5.45 4.05 5.00 4.40 5.25 3.90 2.65 3.80 3.90 3.90
[468,] 3.35 4.85 2.90 3.70 3.40 5.20 5.20 4.45 5.05 4.65 5.20 4.60 3.10 4.15 4.35 4.30
[469,] 3.60 4.80 4.15 4.20 3.90 4.95 5.00 3.75 4.85 4.40 4.90 3.60 3.20 3.80 4.20 3.95
[470,] 3.75 5.25 3.25 3.75 3.05 4.85 4.95 4.60 4.30 4.90 4.70 3.70 3.20 4.05 3.40 3.45
[471,] 3.40 4.40 3.40 3.95 2.90 4.85 5.25 4.05 3.80 4.20 4.80 4.05 3.80 3.65 3.85 4.20
[472,] 3.60 4.90 3.30 3.70 4.10 4.90 4.95 3.90 4.45 4.65 4.60 3.85 3.10 3.60 4.70 3.75
[473,] 3.95 4.50 4.50 3.80 3.95 5.10 5.00 4.85 4.45 4.25 4.35 3.75 3.35 4.05 4.30 4.00
[474,] 3.35 4.25 3.60 3.95 4.30 4.90 5.05 4.55 4.45 4.25 4.35 4.15 3.50 3.80 4.30 3.85
[475,] 3.30 4.90 3.50 3.80 3.80 4.70 4.95 4.60 4.05 3.90 4.45 4.35 3.50 3.75 4.25 4.20
[476,] 3.35 5.05 3.40 4.05 3.45 4.90 5.65 4.55 4.45 4.45 4.05 4.15 3.20 3.55 4.60 4.30
[477,] 3.60 4.35 3.35 4.55 3.45 5.25 5.35 4.25 3.90 4.35 4.60 4.15 3.35 3.55 4.75 3.65
[478,] 3.75 5.15 3.55 4.05 4.95 5.10 4.75 4.15 4.55 4.75 4.60 3.85 2.95 4.20 4.45 4.40
[479,] 3.55 5.05 4.00 4.05 3.70 4.85 5.05 4.35 4.20 4.60 4.50 3.80 3.70 4.05 3.75 3.70
[480,] 3.05 4.55 3.40 4.25 3.65 4.75 4.85 3.55 3.90 4.30 4.50 3.65 3.65 3.90 4.20 4.00
[481,] 3.15 5.80 3.25 4.15 4.00 5.30 5.25 4.30 4.85 4.45 4.15 4.00 3.25 3.45 4.25 4.45
[482,] 2.80 5.05 3.70 4.25 3.55 5.40 5.35 4.50 4.00 4.25 4.85 4.55 3.30 4.10 4.35 4.45
[483,] 3.35 5.00 3.85 3.90 3.95 4.65 4.80 4.40 4.40 4.35 4.65 3.85 3.20 3.90 4.55 3.70
[484,] 3.75 4.50 3.70 3.95 3.70 4.80 5.00 4.55 4.20 4.55 4.50 4.00 2.85 3.70 3.85 3.90
[485,] 3.70 5.25 3.80 3.75 3.70 4.85 5.35 3.75 4.35 4.45 4.40 4.10 2.80 3.75 3.95 3.50
[486,] 3.05 4.30 3.50 3.80 4.10 5.15 5.20 4.15 4.75 4.40 4.90 4.60 3.35 3.70 3.40 4.15
[487,] 3.50 5.30 3.70 4.40 3.60 5.05 5.20 3.60 4.05 4.25 4.90 3.65 3.15 4.00 4.40 3.90
[488,] 2.75 4.60 3.90 4.15 3.70 4.95 5.15 5.05 4.60 4.75 4.70 4.25 3.00 3.80 4.85 3.10
[489,] 3.45 4.45 3.95 4.45 3.30 4.75 5.15 3.80 4.55 4.45 4.95 4.05 3.05 4.20 4.90 3.75
[490,] 4.20 4.95 3.60 3.90 3.55 4.95 4.95 4.40 4.30 4.40 4.85 3.35 3.05 3.85 4.10 3.85
[491,] 3.45 4.50 4.15 4.20 4.10 4.85 5.10 3.95 4.40 4.75 4.80 4.20 3.30 3.35 4.60 3.95
[492,] 3.30 4.55 4.10 3.50 3.25 4.75 5.35 4.15 4.15 4.55 4.55 3.35 3.05 3.35 4.45 3.55
[493,] 3.90 4.25 3.25 4.00 4.05 4.70 4.90 3.70 4.05 4.40 4.80 4.40 3.20 3.60 3.40 3.30
[494,] 4.10 5.35 3.35 4.35 3.75 5.15 5.30 4.35 5.00 4.55 4.95 4.65 3.80 4.35 3.95 4.00
[495,] 3.80 5.15 3.35 4.00 3.65 5.00 4.85 3.30 4.55 4.00 4.30 4.05 3.25 3.60 3.00 3.65
[496,] 3.65 5.45 3.15 4.05 4.05 5.20 5.40 4.00 4.50 4.75 4.30 3.90 3.15 3.70 3.90 3.85
[497,] 3.35 4.95 3.40 4.35 3.35 5.00 4.40 3.95 4.30 5.05 4.35 3.80 3.00 3.65 4.00 4.30
[498,] 3.10 4.80 3.00 3.85 3.10 5.00 4.65 4.10 4.40 4.45 4.35 4.05 2.80 3.75 3.75 3.80
[499,] 3.85 5.45 3.35 4.00 4.05 5.30 5.65 3.80 4.20 5.00 5.05 3.50 2.95 3.45 3.25 3.80
[500,] 3.05 4.65 4.30 3.20 3.95 5.50 4.75 4.15 4.65 4.20 5.05 3.65 3.55 3.60 3.90 3.90

=========================================
Fernando Souza
Zootecnista, DSc. Produção e Alimentação Animal
Celular: (31)99796-8781 (Vivo)
E-mail:nandodesouza@gmail.com
Lattes: http://lattes.cnpq.br/6519538815038307
Blog: https://producaoanimalcomr.wordpress.com/
==========================================

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On Mai 19 2017, at 10:45 am, Fernando Antonio de souza <nandodesouza@gmail.com> wrote:

Faça um dput(dados), copie e cole para que eu possa obter seus dados aqui

att

=========================================
Fernando Souza
Zootecnista, DSc. Produção e Alimentação Animal
Celular: (31)99796-8781 (Vivo)
E-mail:nandodesouza@gmail.com
Lattes: http://lattes.cnpq.br/6519538815038307
Blog: https://producaoanimalcomr.wordpress.com/
==========================================

Sent from Nylas Mail, the best free email app for work
On Mai 18 2017, at 11:19 pm, sznelwar@uol.com.br wrote:
Tentei rodar, mas o meu resultado dá muito diferente, qual o motivo?

> dados <- read.table("dados_boot.txt", h=T)

> dados

X4 X5 X15 X6 X11 X12 X13 X16 X17 X18 X19 X20 X25 X26 X27 X29

1 3 4 3 2 3 4 4 2 3 3 3 5 3 4 3 3

2 2 5 5 5 3 5 5 4 5 4 5 4 4 3 5 3

3 4 5 4 3 5 5 5 4 5 6 6 5 4 4 5 4

4 2 6 4 4 2 6 6 4 4 3 6 6 3 4 4 2

5 4 3 1 3 1 4 5 3 3 1 3 1 2 1 2 1

6 5 5 6 5 5 6 6 6 5 5 6 4 5 5 5 4

7 1 7 4 5 7 6 7 5 4 5 5 3 4 4 7 6

8 5 7 6 5 4 6 6 3 7 5 4 3 5 7 5 6

9 5 6 4 3 5 5 5 4 4 4 3 2 2 5 2 5

10 3 6 2 5 5 6 6 6 4 4 5 5 2 2 6 3

11 2 2 3 2 2 4 4 4 3 4 5 4 5 3 4 3

12 2 3 2 2 3 2 5 3 2 4 5 2 4 5 3 2

13 7 7 4 3 3 6 7 6 6 6 6 6 4 4 6 5

14 5 7 6 6 7 6 6 7 7 6 5 5 3 4 6 6

15 3 6 5 6 5 6 6 5 6 6 5 5 4 4 6 6

16 1 2 1 4 1 5 2 2 3 6 5 4 1 2 1 1

17 4 5 2 4 3 4 3 5 5 4 3 3 2 4 3 4

18 5 6 5 5 3 5 5 6 7 6 6 5 4 3 6 6

19 4 3 5 3 1 5 4 2 3 4 4 5 1 4 2 5

20 3 3 3 4 4 3 4 2 2 3 3 3 2 3 1 2

> mean(dados[,1])

[1] 3.5

> library(sem)

> library(boot)

>

> #Bootstrap (médias das replicações)

>

>

> obs <- function(x){

+

+ results <- c()

+ for(i in 1:500){

+

+ results[i] <- mean(sample(dados,replace=T))

+

+ saida <- paste(results,i,sep="")

+

+ }

+

+ return(saida)

+ }

>

> resultado<-apply(dados,2,obs)

There were 50 or more warnings (use warnings() to see the first 50)

>

> resultado[,1]

[1] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[10] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[19] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[28] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[37] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[46] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[55] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[64] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[73] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[82] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[91] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[100] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[109] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[118] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[127] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[136] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[145] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[154] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[163] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[172] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[181] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[190] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[199] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[208] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[217] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[226] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[235] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[244] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[253] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[262] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[271] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[280] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[289] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[298] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[307] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[316] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[325] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[334] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[343] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[352] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[361] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[370] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[379] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[388] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[397] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[406] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[415] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[424] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[433] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[442] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[451] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[460] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[469] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[478] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[487] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"

[496] "NA500" "NA500" "NA500" "NA500" "NA500"

>

Como eu falei,

Basta retirar a variável "saída" da função "obs" que a saída será do tipo numeric e aí funcionará seu comando

segue função obs , corrigida

obs <- function(x){

results <- c()
for(i in 1:500){

results[i] <- mean( sample(x,replace=TRUE))

}

return(results)
}

resultado <- apply(dados,2,obs)

str(resultado)

=========================================
Fernando Souza
Zootecnista, DSc. Produção e Alimentação Animal
Celular: (31)99796-8781 (Vivo)
E-mail:nandodesouza@gmail.com
Lattes: http://lattes.cnpq.br/6519538815038307
Blog: https://producaoanimalcomr.wordpress.com/
==========================================

Sent from Nylas Mail, the best free email app for work
On Mai 18 2017, at 9:44 am, Fernando Antonio de souza <nandodesouza@gmail.com> wrote:
Clodoaldo

Estou sem acesso a meu computador aqui é por as não posso testar. Tente rodar a função obs se a variável saida. Acredito que ela está transformando a saída em carácter e por isso a saída.

Assim q acessar meu PC testo e lhe confirmo.

obs <- function(x){

       results <- c()
       for(i in 1:500){

           results[i] <- mean(sample(dados,replace=T))

       }

return(results)
}
Em 18/05/2017 9:24 AM, "Clodoaldo José Figueredo" <clodoaldo.figueredo@ifc-araquari.edu.br> escreveu:
Caro Fernando (e demais colegas)

Gostaria de agradecer sua grande ajuda, mas preciso tirar mais outras dúvidas.
Abaixo está o código completo e a matriz dados que é base para os cálculos.
A saída que inseri é a coluna resultado [,1]

Porque não consigo calcular a média dos valores?
As saídas serão usadas como vetores (individualmente) em um outro procedimento para uso do pacote "sem" (Modelagem de equações estruturais).
Como tira as aspas, ou não tem nada a ver?

Porque aparecem valores 4500 e 3500 no meio dos calculados? (Não tem lógica)

setwd("C:/Dados")
dados <- read.table("Dados_Boot_txt.txt", h=T)

dados
> dados
   X4 X5 X15 X6 X11 X12 X13 X16 X17 X18 X19 X20 X25 X26 X27 X29
1   3  4   3  2   3   4   4   2   3   3   3   5   3   4   3   3
2   2  5   5  5   3   5   5   4   5   4   5   4   4   3   5   3
3   4  5   4  3   5   5   5   4   5   6   6   5   4   4   5   4
4   2  6   4  4   2   6   6   4   4   3   6   6   3   4   4   2
5   4  3   1  3   1   4   5   3   3   1   3   1   2   1   2   1
6   5  5   6  5   5   6   6   6   5   5   6   4   5   5   5   4
7   1  7   4  5   7   6   7   5   4   5   5   3   4   4   7   6
8   5  7   6  5   4   6   6   3   7   5   4   3   5   7   5   6
9   5  6   4  3   5   5   5   4   4   4   3   2   2   5   2   5
10  3  6   2  5   5   6   6   6   4   4   5   5   2   2   6   3
11  2  2   3  2   2   4   4   4   3   4   5   4   5   3   4   3
12  2  3   2  2   3   2   5   3   2   4   5   2   4   5   3   2
13  7  7   4  3   3   6   7   6   6   6   6   6   4   4   6   5
14  5  7   6  6   7   6   6   7   7   6   5   5   3   4   6   6
15  3  6   5  6   5   6   6   5   6   6   5   5   4   4   6   6
16  1  2   1  4   1   5   2   2   3   6   5   4   1   2   1   1
17  4  5   2  4   3   4   3   5   5   4   3   3   2   4   3   4
18  5  6   5  5   3   5   5   6   7   6   6   5   4   3   6   6
19  4  3   5  3   1   5   4   2   3   4   4   5   1   4   2   5
20  3  3   3  4   4   3   4   2   2   3   3   3   2   3   1   2
mean(dados[,1])
> mean(dados[,1])
[1] 3.5
library(sem)
library(boot)

#Bootstrap (médias das replicações)

obs <- function(x){

       results <- c()
       for(i in 1:500){

           results[i] <- mean(sample(dados,replace=T))

           saida <- paste(results,i,sep="")

       }

return(saida)
}

resultado<-apply(dados,2,obs)

resultado[,1]
> resultado[,1]
  [1] "3.75500" "4.1500"  "4.05500" "3.5500"  "3.8500"  "4.1500"  "3.45500" "4.75500"
  [9] "4.25500" "3.45500" "3.6500"  "3.85500" "3.9500"  "4.05500" "3.6500"  "3.75500"
 [17] "4.2500"  "4.4500"  "3.65500" "4.2500"  "4500"    "4.15500" "5.05500" "3.75500"
 [25] "3.8500"  "4.45500" "4.25500" "3.65500" "3.65500" "4.05500" "4.05500" "3.3500" 
 [33] "3.4500"  "4.15500" "3.15500" "3.8500"  "3.7500"  "4.3500"  "4500"    "3.15500"
 [41] "4.55500" "3.95500" "4.1500"  "4.05500" "4.2500"  "3.6500"  "3.95500" "4.35500"
 [49] "3.65500" "4.75500" "3.7500"  "3.65500" "3.6500"  "4.85500" "3.8500"  "3.05500"
 [57] "3.85500" "3.8500"  "3.8500"  "3.35500" "3.7500"  "4.25500" "4.25500" "3.8500" 
 [65] "3.95500" "3.25500" "3.9500"  "3.75500" "3.7500"  "4.45500" "3.65500" "3.4500" 
 [73] "4500"    "4500"    "3.55500" "3.9500"  "3.75500" "4.3500"  "3.3500"  "4.25500"
 [81] "4.1500"  "4.2500"  "4.2500"  "4.2500"  "3.95500" "4.15500" "4.35500" "4.1500" 
 [89] "4.1500"  "4.05500" "3.9500"  "3.65500" "3.4500"  "3.65500" "4.15500" "3.35500"
 [97] "3.55500" "3.75500" "3.8500"  "3.8500"  "4.25500" "4.15500" "4500"    "3.95500"
[105] "3.95500" "3.65500" "4.4500"  "3.6500"  "3.75500" "3.55500" "3.9500"  "4.3500" 
[113] "3.9500"  "3.55500" "3.35500" "4.1500"  "3.65500" "3.75500" "3.6500"  "3.05500"
[121] "4500"    "3.9500"  "4.05500" "3.65500" "4.15500" "4.25500" "4500"    "4.05500"
[129] "4.7500"  "3.1500"  "3.2500"  "3.85500" "3.5500"  "3.5500"  "4.4500"  "3.45500"
[137] "3.7500"  "3.65500" "4.1500"  "4.05500" "3.35500" "4.1500"  "3.9500"  "3.1500" 
[145] "4.2500"  "4.15500" "4.45500" "3.95500" "3.25500" "3.9500"  "3.9500"  "3.75500"
[153] "3.65500" "3.6500"  "3.5500"  "3.65500" "3.9500"  "4.05500" "4.05500" "3.85500"
[161] "4.05500" "3.45500" "3.05500" "3.45500" "4.3500"  "4.6500"  "3.8500"  "4.35500"
[169] "4.05500" "4.1500"  "4.15500" "4.8500"  "3.6500"  "3.05500" "4500"    "3.95500"
[177] "3.6500"  "4.4500"  "4.25500" "4.35500" "3.6500"  "4.2500"  "3.9500"  "4.55500"
[185] "3.9500"  "4.3500"  "4.15500" "3.35500" "3.6500"  "3.9500"  "3.85500" "3.45500"
[193] "3.65500" "3.45500" "3.7500"  "3.45500" "4.3500"  "3.7500"  "3.4500"  "3.55500"
[201] "3.75500" "3.85500" "3.8500"  "4.65500" "3.8500"  "3.6500"  "3.7500"  "3.45500"
[209] "3.65500" "4.2500"  "3.5500"  "3.75500" "4.4500"  "3.8500"  "3.45500" "3.5500" 
[217] "4.5500"  "4.1500"  "3.85500" "3.6500"  "3.15500" "4.55500" "3.5500"  "3.65500"
[225] "3.8500"  "4500"    "4.15500" "3.65500" "3.35500" "3.5500"  "3.45500" "3.65500"
[233] "3.5500"  "4.1500"  "3.5500"  "4.15500" "4.15500" "4.15500" "4.2500"  "4.05500"
[241] "4.15500" "3.8500"  "3.85500" "4.05500" "4.1500"  "3.75500" "3.75500" "3.9500" 
[249] "3.25500" "4.15500" "3.85500" "4.25500" "4500"    "3.9500"  "4.15500" "4500"   
[257] "3.9500"  "3.95500" "4500"    "4.1500"  "3.8500"  "4.5500"  "4.4500"  "3.9500" 
[265] "3.95500" "4.4500"  "3.65500" "3.5500"  "4500"    "3.8500"  "3.4500"  "4.2500" 
[273] "3.05500" "3.6500"  "3.6500"  "4.2500"  "3.4500"  "3.55500" "4.25500" "3.95500"
[281] "3.4500"  "3.9500"  "4500"    "4.05500" "3.75500" "3.85500" "3.95500" "4.3500" 
[289] "3.1500"  "4.35500" "3.75500" "3.25500" "3.95500" "3.65500" "3.75500" "4.45500"
[297] "4.2500"  "4.2500"  "3.8500"  "4500"    "3.3500"  "4.3500"  "3.6500"  "4.25500"
[305] "3.45500" "3.65500" "5.05500" "4.1500"  "3.85500" "4.1500"  "3.8500"  "3.65500"
[313] "3.55500" "4.05500" "3.45500" "3.8500"  "4.15500" "3.3500"  "3.85500" "4.45500"
[321] "4.05500" "3.8500"  "3.6500"  "4.65500" "3.95500" "3.55500" "3.7500"  "3.95500"
[329] "3.8500"  "3.7500"  "3.55500" "3.95500" "3.55500" "3.15500" "4500"    "3.35500"
[337] "4500"    "4.2500"  "3.85500" "3.6500"  "3.8500"  "3500"    "4.2500"  "3.55500"
[345] "3.8500"  "4.25500" "3.5500"  "3.85500" "3.8500"  "3.25500" "3.65500" "4.4500" 
[353] "4500"    "3.6500"  "4.4500"  "3.3500"  "4.45500" "4.25500" "4.05500" "3.45500"
[361] "4.3500"  "3.85500" "4.15500" "3.65500" "3.55500" "4.3500"  "3.9500"  "4.4500" 
[369] "4.15500" "4.15500" "3.85500" "4.25500" "3.7500"  "3.8500"  "4.2500"  "3.35500"
[377] "3.2500"  "3.8500"  "4.2500"  "3.95500" "3.85500" "3.25500" "4.35500" "3.1500" 
[385] "3.7500"  "3.35500" "4.1500"  "4.15500" "3.85500" "4.55500" "3.65500" "3.75500"
[393] "3.8500"  "3.25500" "3.2500"  "3.5500"  "3.75500" "3.45500" "4.65500" "4.15500"
[401] "3.25500" "4.1500"  "4.3500"  "4.2500"  "3.4500"  "3.9500"  "4.6500"  "4.05500"
[409] "4.35500" "3.55500" "3.8500"  "3.8500"  "3.55500" "3.8500"  "3.7500"  "4.05500"
[417] "3.65500" "3.55500" "3.5500"  "4.15500" "3.75500" "3.6500"  "3.85500" "3.3500" 
[425] "3.85500" "2.95500" "3.85500" "4.45500" "3.85500" "3.45500" "3.65500" "4.1500" 
[433] "3.6500"  "3.9500"  "3.8500"  "3.15500" "3.65500" "3.4500"  "3.5500"  "4.25500"
[441] "4.2500"  "4500"    "3.6500"  "3.6500"  "3.25500" "4.25500" "3.95500" "3.1500" 
[449] "4.2500"  "3.15500" "3.65500" "4500"    "3.4500"  "4.55500" "4.1500"  "4.5500" 
[457] "3.75500" "3.85500" "4500"    "3.85500" "4.55500" "3.6500"  "4.1500"  "4.3500" 
[465] "4.05500" "3.7500"  "3.95500" "3.55500" "3.1500"  "3.95500" "3.85500" "3.3500" 
[473] "3.95500" "4.25500" "3.95500" "4.1500"  "4.05500" "4.25500" "3.75500" "4.3500" 
[481] "4.3500"  "4.3500"  "4.7500"  "4.45500" "2.9500"  "3.5500"  "3.6500"  "3.55500"
[489] "4.4500"  "4500"    "3.4500"  "3.5500"  "3.45500" "3.35500" "4.65500" "4.3500" 
[497] "3.8500"  "4.3500"  "3.95500" "3.65500"
mean(resultado[,1])
> mean(resultado[,1])
[1] NA
Warning message:
In mean.default(resultado[, 1]) : argumento não é numérico nem lógico: retornando NA

Muito obrigado 
"Que força é esta, eu não sei; tudo o que sei é que existe, e está disponível apenas quando alguém está num estado em que sabe exatamente o que quer, e está totalmente determinado a não desistir até conseguir."

Alexander Graham Bell

Prof. Clodoaldo José Figueredo Msc - SIAPE 1800348

Métodos Numéricos para Engenharia - Matemática Aplicada

Instituto Federal Catarinense - Campus Araquari
Rodovia BR 280 - km 27 - Cx. Postal 21
CEP 89245-000 - Araquari/SC
Fone: (47) 3803-7240
Em 17 de maio de 2017 17:42, Fernando Antonio de souza <nandodesouza@gmail.com> escreveu:
Clodoaldo

O código abaixo , ler seus dados e para cada coluna, aplica a função "obs" a saída será uma tabela contendo o número de colunas do seu dado fornecido e cada coluna conterá 500 linhas correspondente as quinhentas amostragens realizadas pela função "sample".

Assim você pode indentificar cada vetor contendo os 500 dados amostrados através do index de coluna

por exemplo : resultado[,1]

Espero ter ajudado

A disposição

dados <- matrix(nrow=10,ncol=16,data=rnorm(160))

obs <- function(x){

results <- c()
for(i in 1:500){

results[i] <- mean( sample(x,replace=TRUE))
saida <- paste(results,i,sep="")

}

return(saida)
}

resultado<-apply(dados,2,obs)

=========================================
Fernando Souza
Zootecnista, DSc. Produção e Alimentação Animal
Celular: (31)99796-8781 (Vivo)
E-mail:nandodesouza@gmail.com
Lattes: http://lattes.cnpq.br/6519538815038307
Blog: https://producaoanimalcomr.wordpress.com/
==========================================

Sent from Nylas Mail, the best free email app for work
On Mai 17 2017, at 4:19 pm, Clodoaldo José Figueredo via R-br <r-br@listas.c3sl.ufpr.br> wrote:
Na rotina abaixo eu preciso gravar cada vetor results para os 16 valores de j. Preciso colocar um índice porque esses 16 resultados formarão uma nova matriz com 16 colunas e 500 linhas.
Como indexar os vetores results no comando "for" e montar a nova matriz?

for(j in 1:16){
y <- dados[,j]
qqnorm(y);qqline(y)
mean(y)
results <- c()
for(i in 1:500){ results[i] <- mean( sample(y,replace=T))}
results
qqnorm(results);qqline(results)
mean (results)
}

Obrigado

Clodoaldo

"Que força é esta, eu não sei; tudo o que sei é que existe, e está disponível apenas quando alguém está num estado em que sabe exatamente o que quer, e está totalmente determinado a não desistir até conseguir."

Alexander Graham Bell

Prof. Clodoaldo José Figueredo Msc - SIAPE 1800348

Métodos Numéricos para Engenharia - Matemática Aplicada

Instituto Federal Catarinense - Campus Araquari
Rodovia BR 280 - km 27 - Cx. Postal 21
CEP 89245-000 - Araquari/SC
Fone: (47) 3803-7240
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