Aqui funcionou perfeitamente!<div><div><br></div><div>Att</div><div><div><div><br></div><div>ℝ> dput(dados)</div><div><div><br></div>structure(list(X4 = c(3L, 2L, 4L, 2L, 4L, 5L, 1L, 5L, 5L, 3L, <br>2L, 2L, 7L, 5L, 3L, 1L, 4L, 5L, 4L, 3L), X5 = c(4L, 5L, 5L, 6L, <br>3L, 5L, 7L, 7L, 6L, 6L, 2L, 3L, 7L, 7L, 6L, 2L, 5L, 6L, 3L, 3L<br>), X15 = c(3L, 5L, 4L, 4L, 1L, 6L, 4L, 6L, 4L, 2L, 3L, 2L, 4L, <br>6L, 5L, 1L, 2L, 5L, 5L, 3L), X6 = c(2L, 5L, 3L, 4L, 3L, 5L, 5L, <br>5L, 3L, 5L, 2L, 2L, 3L, 6L, 6L, 4L, 4L, 5L, 3L, 4L), X11 = c(3L, <br>3L, 5L, 2L, 1L, 5L, 7L, 4L, 5L, 5L, 2L, 3L, 3L, 7L, 5L, 1L, 3L, <br>3L, 1L, 4L), X12 = c(4L, 5L, 5L, 6L, 4L, 6L, 6L, 6L, 5L, 6L, <br>4L, 2L, 6L, 6L, 6L, 5L, 4L, 5L, 5L, 3L), X13 = c(4L, 5L, 5L, <br>6L, 5L, 6L, 7L, 6L, 5L, 6L, 4L, 5L, 7L, 6L, 6L, 2L, 3L, 5L, 4L, <br>4L), X16 = c(2L, 4L, 4L, 4L, 3L, 6L, 5L, 3L, 4L, 6L, 4L, 3L, <br>6L, 7L, 5L, 2L, 5L, 6L, 2L, 2L), X17 = c(3L, 5L, 5L, 4L, 3L, <br>5L, 4L, 7L, 4L, 4L, 3L, 2L, 6L, 7L, 6L, 3L, 5L, 7L, 3L, 2L), <br> X18 = c(3L, 4L, 6L, 3L, 1L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 6L, <br> 6L, 6L, 6L, 4L, 6L, 4L, 3L), X19 = c(3L, 5L, 6L, 6L, 3L, <br> 6L, 5L, 4L, 3L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 3L, 6L, 4L, 3L<br> ), X20 = c(5L, 4L, 5L, 6L, 1L, 4L, 3L, 3L, 2L, 5L, 4L, 2L, <br> 6L, 5L, 5L, 4L, 3L, 5L, 5L, 3L), X25 = c(3L, 4L, 4L, 3L, <br> 2L, 5L, 4L, 5L, 2L, 2L, 5L, 4L, 4L, 3L, 4L, 1L, 2L, 4L, 1L, <br> 2L), X26 = c(4L, 3L, 4L, 4L, 1L, 5L, 4L, 7L, 5L, 2L, 3L, <br> 5L, 4L, 4L, 4L, 2L, 4L, 3L, 4L, 3L), X27 = c(3L, 5L, 5L, <br> 4L, 2L, 5L, 7L, 5L, 2L, 6L, 4L, 3L, 6L, 6L, 6L, 1L, 3L, 6L, <br> 2L, 1L), X29 = c(3L, 3L, 4L, 2L, 1L, 4L, 6L, 6L, 5L, 3L, <br> 3L, 2L, 5L, 6L, 6L, 1L, 4L, 6L, 5L, 2L)), .Names = c("X4", <br>"X5", "X15", "X6", "X11", "X12", "X13", "X16", "X17", "X18", <br>"X19", "X20", "X25", "X26", "X27", "X29"), class = "data.frame", row.names = c(NA, <br>-20L))<br></div><div><br></div><div>ℝ> obs <- function(x){<br>+ results <- c()<br>+ for(i in 1:500){<br>+ results[i] <- mean( sample(x,replace=TRUE))<br>+ }<br>+ return(results)<br>+ }<br>ℝ> resultado <- apply(dados,2,obs)<br>ℝ> resultado<br> X4 X5 X15 X6 X11 X12 X13 X16 X17 X18 X19 X20 X25 X26 X27 X29<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br> [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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br>[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<br></div><br><signature><div>=========================================<br>
Fernando Souza<br>
Zootecnista, DSc. Produção e Alimentação Animal<br>
Celular: (31)99796-8781 (Vivo)<br>
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On Mai 19 2017, at 10:45 am, Fernando Antonio de souza <nandodesouza@gmail.com> wrote:
<br>
<blockquote class="gmail_quote"
style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
Faça um dput(dados), copie e cole para que eu possa obter seus dados aqui<div><div><br /></div><div>att</div><br /><div>=========================================<br />
Fernando Souza<br />
Zootecnista, DSc. Produção e Alimentação Animal<br />
Celular: (31)99796-8781 (Vivo)<br />
<a href="mailto:e-mail%3Anandodesouza@gmail.com" target="_blank">E-mail:nandodesouza@gmail.com</a><br />
Lattes: <a href="http://lattes.cnpq.br/6519538815038307&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">http://lattes.cnpq.br/6519538815038307</a><br />
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<br />
On Mai 18 2017, at 11:19 pm, sznelwar@uol.com.br wrote:
<br />
<blockquote style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div><span style="font-size: medium; color: #0000ff;">Tentei rodar, mas o meu resultado dá muito diferente, qual o motivo?</span></div>
<div> </div>
<div>> dados <- read.table("dados_boot.txt", h=T)</div>
<div>> dados</div>
<div> X4 X5 X15 X6 X11 X12 X13 X16 X17 X18 X19 X20 X25 X26 X27 X29</div>
<div>1 3 4 3 2 3 4 4 2 3 3 3 5 3 4 3 3</div>
<div>2 2 5 5 5 3 5 5 4 5 4 5 4 4 3 5 3</div>
<div>3 4 5 4 3 5 5 5 4 5 6 6 5 4 4 5 4</div>
<div>4 2 6 4 4 2 6 6 4 4 3 6 6 3 4 4 2</div>
<div>5 4 3 1 3 1 4 5 3 3 1 3 1 2 1 2 1</div>
<div>6 5 5 6 5 5 6 6 6 5 5 6 4 5 5 5 4</div>
<div>7 1 7 4 5 7 6 7 5 4 5 5 3 4 4 7 6</div>
<div>8 5 7 6 5 4 6 6 3 7 5 4 3 5 7 5 6</div>
<div>9 5 6 4 3 5 5 5 4 4 4 3 2 2 5 2 5</div>
<div>10 3 6 2 5 5 6 6 6 4 4 5 5 2 2 6 3</div>
<div>11 2 2 3 2 2 4 4 4 3 4 5 4 5 3 4 3</div>
<div>12 2 3 2 2 3 2 5 3 2 4 5 2 4 5 3 2</div>
<div>13 7 7 4 3 3 6 7 6 6 6 6 6 4 4 6 5</div>
<div>14 5 7 6 6 7 6 6 7 7 6 5 5 3 4 6 6</div>
<div>15 3 6 5 6 5 6 6 5 6 6 5 5 4 4 6 6</div>
<div>16 1 2 1 4 1 5 2 2 3 6 5 4 1 2 1 1</div>
<div>17 4 5 2 4 3 4 3 5 5 4 3 3 2 4 3 4</div>
<div>18 5 6 5 5 3 5 5 6 7 6 6 5 4 3 6 6</div>
<div>19 4 3 5 3 1 5 4 2 3 4 4 5 1 4 2 5</div>
<div>20 3 3 3 4 4 3 4 2 2 3 3 3 2 3 1 2</div>
<div>> mean(dados[,1])</div>
<div>[1] 3.5</div>
<div>> library(sem)</div>
<div>> library(boot)</div>
<div>> </div>
<div>> #Bootstrap (médias das replicações)</div>
<div>> </div>
<div>> </div>
<div>> obs <- function(x){</div>
<div>+ </div>
<div>+ results <- c()</div>
<div>+ for(i in 1:500){</div>
<div>+ </div>
<div>+ results[i] <- mean(sample(dados,replace=T))</div>
<div>+ </div>
<div>+ saida <- paste(results,i,sep="")</div>
<div>+ </div>
<div>+ }</div>
<div>+ </div>
<div>+ return(saida)</div>
<div>+ }</div>
<div>> </div>
<div>> resultado<-apply(dados,2,obs)</div>
<div>There were 50 or more warnings (use warnings() to see the first 50)</div>
<div>> </div>
<div>> resultado[,1]</div>
<div> [1] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [10] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [19] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [28] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [37] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [46] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [55] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [64] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [73] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [82] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div> [91] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[100] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[109] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[118] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[127] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[136] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[145] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[154] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[163] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[172] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[181] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[190] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[199] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[208] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[217] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[226] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[235] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[244] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[253] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[262] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[271] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[280] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[289] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[298] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[307] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[316] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[325] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[334] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[343] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[352] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[361] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[370] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[379] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[388] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[397] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[406] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[415] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[424] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[433] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[442] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[451] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[460] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[469] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[478] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[487] "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>[496] "NA500" "NA500" "NA500" "NA500" "NA500"</div>
<div>> </div>
<div> </div>
<div><br />Como eu falei,</div>
<div>
<div> </div>
<div>Basta retirar a variável "saída" da função "obs" que a saída será do tipo numeric e aí funcionará seu comando</div>
<div> </div>
<div>segue função obs , corrigida</div>
<div>
<div> </div>
<div>obs <- function(x){<br /><br /> results <- c()<br /> for(i in 1:500){<br /><br /> results[i] <- mean( sample(x,replace=TRUE))<br /> <br /><br /> }<br /><br /> return(results)<br />}<br /><br /><br />resultado <- apply(dados,2,obs)<br /><br />str(resultado)</div>
<br />
<div>=========================================<br /> Fernando Souza<br /> Zootecnista, DSc. Produção e Alimentação Animal<br /> Celular: (31)99796-8781 (Vivo)<br /> <a href="mailto:e-mail%3Anandodesouza@gmail.com&r" target="_blank">E-mail:nandodesouza@gmail.com</a><br /> Lattes: <a href="http://lattes.cnpq.br/6519538815038307&r&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">http://lattes.cnpq.br/6519538815038307</a><br /> Blog: <a href="https://producaoanimalcomr.wordpress.com/&r&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">https://producaoanimalcomr.wordpress.com/</a><br /> ==========================================</div>
<div> </div>
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<div><br /> On Mai 18 2017, at 9:44 am, Fernando Antonio de souza <nandodesouza@gmail.com> wrote: <br />
<blockquote style="margin: 0 0 0 .8ex; border-left: 1px #ccc solid; padding-left: 1ex;">
<div dir="auto">
<div>Clodoaldo
<div dir="auto"> </div>
<div dir="auto">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.</div>
<div dir="auto"> </div>
<div dir="auto">Assim q acessar meu PC testo e lhe confirmo. </div>
<div dir="auto"> </div>
<div style="font-family: sans-serif;" dir="auto">obs <- function(x){<br /> <br /> results <- c()<br /> for(i in 1:500){<br /> </div>
<span style="font-family: sans-serif;"> results[i] <- mean(sample(dados,replace=T))</span>
<div style="font-family: sans-serif;" dir="auto"> <br /> }<br /> <br />return(results)<br />}</div>
<div><br />
<div>Em 18/05/2017 9:24 AM, "Clodoaldo José Figueredo" <<a href="clodoaldo.figueredo@ifc-araquari.edu.br&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">clodoaldo.figueredo@ifc-araquari.edu.br</a>> escreveu:<br />
<blockquote style="margin: 0 0 0 .8ex; border-left: 1px #ccc solid; padding-left: 1ex;">
<div dir="ltr">
<div>
<div>
<div>
<div>
<div>
<div>
<div>Caro Fernando (e demais colegas)<br /><br /></div>
Gostaria de agradecer sua grande ajuda, mas preciso tirar mais outras dúvidas.</div>
Abaixo está o código completo e a matriz dados que é base para os cálculos.</div>
A saída que inseri é a coluna resultado [,1]<br /><br /></div>
Porque não consigo calcular a média dos valores?</div>
As saídas serão usadas como vetores (individualmente) em um outro procedimento para uso do pacote "sem" (Modelagem de equações estruturais).</div>
Como tira as aspas, ou não tem nada a ver?</div>
<div>Porque aparecem valores 4500 e 3500 no meio dos calculados? (Não tem lógica)</div>
<br />setwd("C:/Dados")<br />dados <- read.table("Dados_Boot_txt.txt", h=T)<br /><br />dados<br />
<pre id="m_795982940808604400gmail-rstudio_console_output" style="font-family: 'lucida console'; font-size: 10pt; outline: medium none; border-width: medium; border-style: none; border-color: currentcolor; word-break: break-all; margin: 0px; white-space: pre-wrap; line-height: 15px; color: #000000; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; text-indent: 0px; text-transform: none; word-spacing: 0px; background-color: #ffffff;"><span style="color: blue; white-space: pre-wrap;">> </span><span style="color: blue;">dados
</span> 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</pre>
<br />mean(dados[,1])<br />
<pre id="m_795982940808604400gmail-rstudio_console_output" style="font-family: 'lucida console'; font-size: 10pt; outline: medium none; border-width: medium; border-style: none; border-color: currentcolor; word-break: break-all; margin: 0px; white-space: pre-wrap; line-height: 15px; color: #000000; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; text-indent: 0px; text-transform: none; word-spacing: 0px; background-color: #ffffff;"><span style="color: blue; white-space: pre-wrap;">> </span><span style="color: blue;">mean(dados[,1])
</span>[1] 3.5</pre>
<br />library(sem)<br />library(boot)<br /><br />#Bootstrap (médias das replicações)
<div><br /><br />obs <- function(x){<br /> <br /> results <- c()<br /> for(i in 1:500){<br /> </div>
results[i] <- mean(sample(dados,replace=T))
<div><br /> saida <- paste(results,i,sep="")<br /> <br /> }<br /> <br />return(saida)<br />}<br /><br />resultado<-apply(dados,2,obs)<br /><br /></div>
resultado[,1]<br />
<pre id="m_795982940808604400gmail-rstudio_console_output" style="font-family: 'lucida console'; font-size: 10pt; outline: medium none; border-width: medium; border-style: none; border-color: currentcolor; word-break: break-all; margin: 0px; white-space: pre-wrap; line-height: 15px; color: #000000; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; text-indent: 0px; text-transform: none; word-spacing: 0px; background-color: #ffffff;"><span style="color: blue; white-space: pre-wrap;">> </span><span style="color: blue;">resultado[,1]
</span> [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"</pre>
<br />mean(resultado[,1])<br />
<pre id="m_795982940808604400gmail-rstudio_console_output" style="font-family: 'lucida console'; font-size: 10pt; outline: medium none; border-width: medium; border-style: none; border-color: currentcolor; word-break: break-all; margin: 0px; white-space: pre-wrap; line-height: 15px; color: #000000; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; text-indent: 0px; text-transform: none; word-spacing: 0px; background-color: #ffffff;"><span style="color: blue; white-space: pre-wrap;">> </span><span style="color: blue;">mean(resultado[,1])
</span>[1] NA
<span style="color: #c5060b;">Warning message:
</span><span style="color: #c5060b;">In mean.default(resultado[, 1]) :</span><span style="color: #c5060b;"> argumento não é numérico nem lógico: retornando NA<br /></span><br />Muito obrigado </pre>
<div>
<div><br />
<div>
<div>
<div dir="ltr">
<div><strong>"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." </strong></div>
<div><a href="http://www.pensador.info/frase/NTQwOTE1/&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">Alexander Graham Bell</a></div>
<div> </div>
<div>Prof. Clodoaldo José Figueredo Msc - SIAPE 1800348</div>
<div>Métodos Numéricos para Engenharia - Matemática Aplicada</div>
<div>Instituto Federal Catarinense - Campus Araquari<br />Rodovia BR 280 - km 27 - Cx. Postal 21<br />CEP 89245-000 - Araquari/SC<br />Fone: (47) 3803-7240</div>
<div> </div>
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<div>Em 17 de maio de 2017 17:42, Fernando Antonio de souza <span dir="ltr"><<a href="nandodesouza@gmail.com&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">nandodesouza@gmail.com</a>></span> escreveu:</div>
<blockquote style="margin: 0px 0px 0px 0.8ex; border-left: 1px solid #cccccc; padding-left: 1ex;">
<div>
<div>Clodoaldo</div>
<div> </div>
<div>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".</div>
<div> </div>
<div>Assim você pode indentificar cada vetor contendo os 500 dados amostrados através do index de coluna</div>
<div> por exemplo : resultado[,1]</div>
<div> </div>
<div>Espero ter ajudado</div>
<div> </div>
<div>A disposição</div>
<div> </div>
dados <- matrix(nrow=10,ncol=16,data=rnorm(160))<br /><br />obs <- function(x){<span><br /> <br /> results <- c()<br /> for(i in 1:500){<br /> <br /></span> results[i] <- mean( sample(x,replace=TRUE))<br /> saida <- paste(results,i,sep="")<br /> <br /> }<br /> <br /> return(saida)<br />}<br /><br />resultado<-apply(dados,2,obs)<br /><br />
<div>=========================================<br /> Fernando Souza<br /> Zootecnista, DSc. Produção e Alimentação Animal<br /> Celular: (31)99796-8781 (Vivo)<br /> <a href="mailto:e-mail%3Anandodesouza@gmail.com&r" target="_blank">E-mail:nandodesouza@gmail.com</a><br /> Lattes: <a href="http://lattes.cnpq.br/6519538815038307&r&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">http://lattes.cnpq.br/6519538815038307</a><br /> Blog: <a href="https://producaoanimalcomr.wordpress.com/&r&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">https://producaoanimalcomr.wordpress.com/</a><br /> ==========================================</div>
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<div><br /> On Mai 17 2017, at 4:19 pm, Clodoaldo José Figueredo via R-br <<a href="r-br@listas.c3sl.ufpr.br&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">r-br@listas.c3sl.ufpr.br</a>> wrote: </div>
</div>
<blockquote style="margin: 0px 0px 0px 0.8ex; border-left: 1px solid #cccccc; padding-left: 1ex;">
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<div>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.</div>
Como indexar os vetores results no comando "for" e montar a nova matriz?<br />
<div>
<div><br />for(j in 1:16){<br />y <- dados[,j]<br />qqnorm(y);qqline(y)<br />mean(y)<br />results <- c()<br />for(i in 1:500){ results[i] <- mean( sample(y,replace=T))}<br />results<br />qqnorm(results);qqline(results)<br />mean (results) <br />}<br /><br /></div>
<div>Obrigado<br /><br /></div>
<div>Clodoaldo<br /><br /></div>
<div>
<div>
<div>
<div dir="ltr">
<div><strong>"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." </strong></div>
<div><a href="http://www.pensador.info/frase/NTQwOTE1/&r=Y2xvZG9hbGRvLmZpZ3VlcmVkb0BpZmMtYXJhcXVhcmkuZWR1LmJy&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy&r=ci1ickBsaXN0YXMuYzNzbC51ZnByLmJy" target="_blank">Alexander Graham Bell</a></div>
<div> </div>
<div>Prof. Clodoaldo José Figueredo Msc - SIAPE 1800348</div>
<div>Métodos Numéricos para Engenharia - Matemática Aplicada</div>
<div>Instituto Federal Catarinense - Campus Araquari<br />Rodovia BR 280 - km 27 - Cx. Postal 21<br />CEP 89245-000 - Araquari/SC<br />Fone: (47) 3803-7240</div>
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