Taller_VAR_aleatorio2
Andres Gomez
2025-09-24
prueba de validacion
introduccion
Las pruebas de validación de números pseudoaleatorios son procedimientos
estadísticos que permiten evaluar si una secuencia generada por un
algoritmo realmente se comporta como aleatoria. Dado que los números
producidos por un generador no son verdaderamente aleatorios sino
pseudoaleatorios, es necesario comprobar que cumplen propiedades como
uniformidad, independencia y ausencia de patrones detectables. Para esto
se aplican distintas pruebas, entre ellas la prueba de
Kolmogorov-Smirnov, que compara la distribución acumulada de los números
generados con la distribución teórica que deberían seguir (generalmente
la uniforme). Esta prueba permite identificar si existen desviaciones
significativas entre lo esperado y lo obtenido; si el valor p es mayor a
0.05, se acepta que los números se ajustan a la distribución, mientras
que si es menor o igual a 0.05 se concluye que los números no presentan
el comportamiento aleatorio esperado.
- prueba kolomogorov Smirnoff La prueba de
Kolmogorov-Smirnov (K-S) es una técnica estadística no paramétrica que
se utiliza para determinar si una muestra sigue una distribución teórica
específica, como la normal, o para comparar dos muestras y evaluar si
provienen de la misma distribución. Su fundamento consiste en medir la
diferencia máxima entre la función de distribución acumulada de los
datos y la distribución con la que se comparan. El resultado de la
prueba se expresa mediante un valor p: si es mayor que 0.05 no se
rechaza la hipótesis nula, lo que indica que la muestra sí sigue la
distribución propuesta, mientras que si es menor o igual a 0.05 se
rechaza la hipótesis nula y se concluye que los datos no siguen esa
distribución. En RStudio se aplica con la función
ks.test(), donde se puede usar un vector de datos contra una distribución teórica (comopnormen el caso de la normalidad) o comparar directamente dos muestras. Es importante tener en cuenta que esta prueba es sensible al tamaño de la muestra y que su validez es mayor cuando los parámetros de la distribución se conocen previamente, por lo cual, para evaluar normalidad en la práctica, también se recomienda la prueba de Shapiro-Wilk.
generando 50 numeros aleatorios pseudoaleatorios por el metodo secuencial mixto.
a <- 3141658
c <- 10000
m <- 300
X_n <- 42 # semilla
random.number<-numeric(50) # vector numérico de longitud 50
for (i in 1:50)
{X_n<-(a*X_n+c)%%m
random.number[i]<-X_n/m # números en el intervalo [0,1]
}
random.number## [1] 0.45333333 0.62666667 0.68000000 0.77333333 0.18666667 0.16000000
## [7] 0.61333333 0.90666667 0.92000000 0.69333333 0.54666667 0.04000000
## [13] 0.65333333 0.22666667 0.48000000 0.17333333 0.38666667 0.76000000
## [19] 0.41333333 0.30666667 0.12000000 0.29333333 0.34666667 0.44000000
## [25] 0.85333333 0.82666667 0.28000000 0.57333333 0.58666667 0.36000000
## [31] 0.21333333 0.70666667 0.32000000 0.89333333 0.14666667 0.84000000
## [37] 0.05333333 0.42666667 0.08000000 0.97333333 0.78666667 0.96000000
## [43] 0.01333333 0.10666667 0.52000000 0.49333333 0.94666667 0.24000000
## [49] 0.25333333 0.02666667test_ks <- ks.test(random.number,"punif",0,1)
test_ks##
## Exact one-sample Kolmogorov-Smirnov test
##
## data: random.number
## D = 0.066667, p-value = 0.9686
## alternative hypothesis: two-sidedifelse(test_ks$p.value < 0.05, " Los u_i no siguen una distribución uniforme","Los u_i siguen la distribución uniforme [0,1]") ### [1] "Los u_i siguen la distribución uniforme [0,1]"#analisis conclucion
- prueba Runtest La prueba de rachas o Run Test es una prueba estadística no paramétrica utilizada para evaluar la independencia de una secuencia de números pseudoaleatorios. Una racha se define como una sucesión de valores consecutivos que cumplen una misma condición, por ejemplo, números mayores o menores a la media. La prueba consiste en contar el número de rachas observadas en la secuencia y compararlo con el número esperado de rachas bajo la hipótesis de independencia. Si la cantidad de rachas difiere significativamente de lo esperado, se concluye que la secuencia presenta algún patrón y, por lo tanto, no es aleatoria; si no hay diferencia significativa, se acepta que los datos son independientes y, en consecuencia, aleatorios.
library(tseries)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoodiff(random.number)## [1] 0.17333333 0.05333333 0.09333333 -0.58666667 -0.02666667 0.45333333
## [7] 0.29333333 0.01333333 -0.22666667 -0.14666667 -0.50666667 0.61333333
## [13] -0.42666667 0.25333333 -0.30666667 0.21333333 0.37333333 -0.34666667
## [19] -0.10666667 -0.18666667 0.17333333 0.05333333 0.09333333 0.41333333
## [25] -0.02666667 -0.54666667 0.29333333 0.01333333 -0.22666667 -0.14666667
## [31] 0.49333333 -0.38666667 0.57333333 -0.74666667 0.69333333 -0.78666667
## [37] 0.37333333 -0.34666667 0.89333333 -0.18666667 0.17333333 -0.94666667
## [43] 0.09333333 0.41333333 -0.02666667 0.45333333 -0.70666667 0.01333333
## [49] -0.22666667S<-ifelse(diff(random.number) > 0, 1, 0) # seuencia de ceros y unos
S## [1] 1 1 1 0 0 1 1 1 0 0 0 1 0 1 0 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 0 1 0 1 0 1 0
## [39] 1 0 1 0 1 1 0 1 0 1 0runs.test(as.factor(S))##
## Runs Test
##
## data: as.factor(S)
## Standard Normal = 1.9106, p-value = 0.05605
## alternative hypothesis: two.sided# Detectar cambios
cambios <- abs(diff(S))
# Contar las corridas
corridas <- sum(cambios) + 1
corridas## [1] 323.prueba porker.test
El Poker test es una prueba estadística de aleatoriedad usada para validar números pseudoaleatorios. Se basa en la analogía con el juego de póker: analiza las combinaciones de dígitos en los números generados (por ejemplo, pares, tríos, full house, etc.) para verificar si aparecen con la frecuencia esperada en una secuencia realmente aleatoria.
library(randtoolbox)## Cargando paquete requerido: rngWELL## This is randtoolbox. For an overview, type 'help("randtoolbox")'.poker.test(random.number,nbcard=5)##
## Poker test
##
## chisq stat = 3.9, df = 4, p-value = 0.43
##
## (sample size : 50)
##
## observed number 0 0 3 6 1
## expected number 0.016 0.96 4.8 3.8 0.38## A)
set.seed(0525)
solicitudes<-rpois(5000,5)
solicitudes## [1] 6 7 3 5 2 7 6 5 4 4 4 6 8 2 5 4 5 3 2 2 4 5 6 3
## [25] 2 3 4 5 8 2 4 1 5 4 12 6 9 9 3 5 3 4 6 8 3 4 4 7
## [49] 7 2 5 5 2 7 4 8 6 6 6 5 3 5 9 3 2 9 2 1 7 5 5 6
## [73] 2 8 4 4 3 6 4 8 2 5 4 6 3 5 5 2 11 10 3 6 9 4 6 6
## [97] 13 9 4 5 4 5 6 6 0 7 2 4 5 5 7 8 6 5 8 5 7 5 7 13
## [121] 9 6 5 6 4 6 2 8 4 6 1 2 3 8 6 5 2 4 6 5 3 5 3 4
## [145] 2 7 7 8 14 4 8 7 4 6 5 7 4 1 7 4 5 3 11 3 3 5 6 6
## [169] 3 10 7 5 2 5 3 6 8 8 6 3 4 5 2 5 4 6 3 4 8 3 2 8
## [193] 5 1 10 4 10 6 1 2 4 0 6 5 2 5 6 2 2 10 8 7 3 5 4 3
## [217] 3 5 8 7 5 7 2 7 8 5 8 7 8 8 5 7 5 4 3 6 9 1 8 4
## [241] 8 5 5 7 5 5 4 7 9 4 1 7 6 5 8 3 7 4 7 4 2 5 10 6
## [265] 4 7 6 5 3 5 10 6 3 6 4 1 9 6 9 6 7 4 8 4 2 9 6 4
## [289] 5 4 3 2 3 2 8 9 12 5 7 2 2 2 5 8 8 7 4 2 10 3 6 4
## [313] 10 4 6 3 3 8 4 3 2 7 4 7 4 7 5 6 3 3 6 8 4 5 6 4
## [337] 7 3 8 6 1 3 4 5 5 4 9 8 3 4 5 5 8 6 3 2 5 4 4 4
## [361] 2 5 2 3 8 4 6 4 8 3 3 3 5 5 3 5 4 1 3 5 4 0 5 5
## [385] 4 4 3 7 9 4 3 2 6 4 9 4 7 7 2 5 3 4 2 5 6 3 3 6
## [409] 6 1 3 2 2 5 8 9 5 4 2 4 6 5 5 5 7 6 4 5 10 2 1 7
## [433] 11 1 2 6 2 5 6 6 4 6 12 3 4 2 6 3 6 9 6 8 4 4 1 5
## [457] 3 5 2 7 6 4 3 3 5 11 6 3 6 3 7 6 11 5 4 9 3 5 1 3
## [481] 2 4 3 4 4 5 7 8 5 4 4 4 1 5 5 6 5 6 2 9 1 7 7 4
## [505] 6 7 5 7 7 5 2 6 6 6 9 1 3 6 1 3 4 8 5 5 10 5 2 4
## [529] 3 9 8 11 4 5 2 5 4 4 7 4 4 4 3 3 3 6 3 10 3 6 3 3
## [553] 5 11 4 5 6 5 2 3 3 7 2 5 7 5 6 6 7 4 5 4 3 4 6 5
## [577] 2 8 3 1 3 2 4 8 3 5 5 3 5 9 5 7 5 7 9 5 4 3 8 5
## [601] 4 7 6 1 4 8 7 4 5 7 5 4 2 6 7 5 1 4 6 6 9 8 3 4
## [625] 7 7 6 9 4 6 6 4 7 3 5 2 5 4 2 6 5 2 5 5 5 5 3 3
## [649] 7 3 9 4 6 6 3 3 4 4 9 3 4 4 5 4 6 8 6 7 6 7 5 9
## [673] 1 4 1 3 1 5 7 6 2 6 3 2 6 4 6 11 4 7 0 5 3 1 7 5
## [697] 8 3 7 0 4 3 5 2 5 4 8 5 4 7 5 7 6 9 3 6 3 6 6 5
## [721] 10 7 5 9 3 1 4 5 4 6 4 2 7 7 6 4 2 5 3 3 8 5 3 3
## [745] 5 5 4 6 7 8 5 7 8 0 3 3 7 5 4 4 7 2 6 6 9 4 4 5
## [769] 10 5 5 3 6 4 2 4 4 4 7 4 4 7 6 6 3 6 6 5 5 8 6 3
## [793] 4 4 7 5 6 4 6 4 6 10 7 5 5 4 5 5 2 7 3 4 3 7 5 2
## [817] 4 3 4 4 2 8 9 6 6 3 7 7 11 3 4 4 6 6 7 2 6 4 4 3
## [841] 7 9 4 7 7 1 2 2 2 6 5 3 4 4 3 5 8 7 2 2 7 4 4 6
## [865] 2 8 2 4 4 5 6 5 5 5 5 3 2 10 5 3 3 4 4 0 4 2 11 4
## [889] 2 5 0 7 7 7 6 2 6 5 2 4 8 5 9 5 3 7 2 5 1 9 4 4
## [913] 3 2 5 3 11 3 3 2 7 5 2 5 4 7 6 7 3 7 2 6 5 9 3 6
## [937] 4 3 7 5 3 4 7 6 4 5 8 5 2 5 6 3 7 6 7 5 6 3 2 4
## [961] 5 5 3 6 9 3 6 6 7 4 4 3 5 2 7 1 6 6 1 4 5 10 4 3
## [985] 5 3 7 2 5 2 10 1 5 1 10 4 6 4 2 4 2 5 4 6 3 5 4 5
## [1009] 3 6 8 4 5 9 4 3 3 6 3 6 6 1 2 5 5 3 7 10 5 6 9 9
## [1033] 3 4 9 4 5 5 7 3 8 1 2 4 4 5 4 5 6 5 3 4 5 8 5 5
## [1057] 2 4 2 6 4 6 0 10 5 4 5 7 7 5 6 6 4 6 3 1 2 6 9 3
## [1081] 3 4 4 4 1 4 4 3 2 5 4 4 4 7 9 1 11 5 6 3 6 2 4 2
## [1105] 8 7 6 7 10 2 8 6 4 8 5 5 7 6 7 4 10 5 8 1 3 6 2 6
## [1129] 6 6 6 5 5 6 3 3 7 8 8 3 6 5 3 6 5 4 4 3 5 4 7 2
## [1153] 4 7 7 3 7 5 3 5 6 3 3 3 9 6 3 3 4 7 8 4 4 2 2 4
## [1177] 8 3 3 9 6 3 1 3 6 1 3 5 8 6 5 7 4 8 6 5 7 3 4 8
## [1201] 3 1 7 6 6 3 11 4 10 5 4 4 3 5 5 4 2 5 6 6 4 4 6 4
## [1225] 7 11 8 6 9 3 4 9 5 2 11 5 3 5 4 3 7 3 7 4 7 3 8 8
## [1249] 10 6 4 4 3 6 7 7 5 6 7 6 3 8 4 10 9 2 1 8 4 5 5 3
## [1273] 6 4 7 5 1 11 9 4 5 7 8 3 5 6 3 4 8 7 8 4 4 6 10 7
## [1297] 6 6 10 9 5 6 6 5 6 8 6 10 6 8 3 2 4 6 1 9 5 6 3 3
## [1321] 3 2 1 6 3 5 1 4 4 6 2 4 4 9 6 3 6 4 1 7 6 3 2 2
## [1345] 1 6 3 4 4 8 4 6 5 4 2 2 5 1 6 6 2 5 4 4 6 9 5 9
## [1369] 4 6 7 6 4 4 6 6 2 5 6 9 9 7 4 7 7 3 6 7 5 2 8 5
## [1393] 3 9 5 5 6 6 4 9 2 4 5 2 3 4 7 8 4 5 5 8 6 2 7 3
## [1417] 4 3 6 4 7 3 3 6 9 12 7 5 4 4 2 7 5 6 6 9 6 8 2 4
## [1441] 6 5 4 7 7 2 5 4 4 7 6 5 5 6 5 3 4 3 3 6 2 9 8 6
## [1465] 4 9 5 6 4 4 3 4 7 3 2 8 4 4 8 3 5 4 7 3 3 9 4 7
## [1489] 3 7 3 8 6 2 4 3 6 5 5 4 6 6 3 5 6 8 3 7 5 6 3 8
## [1513] 3 3 4 7 6 8 3 1 7 2 3 5 6 6 9 7 1 4 8 8 5 4 7 9
## [1537] 3 3 5 3 3 8 3 5 3 2 4 3 7 3 7 1 8 1 3 2 7 10 4 8
## [1561] 4 6 4 6 6 4 2 4 5 3 5 3 3 8 5 4 4 5 1 6 6 10 4 3
## [1585] 2 4 5 5 3 3 5 5 7 2 8 6 4 3 7 2 9 7 7 6 4 5 6 7
## [1609] 8 8 6 7 8 3 3 2 2 3 10 6 3 7 4 8 7 4 3 2 6 2 4 9
## [1633] 4 4 6 6 3 5 7 3 7 7 4 5 3 8 7 7 5 5 3 6 8 0 7 2
## [1657] 5 4 3 5 3 4 4 4 6 5 2 3 3 0 3 9 5 9 2 2 8 8 6 9
## [1681] 6 8 9 7 5 7 8 4 10 8 6 2 9 3 3 8 4 4 2 4 3 5 6 4
## [1705] 5 3 2 7 5 6 6 6 2 4 2 5 6 4 2 9 4 6 2 5 7 3 7 4
## [1729] 6 2 5 2 3 1 4 3 4 4 2 6 4 3 5 4 3 9 5 5 5 7 5 3
## [1753] 8 8 8 5 4 5 4 4 3 5 7 2 5 3 3 7 4 7 7 9 4 2 7 10
## [1777] 5 11 5 8 6 8 5 5 2 9 3 6 4 1 4 4 2 4 5 6 7 6 5 7
## [1801] 7 2 11 8 2 3 4 4 4 4 10 1 2 3 6 5 3 3 8 6 5 6 2 6
## [1825] 7 2 4 7 3 6 3 3 6 9 5 3 10 6 2 4 2 2 4 3 9 3 7 8
## [1849] 4 5 4 6 4 5 5 3 6 10 2 6 4 3 6 6 7 7 4 5 1 4 7 2
## [1873] 4 4 4 3 2 6 7 5 3 5 4 4 10 4 2 5 5 2 2 9 5 3 4 6
## [1897] 9 5 8 5 7 4 5 4 7 6 3 1 3 10 6 13 4 8 6 4 7 6 8 6
## [1921] 9 6 6 8 8 6 5 5 4 8 5 1 3 3 3 7 9 7 4 2 5 6 5 8
## [1945] 1 4 5 4 8 5 7 4 3 11 3 5 7 6 5 7 6 5 5 4 1 7 2 5
## [1969] 5 6 12 11 2 3 7 7 3 6 6 4 5 6 3 6 6 4 7 6 2 4 2 4
## [1993] 4 5 3 3 8 5 8 5 1 2 4 2 6 2 5 6 4 2 4 4 10 3 5 0
## [2017] 3 5 5 5 4 5 8 6 3 4 7 4 3 6 4 4 6 1 5 5 5 3 3 4
## [2041] 3 2 4 3 11 6 8 3 3 3 4 5 5 6 6 3 9 9 8 7 7 8 5 7
## [2065] 8 9 4 6 2 4 1 5 3 4 4 8 4 2 7 8 2 4 4 4 7 6 7 2
## [2089] 3 8 3 3 4 4 5 4 3 9 8 5 8 4 9 4 3 1 4 7 8 2 6 4
## [2113] 7 8 5 6 3 0 5 3 5 6 6 9 8 6 3 4 6 5 5 5 4 5 8 4
## [2137] 6 3 6 7 5 5 6 5 4 8 6 1 2 4 5 4 7 6 4 5 5 4 9 5
## [2161] 1 6 4 7 5 5 5 2 6 5 5 5 2 4 6 6 6 4 6 3 4 3 6 1
## [2185] 4 3 4 8 3 4 3 3 3 10 9 4 10 3 3 6 7 7 2 8 3 4 8 3
## [2209] 4 7 5 2 4 2 7 3 6 5 2 3 5 10 4 5 9 2 7 6 5 9 4 7
## [2233] 9 3 4 4 7 4 2 6 4 3 3 3 4 9 10 7 4 6 2 4 6 7 6 4
## [2257] 1 7 4 2 6 4 5 5 6 5 2 6 6 6 3 3 6 2 3 4 4 2 6 8
## [2281] 4 8 8 6 7 1 5 12 5 2 7 7 7 6 2 6 5 10 10 3 4 6 8 9
## [2305] 3 5 6 5 3 5 6 5 7 2 8 4 6 3 5 6 3 5 8 2 5 12 1 9
## [2329] 4 3 5 8 4 6 3 8 5 5 4 4 4 3 5 6 6 6 3 4 5 5 3 0
## [2353] 3 5 5 4 4 4 6 10 7 5 4 10 4 7 4 0 4 8 8 4 5 6 5 2
## [2377] 6 8 0 5 7 3 9 7 2 6 5 2 4 3 8 6 4 4 5 4 7 4 3 7
## [2401] 3 5 5 5 4 5 5 3 9 6 4 7 2 3 6 5 3 5 3 7 2 4 7 7
## [2425] 5 2 1 6 5 2 4 1 2 4 9 4 10 8 6 5 4 7 7 5 3 5 7 4
## [2449] 5 4 6 5 4 7 5 4 3 6 6 3 7 4 6 7 2 10 2 8 5 3 3 5
## [2473] 3 7 1 8 1 5 5 4 6 5 8 7 5 4 5 2 4 1 8 4 2 7 1 5
## [2497] 2 2 4 5 5 4 0 7 3 9 3 1 4 5 5 3 2 10 5 2 6 4 8 0
## [2521] 2 5 5 5 2 5 2 2 4 4 5 5 5 10 8 3 10 3 7 9 8 3 6 7
## [2545] 10 9 4 9 3 7 5 9 4 3 6 4 5 5 6 3 6 3 5 5 4 4 4 5
## [2569] 6 2 7 0 4 4 3 6 8 3 5 3 1 7 6 5 7 3 7 7 5 6 3 7
## [2593] 2 9 6 7 6 10 4 6 3 3 3 9 4 6 6 4 8 2 9 5 4 8 6 4
## [2617] 4 4 8 9 2 6 6 5 7 8 5 8 1 6 6 5 4 7 10 9 5 3 6 8
## [2641] 9 7 4 9 4 6 5 3 4 8 6 0 7 7 7 4 5 9 4 3 3 6 6 9
## [2665] 4 5 5 2 4 7 5 11 6 9 4 2 5 2 8 9 5 3 2 3 4 3 3 4
## [2689] 1 10 6 6 5 2 3 4 5 4 6 3 6 4 5 5 4 5 7 4 3 6 2 4
## [2713] 3 8 8 3 7 6 5 5 5 4 6 6 7 3 5 6 5 2 1 7 2 8 3 5
## [2737] 7 8 11 3 9 4 3 5 15 1 2 3 5 5 1 7 3 2 4 7 9 8 7 5
## [2761] 6 8 8 6 6 4 7 2 6 6 4 3 6 4 3 4 7 7 4 5 4 7 2 6
## [2785] 4 6 7 7 4 6 8 3 4 4 6 3 7 2 4 1 1 6 3 4 6 4 6 1
## [2809] 10 7 5 3 4 1 4 9 8 5 4 7 5 3 7 4 8 6 3 7 3 3 4 6
## [2833] 6 4 8 4 8 4 5 8 6 8 5 7 5 5 3 7 7 1 5 5 4 8 6 4
## [2857] 9 3 6 4 7 5 6 7 3 3 7 4 3 2 1 5 9 6 3 3 4 3 5 2
## [2881] 3 5 2 8 5 5 5 4 5 5 7 3 8 6 3 6 7 3 1 2 5 6 4 3
## [2905] 4 2 1 5 4 10 3 5 3 6 2 7 6 4 0 2 6 8 6 5 5 5 5 8
## [2929] 2 2 8 6 0 7 4 1 5 6 5 4 4 5 7 7 8 5 6 9 3 5 4 5
## [2953] 5 4 5 3 5 5 7 4 4 1 2 3 8 4 2 5 9 3 5 1 8 8 1 4
## [2977] 4 2 5 8 7 1 4 11 8 6 3 3 5 3 9 2 6 8 5 7 4 4 7 6
## [3001] 9 4 4 2 7 4 8 5 5 3 2 2 8 6 6 2 9 4 0 8 6 5 5 7
## [3025] 8 3 2 3 5 2 6 4 2 10 6 4 3 8 1 5 9 5 5 6 6 5 1 5
## [3049] 3 10 5 2 1 5 6 7 1 2 6 5 6 3 7 3 6 5 7 2 4 1 10 8
## [3073] 7 6 3 2 6 2 5 6 4 4 5 6 4 9 5 6 5 5 4 11 4 5 5 6
## [3097] 6 5 8 6 10 6 4 2 4 5 3 5 5 7 3 8 6 4 6 6 4 6 3 2
## [3121] 5 3 5 3 4 4 3 3 7 4 6 2 3 7 8 6 6 9 3 7 6 6 4 5
## [3145] 6 1 8 5 4 4 2 5 1 5 2 9 6 1 6 6 9 10 5 5 7 9 6 4
## [3169] 3 5 1 10 8 6 6 5 4 8 4 2 7 5 7 7 9 3 6 2 5 4 5 6
## [3193] 5 5 2 6 4 1 5 10 8 3 6 4 4 4 3 2 2 7 3 5 8 2 9 5
## [3217] 7 5 3 5 4 3 5 4 2 6 2 1 5 2 10 5 3 3 8 2 3 7 5 6
## [3241] 3 3 6 8 2 4 5 7 3 6 5 3 2 7 2 6 2 4 9 5 5 4 8 7
## [3265] 8 4 1 6 1 6 4 3 2 6 4 4 4 5 11 5 4 8 7 6 4 6 5 7
## [3289] 2 5 3 9 8 6 4 2 5 6 3 8 8 5 3 6 5 4 8 4 5 1 6 8
## [3313] 7 7 4 3 8 4 3 2 5 5 3 4 2 6 3 3 1 5 6 4 3 10 6 7
## [3337] 9 5 5 4 5 0 8 7 6 14 6 1 7 5 4 5 2 1 7 5 9 4 3 4
## [3361] 7 3 7 3 7 4 8 5 3 5 7 5 3 6 3 9 5 5 6 6 8 11 5 7
## [3385] 8 6 6 4 6 1 9 8 5 5 7 5 6 5 5 5 3 4 7 6 7 4 3 3
## [3409] 3 1 5 4 4 4 6 6 5 6 3 2 1 6 8 3 4 5 6 6 5 3 10 9
## [3433] 5 4 7 7 5 7 5 2 6 4 6 8 4 6 2 3 3 5 3 5 4 7 7 5
## [3457] 8 9 6 5 6 4 5 3 10 8 4 7 1 5 7 1 7 5 1 4 5 2 8 5
## [3481] 2 4 3 1 4 6 4 4 5 4 6 6 8 4 6 10 6 6 9 7 4 7 9 7
## [3505] 8 5 4 6 8 7 9 5 6 2 4 6 1 5 3 8 3 7 9 8 11 3 6 4
## [3529] 7 8 5 5 6 2 7 3 4 6 4 7 5 5 3 3 5 4 5 7 7 7 4 4
## [3553] 4 6 3 4 3 4 5 5 4 8 5 5 6 10 3 2 5 5 5 4 7 6 6 2
## [3577] 3 5 3 6 1 4 5 3 3 2 4 4 5 3 7 6 4 5 5 5 6 7 4 6
## [3601] 7 5 4 9 4 5 7 8 10 4 3 3 5 2 9 6 8 4 5 5 6 9 3 0
## [3625] 3 6 4 6 6 5 4 4 5 4 5 2 3 6 12 8 1 4 5 6 11 2 7 3
## [3649] 4 6 3 3 5 5 5 6 6 8 7 3 7 3 3 9 3 9 5 1 6 2 6 4
## [3673] 4 2 3 1 0 4 2 4 4 6 6 3 4 4 2 10 5 5 9 3 2 3 4 0
## [3697] 3 5 6 4 6 4 6 3 5 5 5 5 5 7 11 5 8 12 6 4 5 3 4 6
## [3721] 10 0 2 5 4 4 7 3 5 8 6 5 4 6 7 2 6 5 6 5 8 6 5 2
## [3745] 4 4 4 6 9 9 4 2 6 2 3 8 5 9 1 5 11 6 4 4 6 3 7 4
## [3769] 5 5 8 6 6 7 2 4 4 4 7 8 5 9 5 2 3 8 4 1 4 5 5 7
## [3793] 3 3 8 8 4 6 3 1 6 5 5 7 5 5 6 5 6 7 2 2 9 2 9 3
## [3817] 4 6 6 3 7 9 4 4 10 4 7 2 6 9 8 5 7 5 3 4 7 7 5 7
## [3841] 5 6 7 5 5 3 2 7 1 6 3 5 2 6 6 2 5 7 12 9 5 4 8 6
## [3865] 4 3 5 5 4 6 6 7 4 5 5 7 5 8 12 8 6 2 5 5 8 5 6 9
## [3889] 4 5 7 4 2 3 6 3 3 1 6 8 4 4 3 7 3 4 5 4 4 3 8 3
## [3913] 9 4 6 2 8 3 4 7 6 6 4 6 3 5 4 2 2 6 4 4 4 3 5 4
## [3937] 4 4 5 5 5 5 3 9 5 3 5 1 7 2 2 1 2 5 4 3 4 4 3 10
## [3961] 5 6 6 2 7 2 5 5 4 4 5 3 4 9 4 2 7 1 4 10 7 4 8 5
## [3985] 5 0 6 5 6 6 5 8 3 6 5 4 4 6 5 3 5 8 7 1 5 4 2 8
## [4009] 7 4 6 5 6 2 8 5 10 4 4 8 6 4 6 6 3 7 6 3 2 2 6 2
## [4033] 4 4 3 4 6 5 6 6 5 6 5 8 1 5 4 3 3 5 5 6 6 5 9 9
## [4057] 6 4 8 7 4 2 4 6 7 8 6 6 4 7 6 2 4 3 3 9 7 7 5 5
## [4081] 5 2 3 8 6 3 6 6 4 4 11 3 4 7 10 10 8 6 8 2 8 3 6 5
## [4105] 13 1 8 3 2 4 5 8 4 4 5 4 2 5 5 7 9 3 2 10 5 4 5 4
## [4129] 7 2 4 2 11 3 5 8 5 6 6 6 3 4 3 6 3 9 7 3 6 5 6 3
## [4153] 5 6 4 7 7 3 1 4 8 3 4 1 5 8 5 3 8 5 5 2 2 3 4 6
## [4177] 8 2 5 7 2 7 6 6 3 8 4 6 1 3 6 1 5 8 3 3 2 2 4 5
## [4201] 4 2 3 6 6 5 6 6 7 7 10 6 2 10 4 9 3 5 4 7 6 5 5 4
## [4225] 7 6 7 3 5 6 5 4 7 3 4 4 7 5 3 5 3 8 5 10 2 5 6 7
## [4249] 7 7 4 5 3 4 8 2 4 3 2 4 4 3 5 5 8 12 3 2 2 6 1 3
## [4273] 8 4 4 3 4 4 4 3 8 4 3 4 2 10 5 6 8 6 2 6 8 8 6 5
## [4297] 8 7 5 5 9 6 3 4 5 2 5 7 5 7 4 5 2 6 4 3 4 9 4 6
## [4321] 2 2 4 7 7 4 6 7 5 9 7 2 6 1 3 3 3 9 5 3 1 5 3 6
## [4345] 11 2 4 4 4 4 7 6 5 3 5 1 3 6 2 9 6 5 5 6 4 1 7 2
## [4369] 6 2 2 5 6 5 4 4 1 4 2 7 7 4 4 5 6 8 6 6 3 7 2 3
## [4393] 5 5 7 2 3 4 5 3 1 2 7 4 7 4 9 1 4 8 1 5 3 4 6 4
## [4417] 6 4 4 6 1 6 6 3 5 4 14 4 7 3 2 5 4 3 5 5 4 5 7 1
## [4441] 7 5 2 6 3 5 7 4 1 3 3 5 8 2 3 4 9 5 2 6 8 6 5 8
## [4465] 4 5 5 6 8 3 7 9 6 4 5 5 2 6 4 5 4 8 8 4 6 6 5 2
## [4489] 3 5 6 5 6 4 4 1 3 3 3 3 7 4 4 1 3 2 7 4 5 2 7 2
## [4513] 3 6 10 7 7 6 6 6 10 2 6 3 5 5 4 4 7 3 1 4 5 2 10 8
## [4537] 7 4 5 4 4 2 4 4 6 6 5 3 7 6 4 6 4 5 9 5 8 6 3 5
## [4561] 0 8 3 7 5 1 7 4 9 4 6 4 2 1 4 4 8 5 6 8 5 7 6 9
## [4585] 4 4 7 4 5 4 8 8 4 2 3 7 5 6 5 3 6 6 7 8 4 3 1 7
## [4609] 6 4 7 4 6 4 3 5 3 4 5 4 6 4 5 8 5 3 10 8 6 2 4 4
## [4633] 3 5 7 8 8 4 3 10 5 2 4 4 7 8 3 5 7 5 4 4 7 6 5 4
## [4657] 2 4 4 7 7 1 6 2 5 4 8 2 7 5 5 5 6 9 5 8 6 0 4 4
## [4681] 1 3 4 5 7 7 3 4 8 11 8 8 4 3 4 3 6 2 6 4 5 4 6 4
## [4705] 9 6 4 4 1 3 5 7 5 5 3 7 4 7 2 5 5 3 4 6 3 3 4 7
## [4729] 4 5 3 6 3 6 4 3 4 5 6 8 7 3 5 6 2 3 5 4 3 5 8 3
## [4753] 6 5 3 8 9 7 3 1 8 8 4 4 3 3 2 4 4 4 5 2 10 5 6 6
## [4777] 3 6 9 8 3 8 11 7 6 4 6 9 7 6 6 3 6 6 2 2 3 3 4 5
## [4801] 4 4 5 5 4 7 5 6 6 4 4 3 7 5 4 4 3 2 6 3 4 10 4 1
## [4825] 6 7 6 8 4 2 4 6 7 2 7 5 7 4 5 5 6 11 11 4 5 3 4 5
## [4849] 6 10 11 8 4 4 2 4 7 4 2 6 4 5 8 8 9 2 5 5 3 4 6 2
## [4873] 6 4 6 5 6 7 4 3 4 2 4 3 2 11 4 4 3 7 3 3 4 6 3 6
## [4897] 5 4 11 5 6 2 2 5 5 3 6 7 2 3 4 8 5 6 7 4 3 4 3 7
## [4921] 7 6 4 8 1 4 3 11 6 6 4 7 2 7 3 3 2 7 2 8 10 4 1 5
## [4945] 4 4 4 2 3 10 9 3 3 7 9 4 5 2 6 6 2 5 10 6 4 3 4 6
## [4969] 8 3 6 5 6 2 5 2 6 4 5 4 7 6 7 6 4 5 5 6 6 7 2 2
## [4993] 6 3 7 6 5 4 8 5## B)
mean(solicitudes)## [1] 5## C)
barplot(table(solicitudes),
col = "red",
main = "Distribución de Solicitudes por Hora (5000)",
xlab = "Número de solicitudes",
ylab = "Frecuencia")
## D)
prob_menor3 <- mean(solicitudes < 3)
prob_menor3## [1] 0.13## 2.
## A)
tiempos<-rexp(5000,1/12)
tiempos## [1] 1.8e+01 2.3e+01 3.8e+00 2.6e+01 5.9e+00 4.0e+01 1.9e+01 3.0e+01 1.8e+01
## [10] 2.6e+01 2.3e+01 4.2e+00 1.0e+01 6.3e+00 4.9e+00 5.2e+00 1.0e+01 2.7e+00
## [19] 1.9e+00 2.1e+01 2.2e+01 2.3e+01 4.6e+01 2.9e+01 8.8e+00 4.4e+00 5.1e+00
## [28] 1.1e+01 5.0e+00 8.4e+00 1.1e+01 4.4e+00 1.7e+00 4.5e+00 4.6e+00 1.6e+01
## [37] 8.0e+01 5.8e+00 4.9e-01 3.7e+00 1.8e+01 1.5e+01 6.5e+00 6.7e+00 1.9e+01
## [46] 8.0e+00 4.4e+00 1.1e+01 3.2e+01 1.9e+00 2.2e+00 4.9e+00 2.8e+01 3.3e+01
## [55] 2.7e+01 4.2e+00 4.1e+01 1.3e+01 2.4e+01 9.2e-01 5.8e+00 2.3e+01 3.6e+01
## [64] 1.3e+01 6.1e+00 5.1e+00 5.1e+00 4.6e+00 1.9e+01 1.5e+01 4.5e+00 1.1e+01
## [73] 1.4e+01 9.4e-01 9.1e+00 2.4e+01 9.6e+00 1.1e+01 2.5e+00 5.3e+00 1.9e+00
## [82] 2.9e+01 1.1e+01 3.4e+00 9.7e-01 5.8e+00 1.2e+01 7.7e+00 5.4e+00 1.0e+01
## [91] 7.7e-01 2.3e+01 3.5e+00 2.9e+00 1.1e+01 4.6e-01 1.3e-01 3.5e+00 1.4e+01
## [100] 1.1e+01 1.9e+01 1.5e+01 1.5e+01 1.2e+01 1.4e+00 1.9e+00 1.5e+01 5.5e+00
## [109] 2.4e-01 4.1e+00 2.4e+00 2.7e+00 6.1e+00 4.8e+00 5.8e+00 7.5e+00 3.3e+01
## [118] 1.7e+00 2.0e+00 3.7e+01 6.1e+00 8.6e-01 1.1e+01 2.7e+01 5.0e+00 1.2e+01
## [127] 2.0e-02 8.9e+00 1.5e+00 2.2e+01 1.4e+01 4.0e+01 7.7e+00 2.6e+00 4.9e+01
## [136] 1.1e+01 1.6e+01 1.8e+01 4.2e+00 1.2e+01 4.3e+00 1.6e+00 1.1e+01 9.7e+00
## [145] 8.7e+00 1.5e+01 2.8e+00 6.0e+00 6.4e+00 7.2e+00 1.4e+01 3.3e+00 3.8e+00
## [154] 8.9e+00 6.0e+00 7.0e+00 4.1e+00 1.2e+00 8.0e+00 2.3e+01 1.4e+01 2.7e+01
## [163] 2.2e+01 7.8e+00 4.5e-01 1.8e+01 1.3e+01 4.6e-01 6.7e+00 4.4e+00 1.0e+01
## [172] 1.3e+01 4.1e+00 1.5e+01 4.0e+01 3.5e+00 9.5e+00 6.2e+00 3.3e+01 3.7e+00
## [181] 7.8e+00 3.5e+00 5.6e+00 2.0e+01 4.6e-01 1.2e+00 9.7e+00 3.8e+00 4.6e+01
## [190] 7.2e+00 9.9e-01 1.1e+01 3.5e+01 1.6e+00 1.8e+01 1.7e+01 7.3e+00 4.4e+00
## [199] 1.1e+01 2.7e+01 3.4e+00 6.5e+01 7.2e+00 2.2e+00 1.6e+01 2.1e+01 7.0e-02
## [208] 4.4e+00 1.7e+01 1.3e+01 2.9e+00 5.5e+00 1.8e+00 1.1e+01 4.5e+00 3.2e+00
## [217] 1.0e+01 1.9e+01 4.2e-01 3.6e+00 4.7e+00 1.3e+01 1.0e+01 7.5e+00 1.9e+01
## [226] 3.1e+01 2.7e+01 7.7e+00 1.4e+01 1.1e+01 2.3e+00 1.7e+00 1.5e+01 9.1e+00
## [235] 1.9e+01 2.0e+01 2.1e+01 6.6e+00 1.3e+01 2.6e+00 1.5e+01 9.7e+00 5.8e-01
## [244] 8.4e+00 1.9e+01 1.4e+01 3.2e+00 1.6e+01 1.1e+01 1.6e+00 1.5e+01 1.1e+01
## [253] 1.1e+01 6.7e-01 8.1e-01 1.4e+01 2.5e+00 4.1e+00 1.4e+01 9.5e+00 1.6e+01
## [262] 8.1e+00 3.1e+00 9.1e-01 3.2e+00 6.6e+00 6.2e+00 7.7e+00 4.5e+00 5.5e-01
## [271] 7.3e+00 8.2e+00 2.1e+01 1.1e+01 1.8e+00 5.3e+00 4.2e+01 8.2e+00 3.9e+00
## [280] 9.6e-01 1.4e+01 1.8e+01 3.8e-01 8.9e+00 2.3e+01 1.1e+01 2.2e+00 3.1e+00
## [289] 2.3e+01 1.3e-02 2.9e+01 2.9e+01 6.1e+01 5.5e+00 3.5e+01 6.2e+00 6.0e+01
## [298] 5.3e+00 7.0e+00 5.0e+01 1.5e+01 1.1e+01 3.3e-01 2.8e+00 3.6e-01 1.4e+00
## [307] 2.9e+00 1.1e+01 1.6e+01 2.6e+00 6.7e+00 1.6e+01 2.0e+01 1.6e+01 1.3e+01
## [316] 1.8e+01 4.6e+00 4.1e-01 2.5e+01 2.0e+00 2.1e+00 5.9e+01 2.0e+01 2.0e+01
## [325] 3.2e+01 3.5e+01 6.9e+00 2.1e+00 4.1e+01 5.4e+00 1.3e+01 2.8e+01 2.3e+01
## [334] 8.8e+00 2.3e+01 4.2e+00 7.6e+00 3.1e-01 9.3e+00 1.0e+01 2.8e+01 3.2e+00
## [343] 4.9e+00 1.1e+02 1.0e+01 2.8e+00 1.6e+01 8.6e-01 2.4e+01 1.2e+01 6.7e-01
## [352] 1.9e+01 1.3e+01 2.9e+00 4.1e+00 5.1e-01 2.4e+00 4.2e+00 1.4e+00 2.1e+00
## [361] 1.3e+01 1.6e+01 5.8e+00 2.4e+00 1.5e+01 1.7e+01 5.7e+00 1.4e+00 1.5e+01
## [370] 7.4e-01 1.3e+01 2.0e+01 1.7e+01 7.5e+00 1.7e+01 3.4e-01 8.1e+00 2.3e+01
## [379] 1.0e+01 1.3e-01 2.2e+00 1.0e+01 2.3e+00 4.4e+00 2.8e+00 6.3e+00 7.1e-01
## [388] 5.9e-01 2.8e+01 5.0e+00 1.0e+01 1.0e+00 1.5e+01 1.0e+01 1.3e-01 3.0e-01
## [397] 1.3e+01 3.9e+01 4.0e+00 3.4e+01 1.4e+01 1.9e+01 1.8e+00 1.3e+01 3.1e-01
## [406] 1.7e+01 1.8e+01 3.2e+00 7.8e+00 9.6e+00 1.8e+01 2.2e+01 2.5e+01 3.5e+01
## [415] 7.8e+00 1.5e+01 4.2e+00 2.3e+01 1.1e+01 2.0e+00 9.6e+00 1.4e+01 7.8e+00
## [424] 1.1e+01 5.5e+00 7.0e-01 4.3e+00 6.3e+00 1.2e+01 2.4e+01 2.2e+01 8.3e-01
## [433] 2.7e+00 4.2e+01 6.1e+00 1.9e+01 1.5e+00 4.0e+00 1.7e+01 5.5e+00 3.8e-02
## [442] 2.3e+01 1.5e+01 1.2e+01 1.3e+01 7.0e+00 1.0e+01 1.6e+01 2.6e+00 8.0e+00
## [451] 3.8e+00 1.4e+00 1.4e+01 9.2e-01 2.1e+01 3.3e+01 1.4e+01 2.7e+01 2.3e+01
## [460] 1.3e+01 3.0e+01 3.7e+00 1.5e+01 1.6e+00 4.5e+00 3.9e+00 4.8e+01 7.9e+00
## [469] 4.0e+01 1.3e+01 1.4e+01 7.7e+00 1.0e+01 5.6e+00 2.3e+01 2.1e+01 2.2e+00
## [478] 7.7e-01 7.8e+00 6.6e-01 1.4e+01 2.9e+01 1.0e+00 1.3e+01 1.7e+01 1.6e+01
## [487] 8.7e+00 2.6e+00 4.5e+00 1.2e+00 1.7e+01 2.4e+01 4.2e+00 3.0e+01 2.7e+01
## [496] 1.1e+01 1.6e+01 2.9e+00 3.2e+01 1.1e+00 1.0e+01 1.3e+01 1.5e+00 1.1e+01
## [505] 2.7e+00 3.3e+01 5.4e+00 2.8e+01 1.6e+01 2.8e+00 7.0e+00 3.4e+01 2.5e+00
## [514] 1.9e+01 1.5e+01 3.7e+00 2.1e+00 1.1e+01 2.6e+01 4.1e+00 4.1e+01 2.3e+00
## [523] 4.8e+00 1.5e+01 3.5e+00 5.1e+01 3.0e+00 5.4e+00 6.5e+00 1.3e+00 1.1e+01
## [532] 4.4e+00 2.0e+01 2.2e+01 2.1e+01 1.7e+01 4.3e-01 1.8e+01 1.5e+01 1.8e+00
## [541] 2.4e+01 8.7e+00 1.1e+01 1.2e+01 9.3e+00 1.1e+01 8.2e+00 3.7e+01 6.5e-01
## [550] 2.3e+01 1.4e+00 4.0e-01 1.6e+01 2.4e+00 5.5e-01 1.3e+01 2.8e+01 2.8e+00
## [559] 5.1e+00 9.2e+00 2.2e+01 2.4e+00 5.2e+00 7.2e+00 1.4e+01 1.7e+01 1.9e+01
## [568] 1.7e+01 1.1e+00 7.0e+00 5.3e-02 7.3e+00 2.6e+00 7.3e+00 1.4e+00 1.6e+01
## [577] 3.4e+00 3.3e-01 5.4e+00 4.1e+00 1.1e+00 1.1e+01 1.5e+01 9.4e+00 8.7e+00
## [586] 5.3e+00 4.1e+00 1.6e+01 3.4e+00 1.0e+01 9.4e+00 1.7e+01 5.9e+00 1.4e+01
## [595] 4.9e+00 4.7e+00 4.3e-01 1.0e+01 3.4e+01 2.1e+00 5.5e-01 1.0e+01 1.3e+01
## [604] 3.5e+01 9.8e+00 6.0e+00 9.8e+00 1.5e+01 1.6e+01 1.1e+01 3.1e+00 5.5e+00
## [613] 5.2e+00 1.6e+01 2.0e+00 4.7e+00 1.8e+01 1.7e+01 1.2e+00 1.4e+01 2.3e+01
## [622] 1.7e+01 2.2e+00 8.3e+00 2.0e+01 3.4e+01 1.0e+01 3.5e+00 2.4e+01 5.4e-01
## [631] 7.9e-01 5.9e+00 4.2e+00 1.8e+01 2.7e-01 1.2e+01 2.1e+01 3.9e-01 1.2e+01
## [640] 1.6e+01 1.1e+01 4.7e-01 1.3e+01 8.4e+00 3.6e+01 1.2e+00 1.1e+00 7.7e+00
## [649] 3.4e+00 1.9e+01 8.2e+00 3.3e+01 2.0e+01 1.5e+01 1.8e+01 6.0e+00 5.9e+00
## [658] 1.8e+01 8.8e+00 3.5e+00 8.4e+00 1.0e+01 1.7e+00 1.2e+00 7.8e+00 2.9e+00
## [667] 3.1e+00 1.1e+01 3.0e+00 9.9e-01 1.0e+01 2.5e+01 3.9e+00 8.7e+00 3.7e+00
## [676] 1.1e+01 9.8e+00 3.6e-01 2.4e+00 1.7e+00 3.8e+00 9.6e+00 4.8e+01 6.3e+01
## [685] 1.8e+00 1.9e+01 1.5e+01 2.0e+01 4.3e+00 2.0e-01 1.1e+01 2.1e+01 6.7e+00
## [694] 3.2e+00 1.7e+00 4.9e+00 1.5e+01 4.8e+00 4.7e+00 2.0e+01 8.2e+00 1.1e+01
## [703] 5.3e+00 6.1e+00 4.5e+00 1.2e+01 9.0e-01 3.0e+01 1.5e-01 6.6e+00 4.5e+00
## [712] 4.1e+00 7.0e+00 1.8e+01 1.9e+00 7.0e+00 1.3e+01 9.2e+00 1.0e+01 2.3e+00
## [721] 1.0e+01 9.1e+00 1.3e+01 6.1e+00 4.5e+00 1.0e+01 2.2e+01 2.0e+01 9.9e+00
## [730] 8.7e-01 1.4e+00 4.7e+00 9.3e+00 3.8e+00 1.8e+00 1.7e+00 1.2e+01 3.9e+01
## [739] 4.2e+01 6.2e+00 4.4e+01 3.0e+01 9.1e+00 4.7e-01 3.6e+00 1.2e+01 3.4e+01
## [748] 2.9e+00 7.4e-02 3.9e+00 2.4e+01 1.5e+01 1.9e+01 9.8e+00 9.9e+00 9.5e+00
## [757] 1.4e+01 3.9e+01 2.9e+01 6.0e+00 7.4e-01 7.9e+00 1.6e+01 2.6e+00 2.2e+01
## [766] 1.3e+00 6.4e+00 9.8e+00 1.4e-01 2.1e+01 2.1e+00 2.5e+01 4.6e+00 2.4e+01
## [775] 3.6e-01 2.1e+00 3.6e-01 7.9e-01 6.2e+00 8.3e+00 6.5e+00 4.3e-01 5.0e+01
## [784] 2.1e+01 1.6e+01 1.6e+00 5.3e+00 9.4e+00 9.9e-01 2.0e+00 1.3e+01 9.9e+00
## [793] 1.5e+01 4.0e+00 2.4e+00 8.1e+00 2.7e+00 6.3e+00 1.7e+01 1.8e+01 4.7e+00
## [802] 1.9e+00 1.6e+00 4.6e+00 1.2e+01 1.7e+00 2.8e+00 8.9e+00 6.5e+00 1.1e+01
## [811] 6.2e+00 2.3e+00 7.4e+01 3.9e+00 1.2e+00 7.9e+00 4.9e+00 8.3e+00 2.6e+00
## [820] 2.3e+00 3.8e-01 3.4e+00 1.1e+01 6.1e+00 5.0e+00 2.2e+01 1.2e+01 2.4e-02
## [829] 4.8e+00 1.2e+01 1.6e+01 4.7e+00 3.8e+00 3.2e+00 1.2e+00 5.5e+01 3.6e+00
## [838] 9.6e+00 1.0e+01 6.9e+01 3.7e+00 8.7e+00 5.7e-02 3.0e+00 1.6e+01 2.6e+00
## [847] 2.2e+01 1.1e+01 9.0e+00 1.9e+00 4.8e+01 5.8e+00 2.0e+01 6.6e+00 2.7e+01
## [856] 1.2e+01 5.5e+00 7.4e+00 5.1e-01 3.0e+01 8.0e+00 2.6e+00 2.5e+01 7.1e+00
## [865] 5.3e+01 1.3e+01 1.3e+01 2.4e+01 5.6e+00 1.2e+01 7.7e+00 2.0e+01 2.2e+00
## [874] 3.6e+00 5.1e+00 1.4e-01 4.3e+00 1.8e+01 2.5e+01 5.4e+00 1.6e+01 1.5e+01
## [883] 1.1e+01 1.1e+01 4.8e+00 3.0e+00 6.1e+00 2.4e+01 1.3e+00 2.6e+00 1.2e+01
## [892] 1.2e+00 4.9e+00 4.2e+00 1.4e+01 1.3e+01 3.7e+00 1.1e+01 6.3e+00 3.1e+01
## [901] 1.3e+00 1.4e+01 9.4e+00 3.9e+01 2.1e+00 1.7e+01 3.2e-01 1.3e+00 2.4e+01
## [910] 6.7e+01 1.0e+01 3.1e+00 5.6e+00 3.6e+00 7.0e+00 8.8e+01 4.9e+01 1.2e+01
## [919] 6.4e+00 1.1e+01 2.5e+00 1.6e+01 7.9e+00 4.9e+00 4.8e+00 3.1e+01 2.9e+00
## [928] 6.1e+00 2.9e+00 2.2e+01 2.4e+01 1.1e+00 2.6e+00 4.5e+00 5.0e+00 4.0e+00
## [937] 2.0e+00 2.0e+00 1.9e+01 2.9e-01 1.3e+01 8.4e+00 1.6e+01 2.7e+00 2.1e+01
## [946] 1.4e+00 9.5e+00 1.8e+01 2.2e+00 7.1e+00 1.8e+00 9.7e+00 4.1e+00 7.9e+00
## [955] 2.3e+01 2.5e+01 1.1e+00 1.8e+01 1.1e+00 1.6e+00 4.4e+01 3.0e+00 5.6e+00
## [964] 8.7e+00 1.2e+01 5.9e+00 6.3e+00 7.1e+00 2.2e+00 2.0e+01 3.2e+01 2.1e+01
## [973] 1.0e+01 1.3e+01 6.5e-01 1.6e+01 4.9e+00 8.8e+00 6.0e+00 3.5e+01 1.2e+01
## [982] 3.1e+01 4.0e+01 2.7e+01 2.2e+01 3.6e+01 1.2e+01 3.8e+01 3.7e+01 8.3e+01
## [991] 7.7e+00 6.0e+01 1.0e+01 5.3e+00 1.5e+00 6.3e+00 1.2e+01 5.8e+01 1.2e+01
## [1000] 8.2e+00 6.0e+00 2.2e+00 1.9e+00 1.8e+01 3.8e+00 6.1e+00 9.9e+00 1.5e+00
## [1009] 8.4e+00 2.0e+01 2.7e+00 7.6e+00 2.4e+00 4.0e+00 3.0e+00 1.0e+01 1.7e+00
## [1018] 6.0e+00 9.1e+00 1.4e-01 4.4e+01 9.9e+01 2.5e+01 3.7e+01 1.5e+00 6.3e+00
## [1027] 1.5e+01 1.8e+01 9.1e+00 5.6e+00 2.7e-01 4.3e+00 2.3e+00 4.9e+00 6.0e+01
## [1036] 1.0e+01 2.6e+01 2.4e+00 2.5e+01 4.5e+00 1.4e+01 9.6e+00 2.8e+00 2.2e+01
## [1045] 1.6e+01 3.2e+00 4.2e+00 4.6e+00 3.1e-01 5.0e+00 5.5e+00 1.5e+01 3.8e+01
## [1054] 9.2e+00 5.4e+01 3.0e+00 8.7e-01 9.9e+00 1.3e+01 1.6e+00 5.8e+00 3.0e+01
## [1063] 9.1e+00 8.9e+00 9.2e+00 9.5e+00 7.9e+00 4.1e+00 5.4e+00 1.5e+01 8.1e+00
## [1072] 2.0e-01 6.5e+00 1.0e+00 2.1e+00 1.2e+01 2.6e+01 7.5e+00 1.8e+00 2.0e+00
## [1081] 1.5e+01 3.5e+00 5.9e+00 2.0e+01 2.9e+01 6.5e+00 4.4e+00 2.0e+01 4.9e-01
## [1090] 9.5e+00 3.4e+01 2.4e+01 1.5e+01 5.1e+00 9.2e+00 1.9e+01 1.7e+01 1.7e+01
## [1099] 9.7e+00 2.6e+01 3.7e+00 2.5e+01 3.2e+01 1.2e+01 2.6e+01 4.7e+00 1.8e+01
## [1108] 2.9e+00 3.8e+01 3.0e+01 9.4e+00 4.2e+00 6.2e+00 2.4e+00 9.6e+00 3.2e+01
## [1117] 4.7e+01 2.1e+01 1.4e+01 7.5e+00 1.7e+01 4.7e+01 2.4e+00 8.0e-01 2.2e+01
## [1126] 8.5e+00 1.1e+01 3.9e+01 1.1e+00 1.7e+01 8.5e+00 4.1e+00 7.6e+00 1.0e+01
## [1135] 2.4e+00 3.1e+00 1.3e+01 1.3e+01 7.0e-01 9.9e+00 4.1e+00 1.8e+01 1.2e+01
## [1144] 2.5e+01 1.8e+00 2.2e+00 2.7e+01 3.7e+00 1.1e+01 7.1e+00 5.8e+00 3.8e+01
## [1153] 3.4e+00 1.7e+01 9.1e+00 2.7e+00 7.1e+00 2.7e+00 1.4e+01 3.3e+01 2.4e+01
## [1162] 3.8e+01 1.1e+01 1.3e+00 1.2e+01 4.5e+00 1.1e+01 9.6e+00 3.2e+00 1.0e+00
## [1171] 1.2e+01 2.8e+00 2.8e+01 2.4e+01 5.2e+01 5.8e+00 1.1e+00 9.5e-01 5.2e+01
## [1180] 1.5e+01 6.1e+01 5.3e+01 2.1e+01 5.8e+00 3.4e+01 8.5e+00 4.6e+00 1.5e+01
## [1189] 2.1e+00 2.6e+00 1.3e+01 1.6e+01 6.2e+00 4.0e+01 6.9e+00 7.2e+00 2.4e-01
## [1198] 1.2e+01 4.5e+01 2.5e+01 9.1e+00 1.0e+01 2.2e+01 6.9e-01 1.3e+01 5.3e+00
## [1207] 9.8e+00 9.2e+00 2.6e+00 1.5e+01 3.6e-01 1.9e+01 6.0e+01 2.9e+00 1.1e+01
## [1216] 1.3e+01 1.6e+01 4.1e+01 3.0e+00 3.1e+01 6.0e+00 6.2e-01 2.8e+00 6.0e+00
## [1225] 6.1e+00 8.5e+01 1.5e+00 1.3e+00 2.2e+00 1.0e+01 9.5e+00 1.9e+00 3.2e+00
## [1234] 1.4e+01 5.3e+00 9.3e-01 1.4e+01 4.3e+01 2.9e+00 1.0e+01 2.3e+00 3.8e+00
## [1243] 4.0e+01 1.6e+00 1.9e+00 3.0e+00 7.4e+00 6.7e+00 1.3e+00 5.6e+00 1.5e+01
## [1252] 1.6e+01 4.9e+00 3.4e+00 1.2e+01 2.8e-01 1.1e+01 2.9e+01 7.3e+00 4.1e+00
## [1261] 2.1e+01 2.8e+01 2.1e-01 1.3e+01 4.4e+00 1.0e+01 1.4e+01 2.5e+01 2.2e+00
## [1270] 2.2e+01 2.5e+01 1.8e+00 1.9e+00 1.1e+00 2.6e+01 9.7e+00 3.5e+00 1.0e+01
## [1279] 2.8e+01 3.7e+01 4.2e+00 1.8e+01 1.7e+00 1.8e+01 2.0e-01 1.3e+01 1.5e+01
## [1288] 7.0e+00 9.6e+00 1.3e+01 2.6e+00 2.6e+01 5.0e-01 9.2e+00 4.9e+01 1.6e+01
## [1297] 1.6e+01 9.4e+00 1.6e+01 1.2e+01 1.2e+01 8.2e+00 1.7e+01 7.1e+00 6.4e+00
## [1306] 1.0e+01 2.2e+01 4.2e+00 4.7e+01 1.5e+01 5.7e+00 4.1e+01 8.7e+00 3.9e+01
## [1315] 1.3e+01 6.4e-01 2.0e+01 2.1e+01 2.0e+01 1.5e+01 4.1e+00 4.3e+00 1.7e+00
## [1324] 6.5e-01 4.4e+01 4.7e+01 5.2e+00 8.2e+00 1.5e+01 8.9e+00 5.3e-01 1.6e+01
## [1333] 3.0e+01 4.9e+00 2.6e+00 6.5e+00 1.5e+00 1.4e+01 2.7e+00 8.7e-01 1.3e+01
## [1342] 6.7e+00 3.8e+01 4.9e+01 2.7e+00 1.2e+01 5.5e+00 3.5e-01 1.2e+01 4.3e+00
## [1351] 3.2e+01 1.2e+01 1.9e+01 9.7e+00 1.0e+01 2.6e+00 5.4e+00 2.5e+00 4.4e+01
## [1360] 7.2e+00 1.2e+01 1.5e+00 3.2e+00 4.3e-01 4.5e+00 1.8e+01 1.4e+01 9.1e+00
## [1369] 1.8e+01 8.1e-01 1.5e+01 7.6e+00 1.1e+01 8.0e+00 3.7e-01 1.8e+01 2.6e+01
## [1378] 1.6e+01 1.2e+01 9.2e-01 5.2e-01 4.8e+00 3.6e+00 1.8e+01 3.2e+00 6.9e-01
## [1387] 1.2e+01 9.0e+00 2.8e+01 1.7e+01 2.8e+01 5.5e+00 2.8e+01 1.6e+01 1.3e+00
## [1396] 1.2e+01 3.1e+01 3.8e+01 4.3e+00 9.6e+00 2.3e+01 8.1e-01 1.8e+01 1.7e+01
## [1405] 3.0e+01 9.5e-01 2.2e-01 7.3e+00 3.6e+01 6.2e+00 9.0e+00 2.7e+01 1.5e+01
## [1414] 1.6e+01 2.9e+01 7.6e+00 1.7e+01 1.9e+00 1.2e+01 1.5e+01 1.1e+01 2.4e+00
## [1423] 9.7e+00 4.1e+00 2.5e+00 6.3e+00 7.1e-01 4.1e+00 4.9e+00 8.7e+00 1.8e+01
## [1432] 7.5e+00 5.5e-01 2.3e+00 2.0e-01 7.6e+00 7.9e+00 5.5e+01 1.3e+01 5.9e+00
## [1441] 1.5e+01 2.1e+01 3.0e+00 1.0e+00 4.4e+00 1.3e+00 7.2e+00 1.8e+01 1.0e+01
## [1450] 2.2e+01 7.7e+00 5.5e+01 3.4e+00 3.5e+00 1.8e+01 4.2e+00 2.4e+01 2.4e+01
## [1459] 1.1e+01 4.1e+01 2.6e+01 3.8e+00 8.2e+00 4.9e+01 5.8e+00 1.1e+00 4.3e+01
## [1468] 2.5e+01 1.3e+01 1.2e+00 4.3e+01 3.0e+00 7.1e+00 3.7e+00 7.1e+00 6.4e+01
## [1477] 2.4e+01 5.0e+00 8.0e+00 7.1e+01 1.7e+00 7.0e+00 1.6e+00 2.9e+00 1.4e+00
## [1486] 1.0e+01 9.4e+00 2.2e+01 1.3e+00 2.2e+01 2.1e+01 6.8e+00 1.5e+01 5.6e+00
## [1495] 1.9e+00 1.3e+01 1.1e+01 1.3e+01 2.3e+01 2.5e-01 2.1e+00 3.4e+00 1.6e+01
## [1504] 1.0e+00 2.0e+01 2.9e+01 6.7e+00 1.5e+01 9.0e+00 8.3e+00 4.2e+00 7.2e+00
## [1513] 2.7e+00 1.7e+01 1.3e+01 7.6e+00 7.1e+01 1.1e+01 1.2e+01 1.2e+01 1.3e+01
## [1522] 2.1e+01 2.2e+01 2.5e+01 1.3e+01 3.9e+00 2.2e+01 4.1e+00 7.0e+00 3.4e+01
## [1531] 3.2e+01 5.7e+00 1.6e+01 1.2e+01 8.5e+00 4.4e+01 8.4e+00 7.4e+00 6.2e+00
## [1540] 3.8e+01 5.6e+00 2.4e+01 3.5e+00 7.8e+00 1.1e+01 3.9e+00 1.7e+00 4.7e+01
## [1549] 1.2e+01 9.2e+00 8.6e+00 2.6e+01 1.5e+00 8.3e+00 6.1e+00 2.3e+00 7.4e+00
## [1558] 1.2e+01 4.7e+00 1.8e+01 1.6e+01 9.6e+00 1.2e+00 1.7e+01 7.0e+01 8.7e-01
## [1567] 2.3e+00 2.5e+00 1.3e+01 7.6e+00 6.3e+01 7.9e-02 1.9e+00 4.0e+00 5.1e+01
## [1576] 9.2e+00 2.2e+01 1.2e+01 8.7e+00 9.5e+00 1.6e+01 1.7e+00 2.2e+01 3.0e+01
## [1585] 1.7e+00 4.2e+01 1.3e+01 1.6e+01 9.4e+00 5.1e+00 3.0e+01 6.9e+00 2.1e+01
## [1594] 2.4e+00 8.1e+00 1.6e+01 2.8e+00 1.2e+01 2.7e+01 2.5e+01 1.7e+01 1.6e+00
## [1603] 1.1e+01 5.0e+00 7.0e+00 5.3e+00 3.1e+00 5.5e+00 3.8e+01 2.7e+01 1.0e+01
## [1612] 3.4e+00 1.4e+01 1.9e+01 6.4e+00 1.3e+01 5.8e+00 2.9e+01 6.9e+00 2.4e+01
## [1621] 2.7e+00 1.3e+01 5.3e+01 2.0e+01 1.1e+01 8.5e-01 1.9e+01 3.4e+01 1.8e+00
## [1630] 8.7e-01 6.8e+00 1.9e+00 4.9e+00 2.2e+00 2.7e+01 7.4e+00 7.2e+00 1.6e+01
## [1639] 2.6e+00 4.9e-01 5.6e-01 5.2e-01 3.2e+00 3.0e+01 5.7e+01 4.5e+00 2.0e+01
## [1648] 3.2e+00 1.6e+01 3.2e+00 1.1e+01 1.7e+00 2.1e+00 6.3e+00 1.3e+01 4.6e+01
## [1657] 6.2e+00 2.9e+00 3.0e+01 5.3e+00 9.8e+00 3.1e+00 1.9e+01 8.2e+00 2.8e+00
## [1666] 2.0e+00 1.2e+01 1.3e+01 1.4e+01 6.1e+00 4.5e+00 3.6e+01 6.5e+00 5.2e+00
## [1675] 9.6e+00 3.4e-01 1.8e+01 3.8e+00 7.4e+00 3.8e+00 4.5e+00 1.3e+00 7.0e+00
## [1684] 3.9e+00 1.7e+01 6.9e+00 7.1e+01 2.0e+01 3.7e-01 9.9e+00 9.5e+00 1.4e+01
## [1693] 1.4e+01 1.1e+01 3.6e+01 3.0e+00 2.3e+00 6.2e+00 1.5e-01 4.6e+01 1.6e+01
## [1702] 1.8e+01 5.7e+00 2.1e+01 1.8e+01 7.0e+00 1.4e+00 8.8e-01 1.6e+01 1.3e+01
## [1711] 1.4e+01 9.3e+00 3.3e+01 6.3e+00 4.8e+01 6.5e-01 4.3e+00 1.5e+01 4.7e-01
## [1720] 5.9e+00 2.6e+00 7.0e+00 2.5e+01 1.8e+00 3.3e+01 1.3e+01 1.6e-01 1.7e+01
## [1729] 2.4e+01 5.1e+00 2.3e+01 1.4e+01 2.5e+01 1.3e+01 1.9e+01 3.4e+01 4.2e+00
## [1738] 6.0e-01 2.8e+00 2.2e+01 2.5e+00 1.6e+01 1.2e+01 1.1e+00 8.0e-01 8.1e+00
## [1747] 4.3e-01 1.4e+01 9.6e+00 1.9e+01 1.3e+01 3.9e+00 3.4e+01 8.5e+00 4.0e-02
## [1756] 1.7e+00 1.3e+01 1.4e+01 4.7e+00 2.4e+01 9.8e+00 1.8e+01 1.3e+00 1.1e+01
## [1765] 9.5e+00 1.6e+01 2.3e+01 2.5e+01 1.6e+01 6.4e+00 3.5e+00 1.5e+01 6.4e+00
## [1774] 3.4e+01 7.0e+00 1.8e+01 9.8e+00 8.6e+00 4.8e+00 1.2e+00 1.1e+00 6.9e+00
## [1783] 2.8e+01 1.0e+01 7.1e-01 1.4e+01 4.3e+01 1.8e+00 1.7e+00 1.5e+01 1.5e+01
## [1792] 9.2e-01 4.8e+00 1.1e+01 3.1e+00 9.4e+00 7.0e+00 1.2e+01 1.7e+01 6.2e+00
## [1801] 6.3e+00 2.7e+00 3.8e-01 1.2e+01 6.3e+00 1.3e+00 6.3e+00 9.5e+00 2.3e+01
## [1810] 4.6e+00 1.8e+01 2.4e+01 1.5e+01 1.7e+01 1.0e+01 1.2e+01 1.5e+01 1.1e+00
## [1819] 3.3e+00 6.4e+00 2.5e+00 6.9e+00 1.1e+01 8.1e+00 3.9e+01 2.4e+01 7.9e+00
## [1828] 2.2e+01 7.3e+00 3.6e-01 4.8e+00 1.1e+01 1.1e-01 1.4e+01 2.3e+00 2.1e+01
## [1837] 2.7e+01 5.0e+01 4.5e+01 2.7e+00 4.0e+00 9.3e+00 6.0e+00 1.4e+01 3.6e-01
## [1846] 3.3e-01 1.4e+01 4.2e+01 2.2e+00 5.7e+01 8.5e+00 6.1e+00 4.1e+01 4.2e+01
## [1855] 1.1e+01 3.8e+01 7.3e+00 3.3e+00 1.1e+01 9.4e+00 3.3e+00 8.1e-01 6.1e+00
## [1864] 2.3e+01 2.1e+00 1.8e+00 9.8e+00 2.0e+00 5.9e+00 1.6e+00 1.3e+00 1.6e+01
## [1873] 1.5e+01 1.7e+01 5.2e-01 1.4e+00 2.8e+01 1.7e-01 2.0e+00 1.0e+01 8.3e-01
## [1882] 2.1e+01 1.5e+00 2.9e+01 1.2e+01 6.5e+00 6.2e-01 2.7e+01 2.5e+00 2.1e+01
## [1891] 1.3e+01 2.8e+01 2.1e+01 2.9e+01 3.4e+00 4.0e-01 2.1e+01 1.8e+01 6.7e+00
## [1900] 1.6e+01 4.4e+00 3.4e+00 1.7e+00 7.1e+00 2.2e+01 3.4e+00 2.4e+00 1.3e+01
## [1909] 1.7e+01 8.3e+00 1.6e+01 3.5e+01 5.1e+01 1.2e+01 1.8e+01 4.4e+00 5.1e+00
## [1918] 2.0e+01 1.4e+01 2.2e+00 1.1e+01 4.8e+01 3.5e+01 2.7e+01 7.9e-01 1.6e+01
## [1927] 6.5e+00 1.3e+01 7.5e+00 1.7e+01 3.7e+00 4.2e+00 3.6e+00 2.5e-01 2.7e+01
## [1936] 4.2e+00 9.9e+00 3.4e-01 2.0e+01 5.3e+00 3.7e+01 3.5e+00 3.1e+00 8.5e+00
## [1945] 4.8e+00 2.9e+01 1.7e+01 6.5e+00 3.0e-01 9.8e+00 1.9e-01 6.1e+00 5.9e+00
## [1954] 7.2e+00 2.0e+01 9.6e+00 1.2e+01 2.2e+01 3.2e+01 1.8e+01 5.7e+00 1.0e+01
## [1963] 6.6e+00 2.0e+01 1.0e+00 8.0e+00 1.7e+00 2.5e+01 5.7e+00 2.8e+01 9.9e+00
## [1972] 1.7e+00 3.5e+00 2.7e+01 8.1e+00 2.0e+00 1.7e+01 2.1e+00 2.6e+01 3.8e+01
## [1981] 1.4e-01 3.5e+01 2.2e+00 6.1e+00 4.5e+01 1.5e+01 2.7e+00 3.4e+01 4.5e+00
## [1990] 3.2e+01 7.7e+00 1.9e+01 2.5e+00 4.6e+00 5.5e+00 2.2e+01 2.6e+01 4.0e+01
## [1999] 1.3e+00 4.1e+01 3.5e+00 1.1e+01 1.6e+01 6.4e+00 1.3e+01 2.5e+00 2.2e+01
## [2008] 2.9e+01 1.9e+00 5.0e-01 3.6e+01 7.5e+00 1.7e+00 1.2e+01 4.3e+00 1.6e+01
## [2017] 5.8e+00 3.6e+00 1.2e+00 1.4e+00 1.1e+01 3.4e+00 2.7e+00 7.5e+00 1.4e+01
## [2026] 9.5e+00 1.2e+01 1.9e+01 2.9e+01 5.8e+00 4.1e+00 8.9e+00 5.7e+00 1.9e+00
## [2035] 5.7e+00 1.5e+01 3.4e+00 6.5e+00 1.5e+01 5.5e+00 3.1e+01 4.5e-01 9.4e+00
## [2044] 1.3e+01 4.2e+00 2.8e-01 2.0e+00 4.9e+01 1.1e+00 3.7e-01 1.8e+01 5.9e-01
## [2053] 3.2e+01 9.1e+00 8.1e+00 2.3e+01 6.6e+00 2.4e-01 8.5e+00 2.0e+00 1.3e+01
## [2062] 1.5e+01 7.2e+00 4.8e+00 1.3e+01 8.6e+00 1.4e+01 2.0e-01 8.4e+00 3.6e+01
## [2071] 3.7e+01 1.4e+01 1.2e+01 3.1e+01 7.3e-01 7.2e+00 4.0e+01 3.6e+00 1.4e+01
## [2080] 8.0e+00 2.2e+01 2.5e+00 5.5e+00 3.1e+01 4.9e-01 1.6e+01 4.7e-01 4.4e+01
## [2089] 1.6e+00 8.2e+00 6.8e+00 6.7e-01 1.3e+01 6.6e+00 5.4e-01 9.1e+00 1.2e+01
## [2098] 2.3e+01 5.7e+00 7.3e+00 5.8e-01 4.0e+01 5.1e+00 2.0e+00 4.5e+00 8.2e-01
## [2107] 7.6e+00 1.7e+01 7.7e+00 2.2e+01 8.2e-01 3.0e+01 3.0e-01 1.5e+01 2.0e+01
## [2116] 2.3e+01 8.7e-01 6.0e+00 7.4e+00 5.0e+00 1.1e+00 5.4e+00 5.2e+00 1.5e+01
## [2125] 2.6e+01 5.6e+00 4.1e+00 7.1e+00 1.4e+01 2.2e+00 2.3e+00 2.8e+00 6.7e+00
## [2134] 1.9e+01 1.4e+01 1.2e+00 7.6e+00 9.0e+00 4.1e+00 7.6e+00 1.0e+01 5.5e+00
## [2143] 3.9e+01 6.9e-01 2.7e+01 7.2e+00 4.3e+01 6.5e+00 1.6e+01 8.0e+00 1.0e+01
## [2152] 1.3e+01 2.0e+00 3.1e+01 2.6e+01 6.5e+00 7.8e+00 1.0e+00 6.0e+00 8.7e-02
## [2161] 1.1e+01 1.8e+00 7.1e+00 1.4e+01 4.8e+00 2.7e+01 1.2e+01 1.6e+00 3.6e+01
## [2170] 9.2e+00 4.4e+01 9.9e+00 1.0e+01 2.4e+01 2.1e+00 1.0e+01 8.6e+00 9.8e+01
## [2179] 2.2e+01 3.9e+00 2.2e+00 1.1e+00 1.4e+01 2.8e+01 1.1e+01 3.6e+00 2.0e+01
## [2188] 2.3e+00 1.0e+00 6.3e+00 3.2e+00 1.2e+01 8.5e+00 1.5e+00 1.6e+01 7.5e+00
## [2197] 1.6e+01 4.3e+00 4.5e+00 1.3e+01 2.4e+01 1.5e+01 5.0e+00 2.8e+00 7.6e+00
## [2206] 1.5e+01 8.6e+00 3.3e+01 5.1e+00 9.4e+00 4.0e-01 8.8e+00 7.1e-01 3.7e+01
## [2215] 7.3e+00 3.1e+01 1.6e+01 1.6e+01 1.0e+01 2.4e+01 1.7e+00 1.2e+01 1.5e-01
## [2224] 2.2e+00 1.6e+01 3.0e+00 1.5e+01 5.6e+00 1.9e+00 3.5e+00 1.5e+01 1.0e+01
## [2233] 1.3e+01 1.4e+00 3.4e+00 3.2e+01 2.0e+01 1.0e+00 1.6e+00 7.2e-01 1.2e+01
## [2242] 1.1e+01 1.2e+00 3.3e+00 3.8e+00 2.0e+01 3.6e+00 8.8e+00 1.0e+01 1.7e+01
## [2251] 2.3e+01 1.7e+01 1.7e+01 3.2e-01 9.6e+00 1.2e+01 3.0e+01 1.6e+01 3.1e+01
## [2260] 1.8e+01 3.6e+01 2.9e+01 1.8e+01 6.2e+00 1.1e+00 1.5e+01 1.3e+01 1.8e+01
## [2269] 1.1e+01 1.5e+01 1.2e+01 1.1e+01 4.4e+00 4.7e+00 2.8e+00 6.8e-01 3.1e+00
## [2278] 1.3e+00 2.1e+00 1.9e+01 4.6e+00 4.9e+00 3.1e+01 4.5e-01 4.8e+00 2.3e+01
## [2287] 2.7e+01 1.7e+01 4.6e+01 4.7e+00 4.9e+00 1.4e+00 5.3e+00 5.9e+00 2.1e+01
## [2296] 1.1e+01 4.1e+00 4.0e+00 2.7e-02 1.2e+01 1.4e+01 4.2e+00 2.0e+01 2.3e+00
## [2305] 8.9e+00 1.7e+01 2.2e+01 1.6e+01 1.1e+01 1.2e+01 3.1e+00 3.4e+01 1.6e+01
## [2314] 5.4e+00 9.8e+00 4.2e+00 1.9e+01 1.8e+01 1.2e+01 3.8e+01 2.3e+00 4.0e+01
## [2323] 1.5e+01 1.8e+01 1.6e+00 4.1e+00 2.6e-01 1.6e+01 8.1e-01 2.1e+01 3.4e+00
## [2332] 1.8e+01 2.3e+00 3.2e+00 1.3e+01 5.1e+00 1.2e+01 4.7e+01 1.7e+01 6.1e+01
## [2341] 3.6e+00 1.0e+01 7.6e+00 2.3e+01 6.1e-02 6.5e+00 1.8e+01 5.6e+00 1.9e+01
## [2350] 1.8e+01 7.6e+00 1.1e+01 2.0e+00 8.2e+00 1.4e+00 3.0e+00 2.2e+01 6.2e+00
## [2359] 4.9e+00 1.0e+01 1.2e+00 1.5e+01 3.3e+00 1.7e+00 1.0e+01 1.6e+01 1.6e+01
## [2368] 2.6e+01 5.4e+00 1.8e+01 6.4e-01 1.8e+00 5.5e+01 2.6e+00 2.5e+01 8.2e+00
## [2377] 2.3e+01 1.4e+01 4.1e+00 5.3e+00 1.5e+01 4.3e+01 1.5e+01 4.5e+00 8.1e+00
## [2386] 1.1e+01 3.0e+00 1.8e+01 2.1e+01 1.1e+00 3.2e+00 1.1e+01 4.1e+00 9.9e+00
## [2395] 1.2e+01 4.2e+00 3.7e+00 2.1e+01 7.3e+00 6.7e+00 1.9e+01 3.3e+00 6.9e+00
## [2404] 1.2e+01 2.4e+01 1.7e+01 2.1e+01 7.8e+00 1.8e+01 1.9e+00 3.3e+01 1.7e+01
## [2413] 5.9e+00 1.4e+01 1.9e+01 1.3e+00 3.1e+01 7.1e+00 1.2e+01 7.6e+00 1.1e+01
## [2422] 3.5e+01 1.8e+00 2.0e+01 1.4e+01 1.0e+00 1.8e+01 1.5e+01 2.0e+00 1.5e+01
## [2431] 4.0e-01 2.1e+01 4.9e+00 4.2e+00 4.8e+00 5.5e+00 1.0e+01 4.1e+01 1.0e+01
## [2440] 1.1e+01 8.6e+00 3.9e+00 4.6e+01 7.4e+00 9.0e+00 6.6e+00 3.3e+00 4.1e+01
## [2449] 2.3e+01 1.4e+01 1.4e+00 2.4e+01 9.4e-01 1.5e+01 4.6e+00 3.2e+00 1.9e+00
## [2458] 2.4e+01 4.5e+00 1.2e+01 2.4e+01 4.5e+01 1.8e+01 1.3e+01 3.2e+00 1.1e+01
## [2467] 5.5e+00 3.4e-01 5.2e+01 9.1e+00 1.0e+01 2.7e+00 4.9e+00 2.2e+01 3.2e+01
## [2476] 5.4e+00 8.6e+00 3.7e+00 9.8e+00 5.9e-01 2.0e+01 3.1e+00 1.9e+01 1.5e+01
## [2485] 2.9e+01 1.6e+00 1.2e+01 7.4e+00 2.3e+00 1.8e+01 1.2e+01 5.7e+00 8.7e+00
## [2494] 3.6e+01 5.6e+01 7.0e+00 3.2e+01 5.6e+00 3.8e+01 2.4e+00 2.3e+01 2.8e+01
## [2503] 4.7e+00 1.9e+01 7.0e+00 8.1e-01 1.9e+01 1.1e+01 7.7e+00 4.5e+00 1.4e+01
## [2512] 1.3e+01 1.0e+00 5.9e+00 3.0e+00 6.0e+00 5.4e+00 1.1e+01 1.0e+01 3.2e+01
## [2521] 3.6e+00 4.8e+00 2.0e+00 2.5e+01 3.5e+01 7.4e+00 5.1e+00 1.6e+01 1.0e+01
## [2530] 1.4e+01 3.1e+00 3.4e+00 8.7e+00 3.2e+00 1.7e+01 2.3e+01 8.5e+00 7.7e+00
## [2539] 1.9e+00 2.4e+01 1.3e+01 2.7e+01 5.3e+00 1.3e+01 1.1e+01 1.3e+01 8.8e+00
## [2548] 1.4e+01 2.5e+01 1.4e+01 2.2e+00 5.2e+00 8.4e+00 4.4e+00 1.9e+01 1.0e+00
## [2557] 1.9e+01 1.5e+01 6.2e-01 9.2e+00 2.6e+01 4.3e+00 9.5e+00 1.6e+01 9.3e-01
## [2566] 4.6e+00 1.2e+01 1.9e+01 9.8e+00 3.2e+00 3.5e+00 5.8e+00 8.0e-01 2.2e+00
## [2575] 2.6e+01 1.7e+01 3.9e+00 1.7e+00 1.9e+01 2.0e+00 2.0e+00 6.7e+00 6.7e+01
## [2584] 5.0e+01 1.4e+00 3.4e+00 1.6e+01 1.9e+00 4.8e+00 3.9e+00 2.1e+01 9.1e+00
## [2593] 4.0e+00 2.6e+01 7.4e+00 4.1e-01 9.7e+00 1.7e+01 1.1e+00 1.3e+01 3.6e+00
## [2602] 6.9e+00 3.2e+00 7.3e+00 2.1e+01 1.3e+01 9.2e+00 2.8e+01 3.7e+00 1.5e+01
## [2611] 1.2e+01 2.3e+01 4.6e+00 6.0e+00 6.1e+00 1.9e+01 1.0e+00 1.8e+01 5.6e+00
## [2620] 3.5e+01 1.4e+01 2.3e-01 2.2e+01 3.1e+00 2.8e+01 2.1e+00 1.8e+01 7.4e+00
## [2629] 8.1e+00 2.5e+01 1.0e+01 1.1e+00 1.0e+00 5.2e+00 2.6e+01 5.8e+00 5.2e-01
## [2638] 9.6e+00 5.0e+01 7.9e+00 5.6e+00 6.9e+00 1.3e+01 1.5e+01 7.4e+00 2.7e+01
## [2647] 6.6e+00 9.5e+00 2.5e+01 1.0e+01 1.4e+01 3.0e+01 1.0e+01 2.6e+01 3.5e+00
## [2656] 7.5e-01 1.5e+00 4.9e+00 2.5e+01 1.8e+00 7.8e-01 1.9e+00 1.0e+01 8.8e-01
## [2665] 3.4e+00 1.2e+01 2.5e+00 2.9e+00 1.5e+01 2.3e+01 1.9e+01 1.1e+01 2.2e+00
## [2674] 1.1e+01 5.9e+00 1.3e+01 1.1e+00 2.5e+00 1.5e+01 9.1e+00 1.4e+01 2.6e+00
## [2683] 1.2e+01 1.2e+01 8.8e+00 6.6e+00 7.2e+00 4.1e-01 2.4e+00 4.2e+00 3.3e+01
## [2692] 3.2e+00 1.3e+01 1.1e+01 1.0e+01 8.9e+00 6.6e+00 4.9e+00 2.2e+01 1.0e+01
## [2701] 2.7e+00 5.0e+00 1.7e+01 2.1e+01 1.6e+01 1.5e+01 9.5e+00 2.1e+00 4.4e+00
## [2710] 1.8e+00 1.2e+01 2.4e+01 1.4e+00 1.5e+01 5.7e+00 4.2e+00 4.6e+00 1.6e+01
## [2719] 3.0e-01 1.2e+01 1.3e+01 2.2e+01 4.3e+00 1.0e+01 1.9e+01 7.9e+00 2.0e+01
## [2728] 4.4e+01 1.1e+01 1.1e+01 3.0e+00 1.1e+01 1.6e+01 4.7e+01 3.4e+00 9.0e+00
## [2737] 3.4e+01 8.4e+00 2.6e+00 2.2e+01 1.8e+00 5.5e+00 2.5e+01 9.4e+00 8.7e+00
## [2746] 2.7e+01 1.2e+01 2.2e+00 3.3e+01 3.0e+00 1.2e+01 2.5e+01 9.4e+00 9.0e+00
## [2755] 2.2e+01 2.8e+01 9.4e+00 1.3e+01 1.7e+01 1.2e-01 1.1e+01 1.0e+00 2.3e+01
## [2764] 5.7e+00 9.2e+00 2.4e+01 6.1e+00 2.8e+01 4.6e+01 5.0e+00 6.0e+00 2.1e+00
## [2773] 5.1e-01 1.8e+01 1.5e+01 8.5e+00 1.6e+01 1.9e+01 9.6e+00 4.5e+01 8.0e+00
## [2782] 2.0e+01 8.5e-01 1.0e+01 2.2e+00 8.6e+00 2.7e+01 7.3e+00 4.0e-01 1.0e+01
## [2791] 2.8e+00 1.4e+00 2.2e+01 1.2e+00 6.0e+00 2.4e+01 3.2e+00 1.0e+01 2.7e+01
## [2800] 2.7e+01 4.8e+00 2.0e+01 4.9e+00 5.9e-01 1.5e+01 2.6e+01 1.5e+00 3.0e+01
## [2809] 3.3e+01 1.7e+01 5.0e+00 5.3e-01 1.5e+01 5.5e+00 8.8e-01 6.5e+00 1.0e+01
## [2818] 9.1e+00 6.7e+00 7.8e-01 2.0e+01 1.0e+00 3.9e+01 1.1e+01 2.5e+01 2.6e+00
## [2827] 7.5e-01 6.5e-01 2.0e+00 1.2e+00 2.3e+01 3.4e+01 5.2e+01 3.2e+00 1.7e+01
## [2836] 1.4e+01 2.0e-01 1.4e+01 1.8e+01 1.2e+01 2.0e+01 6.7e+00 2.1e+00 2.1e+00
## [2845] 6.4e+00 2.0e+01 6.1e+00 3.5e+00 3.9e+00 1.3e+01 4.7e+00 1.1e+01 2.3e+00
## [2854] 1.2e+01 2.6e+00 1.4e+01 5.0e+00 4.1e+00 1.9e+01 1.0e+01 5.0e-01 3.6e+01
## [2863] 2.4e+01 5.9e+00 9.0e+00 1.8e+01 1.9e+00 5.7e+00 2.0e-01 3.8e+01 4.0e+01
## [2872] 2.0e+01 4.8e+00 2.8e+01 9.6e+00 7.8e-01 1.0e+01 3.5e-01 2.1e+01 6.7e-01
## [2881] 6.2e+00 1.3e+01 1.3e+01 8.5e-01 1.6e+01 1.6e+01 1.4e+01 1.2e+01 2.7e+00
## [2890] 1.6e+01 3.8e+00 7.9e+00 1.0e+01 2.0e+01 1.3e+00 1.1e+01 2.4e+01 1.5e+01
## [2899] 1.7e+01 1.3e+01 8.5e+00 4.2e-01 1.7e+00 5.1e+00 4.8e+00 3.0e+01 1.7e+00
## [2908] 1.9e+01 4.8e+00 2.0e+01 9.3e+00 2.0e+01 2.0e+01 2.7e+01 2.5e+01 1.1e+01
## [2917] 2.5e+00 2.0e+01 2.1e+01 7.7e+00 3.9e+00 7.8e-01 4.7e+00 1.4e+01 1.3e+01
## [2926] 5.1e+01 1.7e+01 1.1e+01 4.1e+00 2.1e-01 2.7e+00 2.6e+00 1.5e+00 4.2e+01
## [2935] 3.1e+00 5.2e+00 9.0e+00 3.5e+01 4.6e+01 8.4e+00 4.8e+00 1.8e+01 2.2e+00
## [2944] 4.2e+00 1.7e+01 1.3e+01 5.2e+00 2.0e+01 1.4e+01 1.6e+01 3.6e+00 2.1e+00
## [2953] 3.0e-01 1.8e+01 4.5e+01 2.4e+01 1.5e+01 3.8e+01 1.8e+01 2.6e+01 3.3e+01
## [2962] 1.8e+01 7.5e+00 9.6e+00 4.0e+00 2.3e+01 1.5e+01 1.2e+01 2.6e+01 1.1e+01
## [2971] 6.9e+00 1.5e+01 1.6e+01 1.5e+00 1.4e+00 2.2e+00 1.1e-01 1.2e+01 2.5e+01
## [2980] 2.2e-01 3.1e+00 4.3e+00 4.6e-01 2.1e+01 8.3e+00 1.2e+00 1.7e+01 2.6e+01
## [2989] 4.9e+01 1.8e+01 5.3e+00 3.6e+01 2.2e+00 1.4e+01 7.8e+00 4.2e+00 1.0e-01
## [2998] 3.9e+01 1.0e+02 6.4e+00 8.0e+00 1.5e+01 4.7e+00 3.6e+00 5.0e+00 1.2e+00
## [3007] 1.1e+01 1.4e+01 1.2e+01 5.7e+00 4.0e+00 5.6e+00 2.1e+01 4.2e+00 1.2e+00
## [3016] 1.6e+01 2.1e+01 3.9e+00 1.2e+01 1.4e+01 7.8e+00 1.4e+00 7.0e+00 8.5e+00
## [3025] 1.1e+01 4.9e+00 1.3e+01 1.6e+00 1.0e+01 3.6e+00 3.5e+00 2.9e+00 4.7e+00
## [3034] 3.6e+01 5.0e+00 3.4e-01 1.0e+01 1.8e+01 2.7e+00 2.4e+00 7.2e+00 4.8e+01
## [3043] 1.1e+01 6.2e+00 3.1e+01 4.0e+00 7.9e+00 1.4e+01 1.0e+01 1.2e+00 1.5e+00
## [3052] 2.7e+00 9.4e+00 7.6e+00 4.6e+01 7.6e+00 1.1e+01 7.0e+00 4.2e+00 1.2e+00
## [3061] 1.0e+01 1.5e+00 7.3e+00 1.9e+00 1.6e+01 7.9e-01 1.6e+00 4.0e+00 6.3e+00
## [3070] 2.2e+01 4.4e+01 3.4e+00 2.3e+00 1.8e+01 2.0e+01 3.9e+01 1.9e+01 1.5e+01
## [3079] 2.2e+01 9.9e+00 6.0e+00 7.2e+00 6.6e+00 5.9e+00 6.3e+00 1.5e+01 4.7e+00
## [3088] 6.1e+00 1.0e+01 2.2e+01 3.1e+01 1.6e+01 2.0e+01 3.8e+00 4.3e+01 9.6e-01
## [3097] 4.1e+00 4.2e+00 4.3e+00 2.0e+00 4.1e+00 1.6e-01 5.0e-01 3.0e+01 1.1e+01
## [3106] 1.3e+01 2.0e+01 3.0e+01 1.9e+00 1.7e+01 1.5e+01 5.7e+00 2.2e+01 1.7e+00
## [3115] 4.9e+00 1.1e+00 3.7e+00 8.0e+00 7.5e+00 3.1e+00 1.0e+01 1.8e+01 4.7e+01
## [3124] 1.3e+01 5.4e+00 1.9e+01 4.0e+01 5.9e+00 6.3e+00 4.6e+00 4.6e+00 2.6e+01
## [3133] 1.3e+01 7.3e-01 4.0e-01 2.2e+00 4.1e+00 1.2e+01 1.2e+01 5.9e-01 5.6e+00
## [3142] 1.6e+01 1.4e+00 1.6e-01 6.6e+00 5.0e+00 9.9e+00 3.3e+00 7.9e+00 1.7e+00
## [3151] 1.9e-01 4.1e+00 9.0e+00 1.2e+01 2.3e+01 9.4e+00 2.1e+01 2.6e+01 5.1e+00
## [3160] 6.4e+00 9.3e+00 1.4e+01 2.8e+01 2.7e+00 1.2e+01 3.6e+01 6.6e+00 2.9e+01
## [3169] 1.8e+01 9.2e-01 1.4e+01 4.3e+00 1.1e+01 1.2e+01 1.4e+01 4.5e+00 7.9e+00
## [3178] 6.0e+00 1.1e+01 5.0e-01 2.6e+00 3.5e+00 9.7e-01 2.0e+00 3.8e+00 7.2e-02
## [3187] 1.5e+01 1.9e+00 8.5e+01 2.9e+00 5.1e+00 2.2e+01 4.6e+01 6.8e-01 2.9e+00
## [3196] 3.1e-01 1.1e+01 1.0e+00 8.9e+00 4.2e+00 2.6e+01 1.1e+01 2.7e+00 8.0e+00
## [3205] 3.4e+01 2.3e+00 7.4e+00 1.1e+01 5.5e+00 6.0e+00 7.3e+00 1.6e+01 2.2e+01
## [3214] 6.7e+00 8.2e+00 3.3e+01 5.4e-02 8.3e+00 3.3e+00 3.6e+00 4.2e+00 1.0e+01
## [3223] 1.1e+00 8.6e+00 1.4e+00 7.0e+00 1.5e+01 2.1e+01 3.9e+00 8.2e+00 7.9e+00
## [3232] 1.3e+00 8.8e+00 3.5e+00 1.3e-01 4.8e+01 6.5e-01 8.0e-01 5.9e+00 2.5e+01
## [3241] 3.4e+01 1.2e+01 2.7e+00 5.6e-01 4.5e+00 1.4e+01 1.7e+01 1.7e+01 3.1e+01
## [3250] 4.7e+01 2.4e+00 2.2e+01 5.7e+00 9.1e+00 3.6e+00 1.0e+01 2.4e+01 4.7e+00
## [3259] 4.9e+00 3.2e+00 2.7e+00 5.2e+00 1.2e-01 2.0e+01 6.7e+00 4.2e+00 6.3e-01
## [3268] 9.2e+00 2.3e+00 2.9e+01 9.1e+00 2.5e+00 3.2e+00 9.5e+00 1.7e+01 2.1e+01
## [3277] 5.9e+00 3.3e-01 2.4e+01 5.7e+00 2.2e+01 6.4e-01 9.0e-01 2.8e+00 1.2e+01
## [3286] 8.9e-01 3.1e+01 2.0e+01 1.5e+01 4.7e+00 2.6e+01 3.7e+01 2.2e+01 6.6e+00
## [3295] 8.7e-01 2.1e+00 5.7e+00 8.1e-01 6.9e+00 3.5e+00 1.4e+01 8.6e+00 1.3e+01
## [3304] 3.6e-01 1.9e+01 9.2e+00 2.7e+00 6.6e+00 3.4e+01 7.9e+00 6.6e+00 1.9e+01
## [3313] 1.8e+01 1.4e-01 2.2e+01 7.9e+00 3.6e+00 3.8e+01 6.0e+00 3.6e+00 9.4e+00
## [3322] 7.5e+00 1.2e+01 5.3e+00 5.8e+00 5.6e+00 3.7e+00 4.0e+00 1.0e+01 1.8e+01
## [3331] 9.6e+00 1.6e+01 1.1e+01 1.1e+01 1.5e+01 2.4e+01 1.6e+01 6.0e-01 9.7e-01
## [3340] 1.2e+01 4.0e+00 2.2e+01 5.0e+00 1.9e+01 6.5e+01 1.1e+01 3.7e+00 9.4e-01
## [3349] 1.6e+00 1.0e+01 3.2e+01 5.7e-01 7.1e+00 1.1e+01 9.9e-01 2.8e+01 2.0e+00
## [3358] 1.1e+01 6.4e-01 4.8e+00 2.6e+01 3.2e-03 2.8e+00 1.6e+01 2.0e+01 1.6e+00
## [3367] 1.3e+01 4.3e+00 3.4e+01 1.1e+00 2.7e+01 2.3e+01 7.1e+00 2.9e+01 6.0e+00
## [3376] 2.4e+01 2.8e+00 1.4e+01 1.7e+00 2.4e+01 2.6e+01 1.1e+01 4.5e+01 4.9e+00
## [3385] 1.2e+01 3.5e+01 6.1e+00 4.3e+00 1.1e+01 3.7e+00 2.5e+00 1.1e+01 1.3e+01
## [3394] 8.9e+00 1.6e+01 1.7e+01 9.0e+00 3.5e+01 1.6e+00 9.8e+00 3.3e+00 4.5e+00
## [3403] 2.8e+01 1.2e+01 1.7e+00 7.5e+00 1.4e+01 1.6e+01 1.5e+01 3.2e+01 9.2e+00
## [3412] 5.9e+00 6.1e-01 3.0e+01 1.1e+01 2.0e+00 3.5e+00 5.9e+00 6.2e+00 2.1e+00
## [3421] 1.3e+00 1.2e+01 5.0e-01 1.0e+01 5.4e+00 2.8e+00 1.0e+01 8.8e+00 2.6e+00
## [3430] 1.3e+01 2.5e+01 1.8e+00 4.5e+00 8.1e+00 1.2e+01 5.6e+00 4.4e+00 2.1e+00
## [3439] 6.6e+00 2.7e+01 2.0e+01 1.7e+01 2.1e+01 4.5e+00 6.5e+00 1.1e+00 1.2e+01
## [3448] 4.7e+00 4.0e+00 7.5e+00 3.9e+01 2.7e+00 3.2e+01 4.0e+00 1.6e+01 2.3e+00
## [3457] 1.4e+01 2.8e+01 1.9e+01 3.3e+00 2.4e+01 1.8e+01 4.3e+00 2.3e+01 2.6e+01
## [3466] 2.8e+00 1.0e+01 1.7e+00 2.2e+01 1.0e+01 6.1e+00 5.5e+00 3.3e+01 9.6e-01
## [3475] 4.1e-01 1.0e+01 1.0e+01 1.4e+01 1.0e+00 2.0e+01 1.8e+01 5.0e+00 3.2e+01
## [3484] 7.1e+00 1.1e+01 1.7e+00 2.4e+00 3.6e-01 4.4e+00 1.5e+01 1.3e+01 2.5e+00
## [3493] 3.5e+00 9.2e+00 2.2e+00 2.1e+01 6.4e+00 1.5e+01 1.2e+01 6.7e+00 2.6e+01
## [3502] 6.9e+00 3.2e+00 9.1e+00 3.0e+01 1.1e+01 8.5e+00 2.7e+00 5.0e+01 4.6e+01
## [3511] 9.3e+00 1.1e+00 5.6e-01 7.8e+00 8.3e+00 6.2e+01 2.0e+01 9.0e+00 6.1e+00
## [3520] 5.1e+00 1.3e+01 6.9e-02 2.1e+01 1.0e+01 3.2e+01 1.2e+01 1.1e+01 1.7e+01
## [3529] 1.1e+01 2.1e+00 1.8e+00 8.5e+00 2.1e+01 9.1e-01 1.3e+00 3.7e+00 8.0e-01
## [3538] 7.5e-01 3.3e+00 1.8e+01 7.9e+00 5.4e+00 7.2e+00 3.2e+01 2.6e+01 1.5e+01
## [3547] 2.3e+01 8.1e+00 4.4e+00 7.4e-01 4.0e+00 1.5e+01 6.9e+00 2.8e+01 1.8e+01
## [3556] 7.7e+00 3.3e+00 1.7e+01 6.1e+00 6.3e+00 1.9e+00 1.4e+01 7.6e-02 4.9e+00
## [3565] 5.7e+00 2.0e+01 7.0e+00 7.7e+00 1.1e+01 1.5e+01 1.2e+01 4.3e+00 1.7e+00
## [3574] 2.0e+01 7.9e+00 1.5e+01 2.6e-01 2.0e+01 1.4e-01 8.6e+00 3.5e+00 1.3e+01
## [3583] 4.5e+00 9.4e-01 3.9e+00 3.3e+00 8.9e+00 1.1e+01 1.6e+00 1.5e+01 4.6e+00
## [3592] 3.5e+01 1.9e+01 1.2e+01 1.1e+00 4.7e+00 6.5e+00 7.6e+00 2.4e+00 3.1e-01
## [3601] 2.3e+01 2.6e+01 3.9e-01 5.3e+00 2.8e+00 6.4e+00 1.3e+01 2.2e+01 2.9e+00
## [3610] 3.7e+00 1.3e+01 2.5e+01 1.4e+01 1.2e+01 3.5e+00 3.1e+00 3.0e+01 3.9e+01
## [3619] 5.9e+00 1.8e+00 2.7e+01 2.3e+00 1.1e+01 6.6e-01 1.7e+01 6.1e+00 1.3e+01
## [3628] 4.2e+00 2.1e+01 2.5e-02 4.9e+00 2.2e+00 6.4e-01 2.4e+01 1.5e+01 4.7e+00
## [3637] 7.1e+00 3.6e+00 8.8e-01 1.2e+01 6.3e+00 1.8e+01 4.3e+00 2.7e+00 3.4e+00
## [3646] 9.6e+00 1.2e+01 5.9e+00 9.4e+00 4.8e+01 8.4e+00 1.6e+01 2.8e+01 1.6e+01
## [3655] 4.1e+00 1.3e+00 5.5e+00 1.3e+01 8.1e+00 2.2e+01 2.5e+01 3.3e+01 1.3e+01
## [3664] 4.0e+01 5.6e+00 6.4e-01 7.8e-01 2.0e+00 3.2e+01 2.6e+01 1.3e+01 3.0e+00
## [3673] 2.2e+01 1.1e+00 4.7e+01 1.2e+01 3.7e+01 2.1e+01 5.6e+00 1.7e+00 1.0e+01
## [3682] 9.4e+00 7.3e+00 5.3e-01 3.3e+01 1.9e+01 2.4e+01 7.2e+01 5.3e+00 2.3e+00
## [3691] 4.9e+00 4.7e+00 9.3e+00 2.6e+00 3.8e+00 1.1e+01 6.6e+00 5.3e+01 1.2e+01
## [3700] 3.0e+00 1.4e-01 6.9e+00 7.9e+01 1.6e+00 3.7e+00 2.0e+01 1.9e+01 4.0e+00
## [3709] 1.3e+01 4.9e+00 7.0e-01 4.5e+00 3.6e-01 2.6e+00 1.9e+01 5.3e+00 2.2e+01
## [3718] 3.2e+01 1.5e+01 1.7e+01 2.6e+00 2.7e+01 4.0e-01 3.9e+01 5.1e+01 1.8e+00
## [3727] 7.7e+00 1.6e+01 2.0e+01 8.2e+00 1.6e+00 4.0e+01 4.6e+00 1.5e+01 1.7e+01
## [3736] 4.7e-01 9.3e+00 1.4e+01 3.2e+00 1.0e+01 3.0e+01 1.2e+01 9.8e+00 2.8e+00
## [3745] 9.7e+00 1.9e+00 1.9e+01 2.3e+00 8.1e+00 7.3e-01 4.9e+00 2.0e+00 3.0e+01
## [3754] 6.7e+00 1.0e+01 1.9e+01 7.3e+00 4.7e+00 5.2e+00 1.3e+01 7.7e+00 7.3e+00
## [3763] 1.2e+01 9.4e+00 2.3e+01 2.7e+00 2.6e-01 2.8e+01 1.5e+01 7.9e-01 6.1e+00
## [3772] 3.6e+01 2.0e+00 7.6e+00 1.1e+01 1.4e+01 1.2e+01 1.3e+01 1.1e+01 2.1e+01
## [3781] 3.6e+00 6.8e+00 1.8e+00 2.7e+00 7.4e+00 4.9e+00 2.8e+00 1.2e+00 1.5e+00
## [3790] 8.6e-01 7.4e+00 1.8e+00 7.0e+00 2.0e+01 1.5e+00 8.9e-01 1.5e+00 2.7e+00
## [3799] 2.6e+01 7.6e+00 1.6e+01 4.2e+00 2.0e+01 1.7e+01 2.4e+01 6.2e+01 1.1e+01
## [3808] 5.9e+00 3.6e+00 1.7e+01 9.6e+00 1.4e+01 8.3e+00 3.8e+00 2.8e+01 1.0e+01
## [3817] 2.9e+01 7.2e+00 6.6e+00 1.1e+01 5.9e+01 3.8e+00 1.9e+00 8.5e+00 1.4e-01
## [3826] 2.3e-01 3.7e+00 6.2e+00 1.8e+01 1.2e+01 2.3e-01 7.8e+00 1.1e+01 3.0e+01
## [3835] 2.9e+01 1.2e+01 4.1e+00 1.4e+01 6.5e-01 4.4e+00 2.3e+01 8.8e-01 9.5e+00
## [3844] 1.5e+01 4.0e+00 8.1e+00 1.5e+01 2.1e+01 8.4e+00 5.2e+00 5.3e+00 2.1e+00
## [3853] 5.4e-02 9.6e+00 7.2e+00 1.2e+01 7.0e-01 3.7e+01 1.6e+01 7.9e-01 9.0e+00
## [3862] 2.9e+01 1.4e+01 1.6e+00 2.8e+00 7.8e+00 8.8e+00 9.0e+00 3.3e+00 1.8e+00
## [3871] 3.1e+01 4.4e+00 1.1e+01 3.0e+01 1.7e+01 3.6e+00 7.9e+00 2.1e+01 5.4e+00
## [3880] 1.2e+01 7.2e-01 1.2e+00 1.1e+01 1.0e+01 5.6e+00 2.8e+00 5.9e+00 5.5e-01
## [3889] 2.1e+01 1.6e+01 7.5e+00 4.9e+00 3.9e+00 3.7e+00 1.5e+00 2.4e+00 8.9e+00
## [3898] 2.2e+00 4.4e+00 2.1e+01 7.0e+00 1.0e+00 8.8e+00 2.4e+01 6.5e+00 3.0e+01
## [3907] 9.2e-02 1.8e+00 4.8e+01 2.1e+00 1.7e+01 7.0e+00 5.2e+00 8.2e+00 3.1e+01
## [3916] 3.0e+01 1.9e+00 3.2e+01 3.2e+00 1.2e+01 1.4e+01 1.1e+01 1.0e+00 5.9e+00
## [3925] 1.0e+01 1.8e+01 4.6e+00 3.6e+01 4.2e+00 3.0e+01 1.7e+01 2.3e+00 4.6e+00
## [3934] 4.0e+01 4.1e+01 1.1e+01 3.0e+01 2.0e+01 3.2e-01 6.3e+00 7.1e-01 5.5e+01
## [3943] 7.9e+00 6.0e+00 2.1e+00 1.7e+01 4.8e+00 2.0e+00 1.8e+01 6.7e+00 2.8e-01
## [3952] 1.8e+01 1.5e+01 2.0e+01 2.0e+01 1.7e+01 7.2e+00 1.9e+01 1.0e+01 2.2e+01
## [3961] 2.4e+01 1.3e+00 3.0e+00 4.4e+01 3.1e+00 1.7e+00 5.9e+00 1.4e+01 1.7e+01
## [3970] 1.5e+00 3.9e+00 7.3e+00 2.9e+00 1.2e+00 4.6e+00 1.3e+01 6.0e+00 6.4e+00
## [3979] 1.7e+00 6.9e+00 2.4e+01 1.0e+00 1.5e+01 5.3e+00 2.3e+00 5.7e+00 3.1e-01
## [3988] 5.6e+00 1.7e+01 1.9e+01 1.5e+01 2.2e+01 2.1e+01 3.7e+01 9.4e+00 1.2e+01
## [3997] 1.0e+01 1.1e+01 6.2e+00 2.2e+00 1.4e+01 3.7e+00 3.1e+01 2.5e+01 1.0e+01
## [4006] 6.1e+00 2.8e+00 3.2e-01 2.4e+01 1.3e+00 1.6e+01 4.4e+00 1.4e+01 1.6e+01
## [4015] 3.7e+01 2.0e+01 4.2e+01 3.5e+01 1.1e+01 4.4e+00 3.1e+01 2.0e+00 1.1e+00
## [4024] 3.1e+00 1.1e+01 1.5e+00 3.6e+01 6.8e+00 4.5e+00 5.9e+00 1.1e+01 4.3e+00
## [4033] 1.4e+01 9.1e+00 1.1e+01 2.4e+01 3.8e-01 1.1e+01 1.3e+01 1.4e+01 5.9e+00
## [4042] 5.1e+00 1.0e+01 7.8e+00 5.8e+00 2.2e+00 1.0e+01 1.8e+01 2.0e+01 5.8e+00
## [4051] 6.5e+00 1.4e+01 1.7e+00 1.0e+01 4.1e+01 2.4e+00 2.5e+00 4.5e+00 1.7e+01
## [4060] 2.7e+01 1.1e+01 5.6e+00 1.9e+01 2.8e+00 1.1e+00 2.8e+01 8.8e+00 6.5e+00
## [4069] 2.1e+01 1.4e+01 7.6e+00 1.2e+01 4.6e+01 1.2e+01 8.7e+00 4.7e+00 5.6e+00
## [4078] 9.4e+00 2.2e+01 4.0e+00 3.2e+00 8.1e+00 1.8e+01 5.7e+01 3.7e+00 3.3e+01
## [4087] 9.7e+00 2.6e+00 3.2e+00 8.6e+00 3.2e+01 2.7e+00 6.2e+00 3.4e+01 1.4e+01
## [4096] 1.4e+00 2.3e+00 9.8e+00 3.3e+01 3.8e+00 1.4e+01 5.5e+00 4.6e+00 2.1e+01
## [4105] 3.1e+01 2.0e+01 3.4e+00 2.6e+00 9.1e+00 1.8e+01 2.5e+01 4.3e+00 6.8e+00
## [4114] 4.4e+00 9.3e+00 2.9e+00 4.5e-01 2.6e+01 1.5e+01 2.3e+01 3.2e-01 1.2e+01
## [4123] 1.6e+00 2.2e+01 2.4e+01 2.3e+00 2.3e+01 2.0e+01 1.5e+01 7.7e+00 1.2e+01
## [4132] 4.6e+01 5.8e+00 4.6e+00 8.5e+00 6.9e-01 5.8e+00 1.9e+01 1.8e+01 1.3e+01
## [4141] 6.3e+00 6.7e+00 6.5e+00 3.6e+01 1.1e+01 1.2e+01 6.1e+00 3.7e+00 3.3e+01
## [4150] 2.3e+00 2.9e+00 3.5e+00 7.6e+00 3.9e+01 6.2e+00 1.7e+00 2.6e+00 9.0e+00
## [4159] 1.5e+01 2.4e+01 6.4e-02 6.6e-01 4.7e+00 1.0e+00 2.0e+01 2.4e+01 6.7e+00
## [4168] 1.9e-01 5.3e+00 6.9e+00 1.4e+01 1.2e+01 9.2e+00 1.8e+01 5.4e+01 6.1e+01
## [4177] 2.6e+00 1.3e+01 3.3e+00 2.4e+00 2.9e+01 1.1e+01 5.8e+01 6.4e+00 2.0e+01
## [4186] 1.8e+01 1.1e+01 4.3e+00 1.3e+01 5.2e-01 2.6e+00 4.2e+01 5.1e+00 5.4e+00
## [4195] 1.7e+01 3.0e+00 9.9e+00 6.1e+01 7.2e+00 3.5e+00 2.4e+01 2.0e+01 3.8e+00
## [4204] 8.0e+00 5.7e+00 2.0e+01 7.7e+00 2.3e+00 3.5e+00 7.1e-02 1.0e+01 5.0e+00
## [4213] 1.9e+01 1.1e+01 3.5e+00 8.0e-01 2.0e+01 1.5e+00 1.6e+00 2.0e+01 1.9e+01
## [4222] 2.4e+01 6.3e+00 9.3e+00 9.2e+00 8.8e-01 1.7e+00 3.9e+00 2.1e+01 7.6e+00
## [4231] 2.0e+00 6.6e+00 1.1e+01 6.8e+00 4.8e+00 5.4e+00 2.0e+01 1.5e+01 1.2e+01
## [4240] 2.9e+00 1.4e+00 1.3e+01 3.7e+00 2.7e+00 8.0e+00 4.3e+00 4.2e+00 7.1e+00
## [4249] 4.1e+00 4.4e-01 8.1e+00 1.6e+01 1.6e+01 1.3e+01 3.3e+01 2.4e+01 7.8e+00
## [4258] 1.6e+01 3.6e+01 9.1e-01 5.2e+00 3.1e+01 4.7e+00 1.5e-01 1.7e+01 5.3e+00
## [4267] 1.3e+01 2.7e+00 2.6e+00 8.9e+00 1.1e+00 4.6e+00 2.7e+00 1.1e+00 9.4e+00
## [4276] 8.3e+00 5.3e-02 6.5e+00 5.6e+00 3.2e+01 1.6e+01 4.0e+01 1.7e+01 2.0e+01
## [4285] 8.6e+00 2.2e+01 1.3e+00 6.9e+00 1.1e+00 8.7e+00 4.0e+00 7.9e+00 2.5e+00
## [4294] 8.5e+00 2.9e+01 2.7e+01 2.2e+00 1.1e+00 1.0e+01 4.4e+00 1.4e+01 2.7e+01
## [4303] 5.2e+00 1.7e+01 1.1e+01 2.2e+01 1.5e+01 4.7e+00 3.0e+00 2.3e+01 7.3e-01
## [4312] 1.3e-01 1.8e+01 7.1e+00 9.1e+00 5.0e+01 1.8e+00 4.6e+00 3.4e+01 5.7e+00
## [4321] 3.0e+00 1.0e+01 1.8e+01 1.3e+01 8.8e-01 1.1e+01 1.3e+01 5.6e+00 6.5e+00
## [4330] 1.4e+00 7.1e+00 3.8e+00 2.3e+00 4.4e+01 1.2e+01 1.4e+01 8.3e+00 8.4e+00
## [4339] 7.3e+00 3.6e+01 3.7e+00 4.0e+00 8.6e+00 2.6e+01 8.8e+00 5.2e+00 2.3e+01
## [4348] 7.0e+00 2.6e+01 8.0e+00 2.1e+00 7.1e-01 3.0e+01 3.0e+00 2.1e+01 1.2e+00
## [4357] 1.8e+01 2.1e+01 2.0e+01 3.5e+01 4.1e+01 2.7e+00 1.3e+01 1.2e+01 6.8e+00
## [4366] 2.9e+01 3.3e+01 2.6e+01 5.3e+00 3.4e+00 8.4e+00 2.7e+00 1.5e+01 1.3e+01
## [4375] 8.6e-01 2.8e+01 6.3e+00 4.7e+01 4.9e+01 7.7e+00 5.1e+00 1.7e+00 1.7e+00
## [4384] 8.2e+00 7.1e+00 4.8e+01 1.7e+01 9.0e-02 1.6e+00 1.2e+01 2.7e+00 2.3e+01
## [4393] 8.3e+00 4.7e+00 7.2e+00 2.7e+01 2.2e+01 6.7e+00 1.1e+01 1.1e+01 1.4e+01
## [4402] 2.0e+00 8.7e+00 3.9e+00 1.8e+00 6.4e+00 5.4e+01 9.3e+00 4.8e+00 4.4e+00
## [4411] 3.9e+01 1.0e+00 2.9e+01 2.8e+01 3.2e-01 4.4e-01 6.8e+00 1.0e+00 1.9e+01
## [4420] 1.3e+01 5.1e+00 4.0e+00 4.2e+01 8.3e-01 1.1e+01 4.5e+01 9.1e+00 3.5e+00
## [4429] 9.4e+00 2.5e+00 2.6e+00 3.9e-01 6.7e+00 5.4e+00 2.1e+01 2.9e+01 1.1e+00
## [4438] 6.7e+00 4.0e+00 5.7e+00 3.6e+00 2.0e+01 3.4e+00 2.1e+01 1.7e-01 3.1e+00
## [4447] 8.7e+00 1.3e+01 8.7e+00 5.5e+01 7.3e+00 1.5e+01 8.9e+00 1.9e+01 4.2e+01
## [4456] 8.3e+00 9.4e+00 3.0e+00 1.7e+01 3.3e+00 5.4e+00 9.7e+00 5.6e+00 1.5e+01
## [4465] 2.9e+01 2.3e+00 1.1e+01 1.6e+01 2.9e+01 3.2e+00 1.1e+01 1.5e+01 5.2e+00
## [4474] 3.4e+01 3.2e+00 1.6e+01 8.7e+00 1.7e+01 1.0e+00 2.1e+00 6.6e+00 1.7e+01
## [4483] 9.7e-01 9.5e+00 1.4e+01 3.7e+01 6.5e+00 2.0e+01 2.1e+01 1.1e+01 1.0e+01
## [4492] 5.6e-01 1.1e+01 3.2e+01 8.2e+00 1.1e+00 1.2e+00 1.3e+01 1.7e+01 6.2e+00
## [4501] 8.5e+00 1.2e+01 5.2e+00 2.2e+00 7.7e+00 3.8e+01 7.7e+00 4.3e+01 5.4e+00
## [4510] 2.7e+00 8.7e+00 1.6e+01 1.4e+01 3.7e+00 1.7e+01 4.6e+00 7.1e+00 3.1e+00
## [4519] 5.0e+01 2.5e+01 7.7e-01 7.4e-01 3.1e+00 3.6e+01 3.5e+00 5.0e+00 1.0e+00
## [4528] 1.7e+01 5.6e+00 4.3e+00 3.1e+01 1.7e+01 5.3e+01 4.0e+00 6.0e+00 3.8e+01
## [4537] 3.2e+01 1.3e+00 6.6e+00 2.4e+01 1.4e+01 4.9e+00 2.2e-01 8.5e+00 7.4e+00
## [4546] 1.4e+01 3.5e+01 4.5e+00 9.8e+00 1.8e+01 6.6e+00 2.1e+01 1.7e+01 1.4e+01
## [4555] 4.4e+01 7.2e-01 3.3e+00 9.7e+00 1.8e+01 9.2e+00 2.0e+00 1.7e-01 1.3e+00
## [4564] 1.6e+01 7.8e+00 2.7e+00 2.9e+00 5.2e+00 9.0e-01 9.7e+00 5.8e-01 1.2e+00
## [4573] 4.2e+01 1.1e+01 1.7e+01 1.2e+01 2.9e+00 1.9e+00 2.9e+01 8.5e+00 3.1e+01
## [4582] 2.6e+01 1.4e+00 1.4e+01 1.7e+01 6.4e+00 1.9e+00 2.8e-01 8.7e+00 7.7e+00
## [4591] 2.2e+01 9.2e+00 2.2e+01 6.3e+00 1.3e+01 2.2e+01 2.2e+00 8.8e+00 8.1e+00
## [4600] 3.3e+01 7.0e+00 3.7e+00 3.7e+01 1.9e+01 1.3e+01 4.7e+00 2.1e+01 5.6e+00
## [4609] 1.5e+01 1.4e+01 7.8e+00 1.3e+01 1.1e+01 8.2e+00 1.8e+00 5.0e+00 3.0e+01
## [4618] 8.5e+00 4.2e+01 3.4e+00 1.6e+01 1.7e+01 2.1e+01 1.4e+01 2.0e+01 5.5e+00
## [4627] 4.0e-01 6.3e+00 7.2e-01 3.2e+00 1.1e+01 4.2e+00 6.2e+00 6.2e+00 1.4e+01
## [4636] 9.4e+00 1.0e+01 2.3e+01 3.0e+00 7.1e+00 1.4e-01 2.7e+01 1.6e+01 2.9e+01
## [4645] 4.1e+00 3.7e+00 1.1e+01 4.4e-01 3.5e+00 1.5e+01 3.0e+00 1.6e+01 1.6e+01
## [4654] 5.2e+00 5.3e+00 2.6e+00 2.9e+01 8.2e-01 4.2e+00 3.1e+01 6.6e+00 3.8e+00
## [4663] 4.3e+01 3.0e+01 3.1e+01 1.7e+01 2.1e+01 7.9e+00 2.6e+01 2.6e+01 5.8e+00
## [4672] 3.9e+00 2.3e+01 5.2e+00 3.1e+01 2.3e+00 1.3e+01 1.3e+01 2.0e+01 2.4e-01
## [4681] 5.3e+00 5.3e+00 3.3e+01 4.5e+00 3.3e+00 3.8e+01 2.8e+00 8.4e-01 1.3e+00
## [4690] 4.6e+00 1.9e-01 6.1e+00 2.8e-01 7.6e+00 4.9e+00 1.5e+01 2.1e+00 4.8e+00
## [4699] 1.9e+01 3.1e+01 1.2e+01 1.9e+01 1.7e+01 3.4e+01 1.0e+01 8.5e+00 1.2e+00
## [4708] 1.5e+01 3.0e+00 1.7e+00 4.7e+00 8.5e-01 9.7e+00 2.7e+00 4.0e+01 4.2e+00
## [4717] 4.7e+01 3.5e+00 3.3e+00 4.8e+00 4.3e+00 7.7e+00 3.3e+00 7.4e+00 8.0e+00
## [4726] 1.3e+00 8.5e+00 4.4e+00 3.5e+01 2.0e+01 3.4e+00 1.1e+00 6.5e+00 5.6e+00
## [4735] 8.7e+00 2.4e+01 1.3e+00 5.3e+00 6.3e+00 3.7e+01 3.9e+00 9.4e+00 2.0e+01
## [4744] 1.6e+00 2.1e+00 1.0e+01 3.9e+01 1.7e+01 2.7e+00 1.7e+01 1.1e+01 6.9e-02
## [4753] 3.2e+01 3.5e-01 7.7e+00 6.7e-01 9.3e+00 1.7e+01 8.7e+00 1.4e+01 1.1e+01
## [4762] 1.3e+01 9.0e+00 9.3e+00 8.7e+00 3.2e+00 8.0e+00 6.3e+01 7.0e+00 1.3e+01
## [4771] 3.7e+00 2.5e+00 1.6e+01 4.2e+00 3.0e+01 2.1e+01 2.1e+01 3.1e+01 2.1e+01
## [4780] 1.8e+00 2.7e+01 7.4e-02 7.7e+00 2.8e+01 2.4e+01 3.9e+00 3.7e+01 1.9e+01
## [4789] 2.9e+01 4.3e+00 1.3e+01 9.6e-02 5.1e+01 5.7e+00 2.0e+01 2.4e+01 2.7e+00
## [4798] 6.3e+01 5.2e+00 4.7e+00 2.6e+01 4.5e+00 8.3e+00 1.1e+01 8.8e+00 1.4e+01
## [4807] 7.7e+00 4.3e+00 3.2e+00 1.6e+01 1.1e+01 2.2e+00 1.4e+01 4.7e-02 1.5e+01
## [4816] 2.2e-01 1.9e+01 1.2e+01 1.1e+00 2.2e+00 2.9e+00 9.9e-01 6.9e+00 4.0e-01
## [4825] 1.6e+00 5.9e+00 8.0e+00 3.1e+01 1.9e+01 3.7e+00 5.1e+01 5.4e+00 2.6e+01
## [4834] 2.9e+01 1.4e+01 1.6e+01 3.1e+01 1.6e+01 1.1e+01 5.6e+00 2.6e+01 2.6e+00
## [4843] 6.1e+00 3.2e+00 2.5e+00 3.7e+01 1.5e+00 6.6e+00 5.2e+01 3.3e+00 3.3e+01
## [4852] 1.6e+01 7.5e+00 8.3e+00 2.0e+01 1.9e+01 1.0e+01 2.0e+00 4.6e+00 3.3e+00
## [4861] 1.0e+01 3.0e+01 5.4e+00 1.3e+01 4.0e+00 1.4e+01 1.5e+01 6.9e+00 6.4e-01
## [4870] 4.4e+00 5.2e+00 7.3e+00 5.7e+00 2.9e+01 1.9e+01 6.9e+00 1.1e+01 1.1e+01
## [4879] 1.4e-01 1.2e+01 6.9e+00 8.4e+00 5.2e-02 2.2e+00 2.8e+00 2.0e+01 5.7e+00
## [4888] 1.6e+01 1.3e+00 1.6e+00 1.8e+01 4.1e+00 2.8e+01 5.0e+00 3.1e+01 1.8e+00
## [4897] 3.2e+00 4.3e+00 1.1e+01 7.0e+01 1.3e+01 2.3e+01 1.4e+01 3.0e+00 5.3e+00
## [4906] 4.4e+01 1.9e+01 6.4e+00 1.3e+00 6.8e+00 1.7e+00 5.3e+00 3.1e+00 1.5e+01
## [4915] 1.7e+00 1.6e+00 6.6e+00 3.0e+01 2.7e+00 4.2e+00 5.4e-01 1.4e+01 6.8e+00
## [4924] 2.1e+01 2.4e+00 3.7e+01 2.2e+01 2.1e+01 4.1e+00 4.0e+00 1.0e+01 3.0e+01
## [4933] 9.0e+00 5.3e+00 4.8e+00 6.3e+00 5.0e+01 4.0e+01 1.4e+01 2.3e+00 2.5e+01
## [4942] 1.9e+00 1.9e+01 2.2e+00 7.7e-01 1.1e+00 7.6e-01 5.2e-01 1.4e+01 7.9e+00
## [4951] 1.4e+01 1.4e+00 1.9e+00 2.2e+01 1.4e+01 7.5e+00 5.8e+00 5.2e+00 1.9e+01
## [4960] 1.9e+00 9.8e+00 2.5e+01 9.7e+00 2.1e+01 2.7e+00 1.2e+01 2.8e+01 7.8e+00
## [4969] 1.1e+01 2.7e+00 3.8e+01 3.2e-01 5.4e+01 4.4e+01 2.0e+01 9.3e+00 7.4e+00
## [4978] 5.0e+00 2.3e-01 4.5e+00 6.3e+00 1.3e+01 8.4e+00 3.4e+01 2.9e+01 1.2e+00
## [4987] 9.0e+00 1.5e+01 1.4e+01 2.7e+00 4.4e-01 4.5e+01 1.1e+01 3.1e+01 3.2e+01
## [4996] 2.0e+00 8.3e-01 2.2e-01 2.7e+00 1.4e+01## B)
hist(tiempos,main = "Tiempo entre llegadas",col="blue")
## 3.
## A)
atenciones<-rnorm(5000,30,5)
atenciones## [1] 31 30 39 29 20 36 41 32 24 27 24 27 34 31 26 33 22 26 27 29 38 34 31 23
## [25] 33 31 33 34 36 31 41 33 39 32 30 30 36 32 32 31 33 27 41 29 31 32 16 39
## [49] 30 42 33 32 20 25 23 23 29 32 31 33 24 34 30 26 37 32 29 25 26 22 31 28
## [73] 29 21 31 26 25 29 28 27 31 32 30 19 37 28 24 26 37 18 22 24 22 25 35 30
## [97] 38 29 31 30 26 30 31 24 30 29 29 34 30 28 40 33 29 34 41 31 29 25 33 35
## [121] 30 35 36 23 30 34 28 30 27 33 36 32 20 34 31 23 29 32 35 36 33 20 38 27
## [145] 27 38 32 36 31 26 32 31 29 30 33 26 38 29 26 31 27 27 29 27 41 23 23 29
## [169] 28 33 32 31 28 28 32 31 26 38 35 34 21 22 32 28 43 30 33 32 30 26 18 25
## [193] 22 31 33 28 26 30 33 20 31 31 34 29 24 43 25 25 31 30 25 31 27 35 30 29
## [217] 30 30 26 32 31 34 33 35 36 38 35 31 31 31 30 32 29 43 28 29 22 28 26 27
## [241] 21 35 24 23 27 27 31 32 24 26 24 26 23 25 34 32 31 32 30 30 22 30 23 32
## [265] 35 29 31 38 28 23 26 31 28 35 22 31 36 25 34 19 31 28 21 33 37 26 30 31
## [289] 32 36 28 33 25 28 27 28 25 24 37 22 38 31 24 31 40 23 32 37 22 26 27 31
## [313] 30 32 29 29 25 32 32 42 29 21 26 33 28 16 40 28 35 30 24 32 32 33 25 32
## [337] 38 38 29 25 29 16 24 29 28 31 24 23 32 30 34 24 28 28 26 32 19 30 28 35
## [361] 22 23 25 35 24 33 26 28 25 29 27 29 25 25 31 33 24 29 39 21 31 31 32 26
## [385] 31 38 19 33 25 33 35 33 27 36 28 31 26 29 26 27 24 31 35 29 29 28 35 23
## [409] 26 30 21 27 29 32 18 34 19 35 23 39 29 36 27 27 33 22 41 13 30 27 34 35
## [433] 37 32 31 36 31 37 27 28 26 33 28 35 37 23 26 35 30 29 28 32 21 29 41 37
## [457] 39 29 29 30 24 26 28 37 19 24 29 34 27 32 21 29 32 30 29 33 26 35 32 27
## [481] 25 38 30 31 27 29 21 26 21 29 24 22 20 27 29 29 36 28 34 23 23 28 28 33
## [505] 31 24 41 33 26 35 28 33 29 34 34 29 31 34 25 27 33 39 24 24 32 32 29 34
## [529] 31 22 27 26 32 36 31 26 29 36 31 30 27 28 23 31 31 27 26 25 35 26 30 31
## [553] 29 30 28 34 28 30 36 32 23 31 27 30 30 29 34 28 34 33 29 27 35 32 25 29
## [577] 35 30 28 34 23 19 32 41 34 21 26 30 29 27 25 30 22 26 29 29 34 28 33 34
## [601] 24 25 33 31 22 32 24 28 33 23 36 25 31 20 37 34 27 33 29 35 27 35 41 34
## [625] 20 42 29 33 29 26 36 36 32 30 28 25 31 25 43 30 21 39 19 37 33 27 23 25
## [649] 31 27 28 28 35 34 23 23 22 29 31 40 26 23 25 25 32 25 28 31 35 19 29 33
## [673] 28 31 35 34 29 30 28 22 27 36 32 30 37 29 25 32 29 30 40 36 35 40 32 27
## [697] 23 26 29 32 39 24 24 32 27 32 30 36 29 39 33 32 30 28 27 33 30 20 19 35
## [721] 30 30 32 27 34 34 41 45 33 31 32 27 34 26 24 41 30 34 33 25 25 32 27 24
## [745] 33 38 21 33 27 29 28 36 30 31 35 37 27 43 24 29 20 27 34 22 31 39 19 27
## [769] 32 28 43 36 30 24 26 25 32 24 38 23 34 34 30 30 36 26 24 21 29 28 33 33
## [793] 25 35 28 30 25 31 31 30 29 25 29 25 32 33 33 35 27 26 37 23 34 36 35 27
## [817] 32 31 28 37 33 29 29 32 27 40 25 31 35 34 34 33 25 31 34 31 30 31 31 31
## [841] 24 35 37 32 34 24 30 37 32 28 32 31 37 32 22 31 34 31 35 24 33 24 37 25
## [865] 31 41 26 30 31 21 34 27 30 32 36 24 28 31 22 30 32 35 43 35 26 25 26 30
## [889] 29 28 20 24 30 22 28 25 30 32 28 33 27 23 33 23 36 34 32 25 36 20 32 27
## [913] 28 29 27 36 30 41 29 25 31 33 26 29 36 24 28 30 30 31 30 26 28 30 34 29
## [937] 31 22 25 29 32 22 42 40 28 35 27 29 30 27 36 28 28 34 32 22 31 32 29 30
## [961] 31 32 26 28 35 32 29 28 34 25 33 39 31 26 30 40 31 40 27 23 30 23 38 29
## [985] 34 35 25 32 29 35 36 34 32 37 27 30 22 29 34 21 40 26 29 32 26 25 34 25
## [1009] 38 26 31 33 28 32 34 25 30 36 27 30 33 33 25 37 23 34 27 40 29 28 24 32
## [1033] 15 28 29 25 37 25 34 28 38 30 27 27 30 33 29 29 28 31 22 28 32 24 39 26
## [1057] 32 25 21 28 30 41 32 28 30 27 31 27 33 23 26 37 29 39 31 32 36 36 35 27
## [1081] 18 26 33 29 27 25 34 34 26 23 37 19 36 32 24 28 36 31 22 29 39 29 39 29
## [1105] 37 31 40 39 31 35 32 19 24 27 34 35 25 23 30 28 26 34 22 33 28 28 35 31
## [1129] 33 36 29 26 24 28 27 28 27 24 25 27 37 36 27 33 26 32 24 36 30 32 32 26
## [1153] 18 20 38 41 33 38 30 34 36 32 35 22 40 31 32 25 34 32 27 27 33 29 29 30
## [1177] 30 29 33 26 32 35 29 39 35 35 33 28 31 35 30 31 33 29 33 28 28 25 21 29
## [1201] 32 23 23 25 26 29 29 26 33 32 43 24 36 31 26 26 29 31 26 34 32 41 34 26
## [1225] 30 37 24 30 27 27 29 30 29 38 28 37 30 28 31 32 30 33 36 36 28 35 28 26
## [1249] 30 34 26 18 39 11 30 32 29 33 39 31 35 35 35 33 23 33 30 35 33 27 23 28
## [1273] 31 34 36 32 33 32 35 35 26 40 35 35 26 27 33 34 29 31 31 26 32 27 31 26
## [1297] 29 37 25 26 26 32 27 24 34 36 41 29 28 33 27 23 37 37 28 27 32 44 30 25
## [1321] 29 25 23 28 30 32 25 30 35 33 24 19 30 31 30 37 27 25 25 37 39 28 35 26
## [1345] 32 29 33 33 31 30 21 31 30 31 24 36 33 33 33 29 25 28 27 26 31 25 37 41
## [1369] 28 31 26 25 25 24 32 34 34 38 23 31 38 28 25 19 23 33 30 25 33 38 38 28
## [1393] 28 34 28 37 26 28 25 24 23 26 30 23 27 28 27 33 30 37 33 31 27 37 30 27
## [1417] 38 43 33 28 24 31 26 27 36 30 28 30 26 21 21 22 36 21 30 44 35 32 35 31
## [1441] 35 30 38 24 31 36 27 24 44 27 38 34 24 27 34 24 28 35 28 29 34 26 31 24
## [1465] 31 27 26 27 25 32 33 33 31 29 39 26 31 34 24 32 33 35 26 32 21 31 31 25
## [1489] 31 23 25 28 30 34 26 32 26 35 40 28 33 28 35 30 31 31 20 34 19 31 23 22
## [1513] 35 34 29 28 34 24 37 25 34 25 25 39 29 25 36 33 28 33 31 27 29 42 32 15
## [1537] 30 27 22 34 36 34 30 21 24 31 36 37 30 27 27 38 38 30 31 35 25 34 23 30
## [1561] 27 27 29 38 37 37 31 27 23 28 37 31 31 26 37 29 40 39 30 35 28 25 41 34
## [1585] 31 28 24 32 29 33 33 32 36 30 35 19 28 38 28 36 32 23 35 34 33 21 27 32
## [1609] 32 33 26 23 34 32 24 28 33 26 31 26 25 33 24 36 24 26 21 30 32 31 30 31
## [1633] 24 28 29 28 35 40 42 35 33 31 32 29 38 35 26 33 23 37 38 30 27 24 37 35
## [1657] 41 33 31 27 22 25 32 27 32 29 22 30 27 38 33 41 29 33 32 34 24 24 30 28
## [1681] 35 24 21 40 28 27 36 28 29 25 30 23 27 30 16 27 34 28 35 34 34 33 21 38
## [1705] 30 20 24 39 34 33 24 30 30 35 27 34 31 34 23 26 29 28 30 29 36 30 29 26
## [1729] 31 30 26 24 32 30 26 31 27 34 29 30 29 30 36 24 42 36 36 32 34 27 36 29
## [1753] 30 39 32 28 23 29 22 29 36 27 27 23 25 29 30 32 23 25 32 31 29 43 37 27
## [1777] 32 26 24 29 27 30 33 33 41 30 37 37 33 27 36 26 40 31 26 36 27 32 32 28
## [1801] 30 35 26 32 25 26 37 38 28 26 28 27 34 25 37 26 27 34 23 25 28 27 27 31
## [1825] 30 31 29 39 28 23 25 46 28 31 33 26 37 32 33 31 27 26 33 36 31 33 41 30
## [1849] 35 31 20 28 36 28 28 27 34 28 42 29 33 28 38 30 34 29 36 29 42 35 32 31
## [1873] 35 20 34 29 25 37 26 34 25 32 34 29 31 39 24 31 39 34 14 25 28 38 29 22
## [1897] 32 34 30 25 32 26 32 31 28 18 33 32 25 35 19 29 26 22 34 34 35 24 31 27
## [1921] 21 30 32 30 34 25 26 25 36 33 31 38 36 31 28 27 27 30 26 25 29 27 31 36
## [1945] 26 36 22 35 28 19 41 17 34 30 29 29 23 28 23 30 25 35 37 31 34 36 25 34
## [1969] 17 25 24 37 40 25 32 28 26 25 35 27 28 27 31 39 19 24 28 22 23 27 37 33
## [1993] 24 32 30 27 31 34 34 23 23 22 33 39 29 30 37 27 31 31 24 28 25 37 43 26
## [2017] 29 30 30 28 34 27 25 28 34 28 23 35 29 38 27 22 29 38 14 32 20 22 33 29
## [2041] 36 25 37 23 36 37 33 29 24 33 38 34 27 37 33 29 28 36 32 29 33 31 30 28
## [2065] 28 42 22 24 35 31 41 27 39 24 38 30 41 35 33 37 30 18 30 28 26 26 35 34
## [2089] 29 28 32 35 38 23 28 28 31 32 25 22 34 28 31 35 31 29 28 34 22 35 26 28
## [2113] 28 30 25 32 32 26 26 29 33 28 33 30 26 23 26 31 21 25 27 34 33 29 33 23
## [2137] 40 33 45 31 31 32 35 29 37 37 29 37 30 22 40 23 27 25 27 44 24 39 33 36
## [2161] 39 35 28 38 25 33 33 28 30 26 26 27 23 38 23 32 37 36 31 33 37 27 29 31
## [2185] 26 32 31 32 39 31 21 32 27 28 28 35 31 34 29 26 29 29 24 28 34 32 33 33
## [2209] 28 25 23 28 32 25 27 26 37 38 29 35 25 22 29 37 28 37 34 28 37 33 25 28
## [2233] 28 36 32 27 37 26 31 34 21 17 32 28 25 37 35 33 35 27 28 26 41 31 33 30
## [2257] 31 34 28 27 34 26 29 36 26 29 24 35 31 30 26 27 30 30 29 28 29 30 25 29
## [2281] 23 31 19 35 29 32 30 22 30 35 30 30 26 30 23 21 32 32 31 30 30 23 25 23
## [2305] 24 29 28 33 31 23 34 24 28 29 26 31 37 33 29 29 27 34 32 25 16 37 39 33
## [2329] 33 25 26 31 34 26 28 27 36 31 28 28 27 27 20 18 29 35 41 36 35 26 28 28
## [2353] 36 24 27 32 22 31 28 28 31 23 31 39 33 32 39 35 36 28 27 31 30 34 39 27
## [2377] 26 26 28 32 35 26 26 29 28 31 28 30 27 37 35 33 35 34 33 22 16 35 14 26
## [2401] 31 38 29 22 34 21 33 32 34 26 36 23 30 38 33 38 22 33 30 30 26 26 31 31
## [2425] 36 29 27 23 35 36 30 41 31 25 23 34 29 37 27 28 27 21 27 24 31 27 35 31
## [2449] 30 29 32 36 30 34 21 25 19 21 31 36 28 28 31 30 37 35 36 30 33 29 33 34
## [2473] 36 33 30 30 33 31 27 34 25 37 36 41 26 36 25 45 17 32 24 27 30 21 25 37
## [2497] 23 25 24 28 36 25 28 29 35 24 29 22 18 27 25 29 31 31 35 35 31 27 34 34
## [2521] 37 36 26 39 38 32 24 37 34 25 34 23 23 31 30 33 28 34 39 26 26 30 23 31
## [2545] 30 33 28 29 29 25 24 31 31 28 25 35 30 37 24 31 31 29 42 26 37 26 34 23
## [2569] 23 39 30 28 35 30 31 25 19 19 41 40 31 30 31 35 36 28 27 30 31 41 33 31
## [2593] 20 25 30 32 26 28 32 31 23 22 27 33 23 34 41 26 34 25 27 31 40 40 31 28
## [2617] 23 25 38 23 30 34 24 28 32 29 23 30 24 37 38 30 29 27 31 38 34 33 33 37
## [2641] 28 33 34 26 25 29 26 32 39 29 26 33 31 32 33 30 36 33 28 30 24 28 33 35
## [2665] 38 32 28 33 30 30 17 31 33 22 16 32 33 34 34 31 40 20 40 25 26 31 27 29
## [2689] 35 40 33 36 35 26 35 33 26 35 29 23 25 29 17 35 23 27 27 24 30 31 27 28
## [2713] 26 28 33 32 28 35 23 26 29 27 29 27 29 31 29 37 32 38 32 34 36 34 30 24
## [2737] 28 33 35 31 35 39 33 26 38 28 32 31 32 23 30 33 36 31 39 35 31 25 32 31
## [2761] 29 31 32 31 25 31 29 30 27 30 22 35 24 26 32 28 28 31 32 28 26 31 33 26
## [2785] 29 33 27 36 30 34 35 29 32 25 27 34 29 30 27 32 32 26 43 30 32 28 33 31
## [2809] 34 27 29 24 25 31 32 27 35 29 30 25 31 27 25 30 28 27 28 32 23 32 28 30
## [2833] 23 28 34 36 30 36 36 31 33 23 22 27 38 33 28 34 34 36 38 16 32 24 35 34
## [2857] 29 33 30 27 29 27 28 33 34 31 32 27 26 27 34 18 31 35 31 37 24 29 21 21
## [2881] 30 31 24 28 29 30 33 31 21 31 36 30 38 24 29 30 35 27 39 29 28 21 36 26
## [2905] 32 21 34 28 26 24 28 33 35 34 28 32 28 29 31 29 34 34 28 28 26 23 35 31
## [2929] 35 20 33 36 28 29 25 29 34 29 33 32 31 39 38 30 33 34 22 24 32 31 29 25
## [2953] 34 23 31 34 34 36 23 24 26 25 35 37 41 33 27 30 29 32 30 29 34 21 21 36
## [2977] 32 22 36 25 23 26 35 35 32 18 27 36 35 35 26 22 28 35 20 29 20 28 37 23
## [3001] 40 21 32 35 32 30 31 30 28 34 34 26 26 36 30 30 33 28 36 28 35 42 31 31
## [3025] 19 26 31 35 30 25 36 26 36 32 27 26 30 28 36 28 32 28 32 29 23 30 22 32
## [3049] 35 38 24 28 36 27 36 34 32 34 26 23 26 22 26 30 31 38 27 34 29 38 37 31
## [3073] 23 29 30 22 31 30 30 19 32 28 39 29 22 24 28 33 39 25 30 26 35 30 20 37
## [3097] 34 37 29 32 37 32 32 31 24 20 32 37 36 20 36 29 35 29 33 36 34 33 32 33
## [3121] 25 35 31 31 25 30 28 31 29 21 39 38 25 33 24 31 24 29 36 38 28 38 32 29
## [3145] 25 24 29 36 24 30 35 26 28 30 35 34 31 37 29 22 25 29 31 30 32 25 31 25
## [3169] 37 35 29 39 33 33 35 25 34 37 36 24 34 38 32 36 39 22 28 27 23 29 32 39
## [3193] 35 21 31 27 31 30 36 38 28 38 27 25 17 23 36 37 19 28 29 25 26 20 40 31
## [3217] 23 28 39 26 27 39 40 27 29 24 37 22 28 23 31 32 28 30 29 25 34 32 31 21
## [3241] 31 30 27 30 34 30 31 39 32 27 24 20 27 32 18 36 32 37 26 29 35 37 25 33
## [3265] 34 30 18 30 24 33 33 35 27 29 31 26 22 27 34 29 36 31 34 34 28 27 29 26
## [3289] 35 30 22 30 31 31 44 29 40 28 35 35 26 38 26 29 27 26 34 30 35 33 35 38
## [3313] 31 29 37 27 30 32 33 40 31 28 29 29 33 26 31 32 29 23 35 41 30 25 32 27
## [3337] 35 32 31 34 30 28 29 28 37 29 32 26 30 31 31 35 31 29 32 38 32 32 30 34
## [3361] 34 23 28 30 30 31 25 29 34 37 26 33 33 22 37 32 31 39 26 27 28 28 34 31
## [3385] 30 32 24 31 31 33 30 31 25 37 35 35 27 30 33 30 32 36 32 31 32 32 38 25
## [3409] 33 32 32 34 23 30 37 31 37 33 38 32 30 35 28 30 28 26 32 31 31 30 28 32
## [3433] 23 23 32 30 26 32 40 22 32 29 21 34 33 27 29 22 34 34 33 25 28 17 33 35
## [3457] 27 29 31 35 30 21 38 29 24 32 23 34 36 30 20 28 29 31 29 27 37 18 33 19
## [3481] 28 26 24 30 23 38 35 42 32 29 35 30 35 36 30 29 32 31 29 21 27 26 23 32
## [3505] 29 23 29 30 30 31 23 30 40 34 28 33 32 28 25 32 30 22 27 36 28 27 32 28
## [3529] 35 38 32 31 32 30 30 34 20 43 35 37 24 35 32 30 33 30 31 28 37 25 31 32
## [3553] 28 31 37 36 30 29 36 31 25 27 40 33 34 27 28 29 27 32 27 34 28 30 28 29
## [3577] 32 31 36 30 33 29 32 28 36 24 31 30 27 29 22 32 32 37 36 30 20 28 38 28
## [3601] 40 23 33 18 43 28 30 32 48 29 36 32 35 31 36 32 37 31 22 19 26 31 39 27
## [3625] 40 29 25 21 33 30 22 40 26 26 25 30 38 30 35 34 28 30 23 30 31 37 18 27
## [3649] 24 32 24 31 43 28 28 35 27 40 16 32 30 31 31 25 31 29 38 24 34 25 30 37
## [3673] 25 30 30 24 40 33 34 29 26 29 34 34 28 36 33 33 34 27 27 33 24 30 28 34
## [3697] 30 38 32 22 25 28 38 26 31 37 29 31 31 31 28 35 30 32 29 37 40 30 43 31
## [3721] 23 36 25 24 24 37 33 36 27 21 35 28 24 32 26 33 30 31 35 30 28 39 22 35
## [3745] 32 29 27 36 26 30 33 37 33 23 32 26 34 35 31 24 33 34 32 31 39 28 28 28
## [3769] 30 27 28 36 30 30 30 31 27 36 30 33 31 25 33 33 32 29 36 29 33 29 34 28
## [3793] 30 32 28 31 31 34 30 34 31 29 25 33 28 33 36 35 30 30 32 41 26 28 36 26
## [3817] 27 29 25 33 24 31 31 35 31 32 34 30 32 36 22 21 31 25 41 29 29 33 33 22
## [3841] 26 30 32 38 39 24 30 31 37 25 33 30 33 34 27 27 33 29 30 19 32 35 26 36
## [3865] 43 34 38 30 26 26 25 26 38 25 26 34 32 32 17 28 27 29 19 28 32 24 34 31
## [3889] 27 33 23 33 28 31 32 31 25 36 26 30 30 32 27 28 30 28 29 39 28 17 19 32
## [3913] 30 29 30 34 40 25 33 19 31 37 30 21 21 36 31 28 37 36 22 33 33 24 34 28
## [3937] 28 29 25 28 42 30 40 22 34 27 37 31 22 24 38 36 35 26 31 28 38 35 44 26
## [3961] 34 33 29 37 26 29 33 34 26 35 38 37 19 35 29 28 34 27 27 35 28 27 34 29
## [3985] 31 31 37 30 41 26 36 38 33 37 32 37 30 27 25 31 44 27 27 29 28 32 34 31
## [4009] 29 28 31 31 40 28 22 38 30 37 32 32 27 21 28 38 34 29 36 35 30 31 31 30
## [4033] 22 31 25 31 28 28 21 31 29 40 29 31 40 26 19 27 25 27 30 24 35 32 32 34
## [4057] 29 31 39 33 32 21 26 36 37 34 37 31 27 22 35 29 29 32 28 37 31 22 20 29
## [4081] 30 30 22 27 27 27 39 30 33 26 34 35 35 34 41 34 36 34 27 30 29 32 30 17
## [4105] 33 23 34 27 27 27 22 34 20 20 33 29 28 27 25 32 27 25 30 24 28 24 33 30
## [4129] 30 29 37 25 34 31 37 27 33 29 30 27 28 30 28 29 33 31 30 36 31 24 40 38
## [4153] 37 25 30 17 23 21 26 31 28 39 22 34 28 21 27 33 31 28 29 30 27 31 23 26
## [4177] 28 22 29 38 31 31 31 27 28 37 26 20 36 28 23 23 35 28 29 41 23 37 29 34
## [4201] 29 35 28 23 30 34 26 22 25 32 36 32 38 27 31 31 34 37 43 36 24 24 28 31
## [4225] 32 26 42 34 32 36 32 34 31 22 38 30 25 28 20 26 31 35 21 20 22 23 36 27
## [4249] 31 30 30 27 35 29 37 25 29 24 30 31 23 39 34 34 27 31 34 29 33 29 31 30
## [4273] 31 35 30 31 36 34 29 31 27 25 31 27 27 25 31 36 27 26 38 34 31 37 31 31
## [4297] 22 32 25 27 28 36 35 39 31 33 31 36 31 35 33 31 31 26 34 30 32 35 25 28
## [4321] 29 28 26 29 19 25 33 26 30 34 34 22 31 27 29 32 29 33 29 36 32 30 27 33
## [4345] 36 27 35 18 20 43 30 32 39 27 36 32 27 42 18 28 27 27 30 33 29 28 25 27
## [4369] 29 26 33 36 28 30 30 32 26 34 29 40 31 24 33 29 35 34 34 30 28 37 20 33
## [4393] 37 28 34 23 34 29 33 29 30 33 27 40 19 35 29 30 27 23 34 32 23 34 17 29
## [4417] 28 19 25 24 36 25 27 26 30 19 33 23 27 36 26 31 33 30 38 27 36 26 23 30
## [4441] 36 37 31 23 38 28 34 32 24 34 31 34 34 23 34 44 37 32 30 27 43 31 37 27
## [4465] 30 23 32 26 24 32 35 26 30 22 31 42 30 28 26 37 27 32 28 26 30 34 26 36
## [4489] 29 29 27 32 22 35 26 29 24 30 36 31 33 31 30 25 32 29 29 35 33 34 30 26
## [4513] 23 27 32 36 29 34 26 33 27 36 21 33 30 29 42 27 35 34 32 27 38 30 38 32
## [4537] 20 42 22 28 26 33 37 36 36 22 22 30 31 37 29 34 28 33 35 34 27 33 29 34
## [4561] 38 30 32 23 34 35 30 37 38 35 32 27 31 31 31 30 27 30 22 23 28 31 33 23
## [4585] 35 26 29 39 26 26 31 26 32 19 31 23 35 27 38 24 30 26 28 32 20 35 31 29
## [4609] 33 28 12 24 35 33 28 26 22 33 34 32 36 27 28 25 35 37 31 32 29 24 28 30
## [4633] 33 33 31 29 35 23 38 32 35 36 28 21 29 15 28 29 35 29 28 29 33 31 23 37
## [4657] 29 37 31 36 31 28 33 34 29 32 26 23 31 25 33 35 23 30 33 41 32 31 20 37
## [4681] 31 36 22 28 36 39 34 26 33 26 26 33 29 36 33 31 32 30 32 34 32 26 25 35
## [4705] 34 29 35 34 31 25 26 31 30 33 29 32 29 23 27 21 32 20 32 32 30 22 25 31
## [4729] 38 35 31 28 32 29 32 36 27 26 35 36 28 36 34 22 21 25 24 38 28 27 27 33
## [4753] 25 31 32 19 30 31 16 35 31 25 29 33 36 39 31 29 30 39 27 29 39 34 34 32
## [4777] 24 24 37 31 35 29 24 24 29 37 28 33 26 35 30 21 39 31 32 33 27 34 33 28
## [4801] 33 37 35 26 28 21 33 30 28 25 27 22 34 33 25 33 28 29 34 31 24 35 28 33
## [4825] 25 32 37 29 35 22 24 31 32 35 26 20 27 31 30 28 34 31 34 28 30 25 37 26
## [4849] 31 31 27 33 29 38 29 35 23 28 40 26 31 33 27 34 26 34 26 26 37 29 23 17
## [4873] 30 22 33 32 25 25 38 29 23 30 39 26 29 29 35 28 23 28 24 26 22 31 35 31
## [4897] 26 32 39 35 35 32 34 23 30 45 20 19 29 27 32 29 26 26 32 29 27 31 31 30
## [4921] 29 30 30 27 34 25 30 33 22 34 31 30 28 22 26 31 34 30 29 32 38 28 36 31
## [4945] 34 31 21 29 27 26 37 36 21 32 33 35 26 28 23 32 31 30 34 29 28 27 31 30
## [4969] 26 20 33 35 31 27 25 27 31 20 30 37 38 26 31 27 28 29 36 40 35 29 27 32
## [4993] 25 32 30 26 29 20 32 16## B)
hist(atenciones, breaks = 50, prob = TRUE,
main = "histograma con la curva normal teórica",
xlab = "Duración (minutos)", ylab = "Densidad",
col = "green", border = "yellow")
curve(dnorm(x, mean = 30, sd = 5),
col = "purple", lwd = 2, add = TRUE)
lines(density(atenciones), col = "white", lwd = 2, lty = 2)
legend("topright", legend = c("Normal Teórica", "Densidad Empírica"),
col = c("black", "darkgreen"), lwd = 2, lty = c(1,2), bty = "n")
## 1)
set.seed(0525)
fallas <- rpois(150,3)
media_fallas <- mean(fallas)
desv_fallas <- sd(fallas)
media_fallas## [1] 3.1desv_fallas## [1] 1.9## 2)
set.seed(0525)
vida <- rexp(1000, 1/500)
prob_mas700 <- mean(700)
prob_mas700## [1] 700## 3)
set.seed(0525)
defectuosos <- rbinom(100,50,0.05)
prom_def <- mean(defectuosos)
prom_def## [1] 2.6## 4)
set.seed(0525)
demanda <- rnorm(365,100,15)
prob_sup130 <- mean(130)
hist(demanda, breaks = 20, col = "blue", main="Demanda de energía diaria",
xlab="MW", probability = TRUE)
curve(dnorm(x, mean=100, sd=15), add=TRUE, col="green", lwd=2)
prob_sup130## [1] 130## 5)
# A)
set.seed(0525)
u <- runif(1000)
vida_cap <- -1000 * log(1 - u)
# B)
media_cap <- mean(vida_cap)
var_cap <- var(vida_cap)
media_cap## [1] 1011var_cap## [1] 1093379# C)
hist(vida_cap, breaks=30, probability=TRUE, col="gold",
main="Tiempo de vida de capacitores", xlab="Horas")
curve(dexp(x, rate=1/1000), add=TRUE, col="yellow", lwd=2)
# D)
prob_menor940 <- mean(vida_cap < 940)
prob_menor940## [1] 0.61