(1). Un sistema de producción tiene fallas según un proceso de Poisson con una tasa de 3 fallas por día. Simular el número de fallas en un semestre (150 días) y calcular la media y desviación estándar.
n <- 150
tasa <- 3
poisson <- rpois(n, tasa)
poisson
## [1] 1 3 5 4 3 4 4 2 4 0 1 4 4 3 2 2 3 4 2 4 5 4 2 4 4 3 7 3 3 2 2 1 5 2 3 1 0
## [38] 5 1 2 4 0 1 1 1 3 3 1 2 4 3 6 5 4 6 2 6 3 5 4 3 2 2 2 4 5 3 4 2 3 2 1 3 1
## [75] 3 2 1 1 6 1 4 2 6 3 3 2 5 1 3 3 5 2 2 1 0 6 5 7 5 4 5 0 3 5 2 1 0 5 2 8 3
## [112] 2 1 3 1 4 2 2 3 0 2 2 0 2 3 4 4 0 0 4 1 1 1 3 1 8 2 1 4 3 3 2 7 3 2 3 2 3
## [149] 2 4
media <- mean(poisson)
desv<- sd(poisson)
media
## [1] 2.873333
desv
## [1] 1.738999
(2). La vida útil (en horas) de un componente electrónico sigue una distribución exponencial con un promedio de 500 horas. Simular 1000 componentes y estimar la probabilidad de que un componente dure más de 700 horas.
n <- 1000
media <- 500
exponencial <- rexp(n, 1/media)
round(exponencial,1)
## [1] 429.7 619.8 1461.6 75.3 467.5 583.4 45.4 53.9 484.7 322.1
## [11] 129.6 522.7 1143.4 125.4 9.7 149.3 385.6 248.8 1643.5 186.3
## [21] 6.6 646.3 30.8 47.8 178.1 134.2 461.7 386.3 140.2 91.1
## [31] 472.7 152.0 459.3 71.4 181.7 394.2 1847.6 271.2 1046.6 163.5
## [41] 739.0 1097.5 143.9 68.5 628.4 907.8 363.0 542.3 663.1 18.1
## [51] 70.9 1532.9 861.3 274.1 814.6 979.9 479.3 127.6 315.2 191.4
## [61] 461.3 515.2 18.3 181.0 438.9 169.1 507.5 467.7 396.1 1793.9
## [71] 246.3 31.6 379.2 12.0 257.7 252.1 332.3 88.2 496.3 539.5
## [81] 384.2 408.7 507.3 27.0 976.1 565.3 239.7 418.4 1018.4 591.5
## [91] 1157.4 60.0 227.2 113.2 96.6 28.7 809.9 106.5 2181.4 92.1
## [101] 209.7 1076.4 437.7 167.2 1201.9 7.5 415.3 25.2 524.2 290.7
## [111] 754.3 169.2 326.9 104.7 32.7 71.3 31.1 1279.8 169.6 438.3
## [121] 660.9 616.8 204.7 1040.0 1057.3 215.8 411.6 130.7 155.4 99.0
## [131] 220.8 290.4 963.8 763.3 242.8 307.5 851.9 68.3 245.2 101.3
## [141] 241.3 82.7 89.1 753.6 191.1 314.0 229.2 360.7 483.8 411.0
## [151] 432.9 367.1 68.7 319.9 524.0 679.3 67.8 1038.6 706.2 79.4
## [161] 1034.6 330.5 235.0 509.2 177.6 42.5 1297.5 623.3 135.5 759.8
## [171] 674.8 719.3 865.0 450.6 537.8 458.5 203.2 377.2 86.6 546.2
## [181] 122.3 402.9 224.3 652.1 267.1 921.1 146.1 212.4 844.8 672.4
## [191] 875.9 187.1 7.8 398.3 62.0 173.9 1012.2 240.0 100.6 940.3
## [201] 842.3 651.1 329.8 713.5 13.8 387.3 1223.8 197.9 1887.0 455.3
## [211] 295.8 28.6 302.5 479.9 592.6 406.1 619.7 60.8 901.4 404.3
## [221] 992.8 134.4 1065.1 217.2 118.2 803.9 236.0 201.4 1023.4 118.5
## [231] 469.0 170.6 420.7 621.1 356.2 1426.4 1694.5 1001.2 708.9 445.9
## [241] 755.2 570.7 437.3 360.3 26.6 230.3 15.4 511.0 69.6 1927.0
## [251] 564.4 576.9 997.0 85.8 253.9 579.4 572.5 621.5 394.9 832.6
## [261] 419.0 1811.7 272.6 940.5 420.1 292.4 1190.7 1491.4 1082.0 847.1
## [271] 443.1 117.7 66.7 317.2 183.2 261.8 201.8 139.3 285.2 303.9
## [281] 265.7 737.5 586.5 104.0 258.5 418.4 238.0 515.7 318.4 947.7
## [291] 328.3 110.4 140.7 170.8 154.7 174.7 317.5 123.8 122.4 207.9
## [301] 1668.8 15.8 235.8 19.1 1796.7 1348.3 57.6 1117.1 711.3 54.2
## [311] 119.7 516.9 458.4 221.5 176.0 251.0 176.0 1614.1 1.3 466.2
## [321] 524.9 503.6 546.1 220.3 171.1 170.4 743.2 454.2 1339.3 154.1
## [331] 712.8 599.8 639.8 1261.4 185.2 256.5 286.9 421.2 999.8 28.8
## [341] 508.6 333.3 324.1 55.3 893.5 574.0 618.6 1070.8 105.3 58.3
## [351] 493.2 813.0 437.0 364.4 795.3 1076.0 1225.6 240.1 187.3 533.7
## [361] 209.7 4.8 1823.7 18.7 1655.6 88.2 276.4 1646.5 791.3 85.8
## [371] 758.9 143.8 168.6 119.4 1069.2 1035.7 989.6 1069.2 1034.4 220.2
## [381] 199.9 358.4 433.7 1495.7 131.3 138.5 128.2 256.5 209.2 891.3
## [391] 292.1 557.2 494.5 173.5 423.5 1004.4 734.6 106.6 386.8 101.2
## [401] 18.4 1056.2 89.0 219.8 1162.6 24.1 370.4 85.2 653.5 121.1
## [411] 307.3 111.5 701.3 1613.1 22.1 1693.1 355.5 4.2 225.6 1170.0
## [421] 178.5 161.9 788.6 76.3 1046.0 586.2 116.0 198.3 146.1 72.1
## [431] 59.2 2880.2 2634.6 53.6 40.3 343.5 179.0 835.5 483.7 40.5
## [441] 612.1 40.0 134.4 864.4 1480.0 834.3 101.0 446.7 1084.5 230.7
## [451] 1062.1 170.1 103.6 120.6 243.8 284.6 19.4 23.1 154.0 164.8
## [461] 1136.7 579.6 1246.7 124.1 663.0 190.8 223.8 448.9 83.3 255.2
## [471] 494.7 700.3 277.9 29.7 126.7 504.3 21.4 393.0 62.8 1826.0
## [481] 138.1 7.9 139.4 409.3 2302.8 53.7 4.1 145.0 1045.7 488.4
## [491] 559.7 128.8 711.6 66.4 604.4 66.8 603.9 560.3 360.0 347.9
## [501] 1180.7 567.0 1956.3 122.4 93.7 341.5 168.2 439.6 357.3 210.6
## [511] 609.4 1846.0 336.5 451.3 150.2 914.2 171.3 455.3 2659.3 56.4
## [521] 42.8 266.2 303.2 211.1 376.3 254.0 197.9 756.7 261.2 633.9
## [531] 334.0 158.8 140.6 201.9 141.1 123.3 657.5 177.0 346.7 107.8
## [541] 272.7 1135.8 304.1 110.0 9.0 24.0 241.3 511.5 1346.9 23.2
## [551] 1048.3 639.4 227.5 167.6 153.7 630.2 275.6 168.3 959.5 2980.7
## [561] 135.7 579.9 697.5 1462.9 103.4 1900.7 137.4 356.7 160.6 658.6
## [571] 1775.5 893.7 399.7 446.9 1620.4 22.1 133.7 33.2 355.2 25.5
## [581] 1739.8 157.5 1761.3 185.8 506.5 405.5 700.1 56.1 653.3 453.9
## [591] 148.7 1975.6 290.2 579.9 449.8 190.8 12.4 48.6 1373.0 24.7
## [601] 350.9 963.9 0.9 63.5 1306.1 633.7 274.1 812.0 1931.9 129.8
## [611] 53.8 44.4 792.6 88.3 213.8 74.4 596.1 176.1 143.7 174.6
## [621] 180.4 826.5 265.3 201.6 38.8 475.4 34.8 34.6 704.4 431.8
## [631] 1215.3 1121.0 211.5 145.0 184.7 176.5 104.2 1322.8 246.3 290.0
## [641] 1173.6 118.7 94.4 2688.8 1023.2 69.3 22.4 1510.4 218.0 61.0
## [651] 18.7 178.7 819.1 292.5 979.2 742.8 0.0 1568.5 1487.0 113.6
## [661] 197.0 1770.2 362.9 108.8 862.2 404.6 941.7 478.4 352.1 151.8
## [671] 6.5 643.0 302.8 267.0 1366.5 3840.4 505.9 152.1 219.1 651.9
## [681] 732.0 1148.2 191.8 349.1 58.2 483.5 282.4 2007.4 958.0 127.6
## [691] 199.1 933.6 407.2 287.3 200.6 37.3 432.9 568.3 552.4 254.5
## [701] 146.6 105.6 1365.1 178.4 751.0 182.3 252.3 281.8 368.7 352.4
## [711] 2038.7 586.3 14.9 244.9 1600.1 807.8 495.6 547.1 680.9 1400.1
## [721] 844.7 403.1 90.8 74.9 65.9 63.6 115.7 146.6 679.9 2016.6
## [731] 485.8 549.0 558.3 680.7 202.8 541.1 301.8 29.0 239.0 110.7
## [741] 142.8 1030.7 606.1 145.8 927.9 274.4 1741.0 1246.2 135.9 342.5
## [751] 1275.7 192.6 474.6 1803.1 324.6 1350.6 426.2 891.1 182.8 181.4
## [761] 540.9 562.0 48.2 203.9 149.3 665.3 438.4 454.3 1340.0 1.9
## [771] 446.5 261.0 61.7 144.9 400.1 392.6 1330.5 1166.3 103.2 328.0
## [781] 1401.3 134.9 1999.5 79.9 602.1 89.0 380.8 2.0 627.5 58.9
## [791] 878.3 1492.3 566.2 171.9 72.1 497.1 125.2 168.7 339.0 963.4
## [801] 827.1 211.1 723.1 490.7 405.7 212.7 110.3 152.0 46.7 447.5
## [811] 92.9 629.3 690.4 194.5 890.8 6.0 11.7 9.9 173.4 64.9
## [821] 20.4 73.8 650.5 210.5 199.3 1020.7 235.6 359.6 568.9 351.4
## [831] 235.4 176.1 1362.3 479.0 806.7 335.8 583.0 880.2 1016.4 174.3
## [841] 378.0 428.9 805.2 1339.3 9.7 1489.2 867.3 415.8 109.9 401.7
## [851] 116.5 64.5 1155.3 115.7 552.7 173.1 456.6 257.2 262.4 48.7
## [861] 334.9 889.3 1354.8 170.2 662.3 403.3 70.1 794.7 36.5 480.4
## [871] 406.5 32.5 745.2 112.9 54.0 361.9 823.7 103.7 223.6 645.5
## [881] 30.7 289.5 304.2 1010.7 508.0 351.4 890.0 160.7 245.7 846.1
## [891] 988.0 0.9 274.2 884.7 1330.1 232.8 23.3 184.7 389.9 254.1
## [901] 2497.2 344.9 1587.8 460.9 473.8 388.9 2231.7 611.3 123.7 251.7
## [911] 158.8 805.9 663.6 460.0 147.3 293.5 14.6 328.3 674.6 40.7
## [921] 395.4 939.4 2188.9 358.0 174.0 425.9 1145.3 318.6 537.1 9.9
## [931] 1603.3 28.1 76.3 192.7 684.6 1014.5 32.4 1028.3 682.3 290.3
## [941] 254.8 1088.7 478.6 239.0 279.7 196.6 413.3 244.9 797.4 238.4
## [951] 1156.9 23.9 389.7 2773.2 693.2 538.5 3.0 74.9 373.6 225.0
## [961] 124.8 167.1 547.1 990.0 361.6 1442.4 56.5 438.5 143.0 823.0
## [971] 321.3 680.6 422.7 1507.7 278.2 310.4 99.9 88.2 183.9 37.1
## [981] 1031.1 177.8 73.5 672.9 589.2 91.3 837.2 522.7 181.0 61.5
## [991] 244.2 197.0 44.8 311.2 52.8 381.7 206.3 517.0 54.5 52.4
prob <- sum(exponencial > 700) / n
prob
## [1] 0.237
(3). En una línea de ensamblaje, la probabilidad de que un producto sea defectuoso es del 5 %. Simular 100 lotes de 50 productos y calcular el número promedio de productos defectuosos por lote.
n <- 100
lote <- 50
probabilidad <- 0.05
binomial <- rbinom(n, lote, probabilidad)
binomial
## [1] 2 2 4 3 3 1 1 1 1 1 4 2 5 2 2 4 2 2 4 1 4 4 0 3 4 2 8 0 0 4 4 3 4 5 5 1 3
## [38] 4 1 2 0 0 2 2 1 1 4 4 3 2 1 2 4 0 2 3 4 1 2 4 1 3 5 4 2 3 2 2 2 3 0 2 3 0
## [75] 2 2 2 1 4 3 2 3 2 5 1 1 2 1 2 1 1 5 0 4 5 2 3 1 3 4
(4). La demanda diaria de energía (en MW) sigue una distribución normal con media de 100 MW y desviación estándar de 15 MW. Simular la demanda de un año (365 días) y calcular la probabilidad de que un día supere los 130 MW. y realizar el histograma.
n <- 365
media <- 100
desviacion <- 15
normal <- rnorm(n, media, desviacion)
prob <- sum(normal > 130) / n
prob
## [1] 0.02739726
hist(normal)
(5). Una empresa de manufactura electrónica quiere simular el tiempo de
vida (en horas) de un nuevo modelo de capacitor. Basado en datos
históricos, se ha determinado que el tiempo de vida sigue una
distribución exponencial con parámetro β = 1000 horas, que representa el
tiempo medio de vida de los capacitores.
n <- 1000
beta <- 1000
exponencial <- rexp(n, 1/beta)
round(exponencial,1)
## [1] 2618.9 3031.6 152.7 944.2 76.7 309.8 992.8 1133.9 254.1 2211.6
## [11] 109.6 214.4 1714.3 807.9 117.9 58.9 645.7 1257.5 39.0 162.9
## [21] 819.8 1566.5 103.5 334.6 862.3 1387.5 260.0 1655.9 586.0 545.1
## [31] 796.3 538.7 236.1 15.1 379.2 1522.2 2737.7 1774.2 308.5 902.0
## [41] 829.8 939.9 147.1 1765.9 570.4 169.3 1671.1 1927.6 355.8 189.0
## [51] 703.8 138.4 488.8 967.7 173.9 2215.2 508.3 1507.3 844.8 330.8
## [61] 510.8 1391.8 1536.6 3040.0 1976.5 1819.9 2566.7 19.3 55.9 478.3
## [71] 2391.0 1158.6 52.8 300.0 528.0 207.6 2244.5 1500.7 792.1 754.3
## [81] 7.4 601.5 1711.1 523.8 1961.6 792.0 605.1 450.9 64.0 825.5
## [91] 364.0 271.4 16.1 5.3 212.4 309.1 394.5 117.6 320.2 700.5
## [101] 321.9 2117.4 188.1 1103.5 393.0 1115.5 37.5 53.9 1911.2 161.3
## [111] 226.1 147.2 2655.1 176.8 726.5 49.4 2667.6 2782.5 1580.0 288.5
## [121] 36.7 1372.4 355.0 1.7 1048.4 413.1 239.8 2495.5 185.9 330.4
## [131] 380.5 2867.4 1304.0 184.7 49.6 123.3 1975.9 151.3 1381.1 2672.7
## [141] 443.4 259.5 145.3 4357.4 36.8 2021.6 340.3 731.5 88.7 207.8
## [151] 1377.6 676.7 1900.1 812.6 3136.0 998.3 1178.6 757.8 557.8 352.4
## [161] 416.6 1011.5 771.9 152.4 982.1 674.5 828.6 780.4 1396.2 17.6
## [171] 469.7 650.0 335.4 2284.3 726.5 132.2 651.4 3133.7 2388.6 1011.8
## [181] 1136.3 2454.9 575.8 1933.9 4006.9 371.7 936.7 77.0 120.4 1039.5
## [191] 2131.5 1720.4 54.3 1960.5 344.3 62.8 69.8 978.9 1068.0 1510.3
## [201] 5075.2 858.3 1512.9 1813.3 563.5 70.4 1171.2 1638.9 129.1 298.9
## [211] 1541.2 840.1 2845.6 3246.7 952.6 356.7 1084.4 1922.2 137.4 311.9
## [221] 1303.0 741.2 386.5 3210.0 1197.0 2228.9 1888.3 1178.2 3176.3 210.7
## [231] 929.6 1921.5 1921.1 742.1 79.2 4620.0 1145.1 198.2 1632.7 166.2
## [241] 116.9 941.5 349.8 635.1 10.5 64.9 250.3 319.0 363.1 289.2
## [251] 785.9 1437.4 257.3 684.2 605.2 583.6 704.4 270.3 1750.0 1447.0
## [261] 83.9 1860.0 1526.2 1504.0 2667.6 1506.9 1084.2 623.0 494.6 1.1
## [271] 2217.7 1487.3 227.4 687.2 664.8 1114.8 281.4 266.1 1778.1 246.8
## [281] 1075.9 1228.0 12.4 293.5 1407.0 513.2 755.6 445.8 21.2 3364.0
## [291] 64.4 2026.0 1417.6 536.4 2236.6 572.3 415.5 554.7 173.5 1983.1
## [301] 466.5 599.2 199.7 437.0 100.1 527.8 3061.8 4417.7 267.7 1790.3
## [311] 239.2 218.3 794.8 401.2 72.1 1252.8 263.0 2111.6 32.7 722.7
## [321] 1062.7 1971.7 1584.7 205.6 1812.2 1930.7 181.5 734.8 2559.1 1684.2
## [331] 1868.3 2193.8 19.8 90.7 1513.9 470.3 1336.1 1395.1 1103.3 1136.0
## [341] 962.5 268.8 1201.2 538.5 756.8 29.3 1848.1 1177.6 363.3 1429.3
## [351] 1495.3 625.9 63.6 308.9 28.7 56.9 2607.7 489.5 241.3 2478.0
## [361] 16.9 1067.4 381.9 907.5 457.8 1900.0 633.4 337.8 2053.5 56.8
## [371] 2422.6 999.6 58.2 671.4 1316.9 2659.4 582.1 2229.0 464.9 686.1
## [381] 275.0 438.1 201.0 1901.0 969.9 1429.6 713.5 11.3 452.3 1076.1
## [391] 1273.9 380.0 2894.6 849.4 1834.1 289.2 2887.0 3092.0 1312.7 1258.2
## [401] 198.6 982.6 872.8 1108.9 840.7 1362.5 330.8 1064.6 1032.8 995.5
## [411] 221.6 1385.2 1153.2 340.1 423.7 951.0 261.5 1006.0 167.8 760.7
## [421] 511.0 3397.3 207.0 2094.3 1733.8 659.9 29.3 86.6 333.4 166.2
## [431] 963.5 933.9 245.3 366.3 1166.9 1598.9 422.4 578.7 432.0 1809.1
## [441] 653.3 656.9 2242.4 1203.5 339.2 538.0 371.7 2663.8 185.7 475.3
## [451] 92.3 17.6 1652.8 418.2 1504.0 254.0 3093.5 1287.0 1309.6 1199.5
## [461] 2595.2 430.9 1414.7 2248.4 663.2 2.4 29.0 680.5 212.5 1178.8
## [471] 73.2 456.9 304.4 4230.7 1858.1 230.8 99.6 666.3 830.3 86.6
## [481] 107.5 740.8 89.9 1891.6 508.5 367.1 922.1 461.2 384.2 293.0
## [491] 180.6 1512.8 1015.5 426.0 525.7 143.9 619.1 1525.7 997.7 419.5
## [501] 1874.9 125.3 654.8 887.0 4100.7 122.2 2561.1 871.2 36.1 145.6
## [511] 1215.3 268.5 306.7 367.2 366.4 1018.2 500.8 1708.6 200.8 622.4
## [521] 67.0 1362.3 23.6 778.3 357.3 527.0 791.1 900.3 1168.4 490.3
## [531] 8.1 1583.6 1648.8 1542.5 355.4 300.8 1964.7 2360.3 1095.4 5387.3
## [541] 1378.4 17.6 954.8 2638.6 1875.7 1854.7 959.8 654.3 162.6 889.3
## [551] 8.9 577.4 1607.4 213.1 430.9 2336.3 378.6 1174.2 2742.5 335.1
## [561] 112.9 354.6 1058.4 362.1 1652.7 285.4 213.0 82.4 805.8 479.9
## [571] 42.0 244.9 804.8 1619.7 787.5 191.4 1083.5 36.5 1037.9 3061.6
## [581] 1478.8 827.4 1012.7 3510.5 1592.3 3047.3 424.4 685.6 34.8 427.5
## [591] 612.2 195.4 168.2 1938.9 319.8 2178.7 3211.5 389.4 1132.6 933.7
## [601] 1532.9 1180.3 2328.1 2235.2 1209.5 519.4 2194.9 2233.6 470.8 83.2
## [611] 2539.5 177.8 257.9 357.7 2700.2 1481.4 339.1 2252.7 350.5 800.3
## [621] 46.4 889.4 1047.2 389.3 889.7 2268.7 358.4 1290.9 1451.0 1116.6
## [631] 94.1 3000.6 1294.2 4252.2 3802.8 779.0 3211.8 225.6 992.0 812.1
## [641] 762.0 2520.7 807.8 173.9 822.6 1409.5 1053.2 1064.3 365.0 1154.2
## [651] 2312.2 652.4 1558.9 675.3 72.6 552.3 553.3 369.8 569.1 743.3
## [661] 1686.3 1356.7 309.3 16.4 2085.3 554.6 1819.8 2842.1 181.8 875.1
## [671] 578.0 584.3 416.6 437.5 970.1 1296.2 302.6 3757.5 401.6 1845.0
## [681] 129.9 612.6 410.1 867.0 419.6 1349.9 2687.7 338.6 2434.3 978.1
## [691] 1717.2 191.0 744.5 1496.7 4523.4 563.9 1975.8 534.5 672.2 319.9
## [701] 1547.2 1383.8 1798.2 267.3 26.2 861.9 323.8 3352.3 700.2 3038.4
## [711] 41.2 171.7 979.6 3092.7 1025.5 227.9 2501.1 517.7 183.4 292.0
## [721] 1437.2 125.6 704.2 1074.6 741.9 616.6 1546.9 1693.3 166.1 39.6
## [731] 2746.9 155.2 380.7 99.3 698.4 813.2 2187.7 372.5 2237.3 305.3
## [741] 3526.1 1660.0 762.7 471.9 321.7 125.5 708.4 51.3 783.3 1078.4
## [751] 1239.3 96.9 54.7 938.4 2088.2 748.1 199.4 169.0 795.2 447.8
## [761] 2143.8 2880.0 193.2 385.4 886.2 2611.1 493.3 1641.7 696.3 1030.3
## [771] 320.9 531.5 914.8 39.1 210.7 1688.4 23.0 648.9 415.5 517.8
## [781] 1445.2 127.6 485.9 573.0 2126.1 1114.2 1700.8 762.2 200.0 457.4
## [791] 4088.5 2196.4 140.7 596.2 573.3 780.0 787.3 247.0 1943.4 1724.6
## [801] 1294.4 387.4 2359.0 171.1 1737.6 1255.8 1746.3 1019.6 2314.1 1781.0
## [811] 2578.7 45.0 905.3 466.9 2096.7 824.7 827.1 782.7 310.2 2420.8
## [821] 35.7 609.1 971.8 22.5 2826.8 277.6 2703.0 81.5 277.5 2013.5
## [831] 308.7 445.7 1094.0 851.4 1091.7 83.4 412.9 272.0 101.4 396.3
## [841] 153.7 270.8 4577.4 3047.6 216.5 986.5 240.7 91.6 2299.5 853.3
## [851] 434.3 176.0 186.5 757.7 3703.3 874.2 16.7 807.5 2207.4 5.0
## [861] 2557.0 172.8 1707.2 678.2 1409.6 245.5 279.7 412.1 688.1 1127.8
## [871] 2623.3 605.2 445.8 550.1 567.8 386.2 800.2 3625.2 1753.7 1896.3
## [881] 10.1 354.7 1551.5 247.0 59.7 1103.3 2509.5 953.2 3522.8 90.9
## [891] 208.5 468.6 928.4 105.7 314.8 3162.0 1505.1 642.6 327.0 2039.2
## [901] 1457.4 3139.5 246.0 2642.4 723.7 662.4 1540.2 147.9 220.9 2215.5
## [911] 400.0 1539.4 239.9 1644.9 663.2 1346.3 668.0 575.9 510.0 2685.0
## [921] 998.4 206.1 3088.5 719.2 1395.5 919.4 3389.3 1548.0 620.2 201.4
## [931] 102.3 3351.0 1840.4 2784.3 1067.0 700.6 558.4 841.3 625.0 191.3
## [941] 204.7 80.3 1092.4 53.5 1023.2 723.0 479.4 81.9 1112.5 3073.5
## [951] 689.8 750.1 1795.5 1282.6 82.4 25.6 1985.6 2126.9 5035.1 297.8
## [961] 408.0 443.6 685.4 2153.8 829.9 2098.7 1682.3 238.8 1306.0 949.5
## [971] 904.9 225.8 718.0 477.6 75.0 615.1 924.8 891.6 1198.5 218.2
## [981] 204.2 377.7 805.7 1192.2 5850.1 77.2 1380.0 1961.8 627.5 284.0
## [991] 536.5 527.1 373.9 661.2 343.8 346.7 244.8 1272.2 550.6 259.2
media <- mean(exponencial)
varianza <- var(exponencial)
media
## [1] 1001.176
varianza
## [1] 879701
hist(exponencial, breaks = 30, probability = TRUE,
col = "skyblue", border = "black",
main = "Tiempos de Vida Simulados",
xlab = "Tiempo de Vida (horas)")
curve(dexp(x, rate = 1/beta), col = "red", lwd = 2, add = TRUE)
legend("topright", legend = c("Densidad Teórica"), col = "red", lwd = 2)
Prob_simulada <-sum(exponencial <940)/n
Prob_simulada
## [1] 0.601