KASUS 1
set.seed(221016)
dtnorm<-rnorm(n=500,mean=100,sd=5);dt
## function (x, df, ncp, log = FALSE)
## {
## if (missing(ncp))
## .Call(C_dt, x, df, log)
## else .Call(C_dnt, x, df, ncp, log)
## }
## <bytecode: 0x000001877d2fe1d8>
## <environment: namespace:stats>
mean(dtnorm)
## [1] 99.73688
var(dtnorm)
## [1] 24.11463
hist(dtnorm, main = "Normal Distribution")
abline(v=mean(dtnorm), lty=2, lwd=3, col="blue")
set.seed(221016)
s5<-sample(dtnorm,size=5);s5
## [1] 85.07063 98.29833 106.06108 99.90953 105.35139
s10<-sample(dtnorm,size=10);s10
## [1] 103.97203 102.86302 98.23660 96.78828 97.30528 99.26633 105.04064
## [8] 101.72935 100.65908 99.83953
s30<-sample(dtnorm,size=30);s30
## [1] 106.06108 103.88627 100.11103 100.10654 100.65908 101.96803 100.29231
## [8] 98.29975 102.33300 96.90386 102.31160 98.02521 94.55888 107.86063
## [15] 100.29617 104.54915 90.50557 108.71892 103.59422 90.25722 103.43924
## [22] 89.62194 100.51071 108.26172 95.45269 96.46583 91.23008 107.52601
## [29] 101.37606 97.07568
rata.s5<-mean(s5)
rata.s10<-mean(s10)
rata.s30<-mean(s30)
ragam.s5<-var(s5)
ragam.s10<-var(s10)
ragam.s30<-var(s30)
set.seed(221016)
iterasi<-100
n<-5
means.s5<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n)
means.s5[i]<-mean(p)
}
hist(means.s5,main = "Histogram Rata-rata dari 5 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 5 Sampel")
abline(v=mean(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(means.s5), lty=2, lwd=3, col="red")
# Uji Normalitas
library(nortest)
ns5<-nortest::lillie.test(means.s5)
set.seed(221016)
iterasi<-100
n<-10
means.s10<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n)
means.s10[i]<-mean(p)
}
hist(means.s10,main = "Histogram Rata-rata dari 10 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 10 Sampel")
abline(v=mean(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(means.s10), lty=2, lwd=3, col="red")
# Uji Normalitas
library(nortest)
ns10<-nortest::lillie.test(means.s10)
set.seed(221016)
iterasi<-100
n<-30
means.s30<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n)
means.s30[i]<-mean(p)
}
hist(means.s30,main = "Histogram Rata-rata dari 30 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 30 Sampel")
abline(v=mean(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(means.s30), lty=2, lwd=3, col="red")
# Uji Normalitas
library(nortest)
ns30<-nortest::lillie.test(means.s30)
mean(means.s5)
## [1] 99.64863
mean(means.s10)
## [1] 99.47457
mean(means.s30)
## [1] 99.68001
set.seed(221016)
iterasi<-100
n<-5
var.s5<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n)
var.s5[i]<-var(p)
}
hist(var.s5,main = "Histogram Varians dari 5 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Varians 5 Sampel")
abline(v=var(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(var.s5), lty=2, lwd=3, col="red")
set.seed(221016)
iterasi<-100
n<-10
var.s10<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n)
var.s10[i]<-var(p)
}
hist(var.s10,main = "Histogram Varians dari 10 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Varians 10 Sampel")
abline(v=var(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(var.s10), lty=2, lwd=3, col="red")
set.seed(221016)
iterasi<-100
n<-30
var.s30<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n)
var.s30[i]<-var(p)
}
hist(var.s5,main = "Histogram Varians dari 30 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Varians 30 Sampel")
abline(v=var(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(var.s30), lty=2, lwd=3, col="red")
mean(var.s5)
## [1] 25.69085
mean(var.s10)
## [1] 25.51659
mean(var.s30)
## [1] 24.68252
GABUNGAN DISTRIBUSI NORMAL
set.seed(221016)
dtnorm<-rnorm(n=500,mean=100,sd=5)
rata.pop<-mean(dtnorm)
var.pop<-var(dtnorm)
rata_norm <- c(mean(s5),mean(s10),mean(s30))
ragam_norm <- c(var(s5),var(s10),var(s30))
ukuran.contoh <- c("n=5","n=10","n=30")
rata.pengulangan.100x<-c(mean(means.s5),mean(means.s10),mean(means.s30))
hasil1 <- cbind(ukuran.contoh,rata.pop,var.pop,rata_norm,ragam_norm,rata.pengulangan.100x)
colnames(hasil1)<-c("Ukuran Contoh","Rata Populasi Normal","Ragam Populasi Normal","Rata Contoh","Ragam Contoh","Rata Pengulangan 100x")
as.data.frame(hasil1)
## Ukuran Contoh Rata Populasi Normal Ragam Populasi Normal Rata Contoh
## 1 n=5 99.7368817482474 24.1146279082558 98.9381932192459
## 2 n=10 99.7368817482474 24.1146279082558 100.57001252319
## 3 n=30 99.7368817482474 24.1146279082558 100.07528242561
## Ragam Contoh Rata Pengulangan 100x
## 1 71.3816926959421 99.6486329567975
## 2 7.86756801568658 99.4745729128662
## 3 28.780919600471 99.680006344368
# Histogram Rata-rata Sampel dengan Pengulangan 100x
par(mfrow=c(3,1))
set.seed(221016)
iterasi<-100
n1<-5
n2<-10
n3<-30
means.s5<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n1)
means.s5[i]<-mean(p)
}
hist(means.s5,main = "Histogram Rata-rata dari 5 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 5 Sampel")
abline(v=mean(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(means.s5), lty=2, lwd=3, col="red")
means.s10<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n2)
means.s10[i]<-mean(p)
}
hist(means.s10,main = "Histogram Rata-rata dari 10 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 10 Sampel")
abline(v=mean(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(means.s10), lty=2, lwd=3, col="red")
means.s30<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtnorm, n3)
means.s30[i]<-mean(p)
}
hist(means.s30,main = "Histogram Rata-rata dari 30 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 30 Sampel")
abline(v=mean(dtnorm), lty=2, lwd=3, col="blue")
abline(v=mean(means.s30), lty=2, lwd=3, col="red")
shapiro.test(means.s5)
##
## Shapiro-Wilk normality test
##
## data: means.s5
## W = 0.98804, p-value = 0.5108
# Uji Normalitas-Liliefors (Kolmogorov Smirnov)
ns5<-nortest::lillie.test(means.s5)$p.value
ns10<-nortest::lillie.test(means.s10)$p.value
ns30<-nortest::lillie.test(means.s30)$p.value
p.value<-c(ns5,ns10,ns30)
uji.normal <- cbind(ukuran.contoh,p.value)
colnames(uji.normal)<-c("Ukuran Contoh","Nilai P-value Liliefors")
as.data.frame(uji.normal)
## Ukuran Contoh Nilai P-value Liliefors
## 1 n=5 0.560112342623957
## 2 n=10 0.55168384502463
## 3 n=30 0.14418928789647
DISTRIBUSI GAMMA
set.seed(221016)
dtgamma<-rgamma(n=500,shape = 4,scale = 2);dtgamma
## [1] 4.4442100 12.2420556 10.8376244 5.9388531 6.0376526 3.8214351
## [7] 4.6851714 14.5201442 3.9122024 12.1037677 8.3539361 8.4589671
## [13] 8.7125792 8.6516013 7.9495135 7.7439122 5.2868848 12.2370215
## [19] 5.1379260 6.9429308 6.8187114 7.2362267 8.5141601 2.5258791
## [25] 5.1234524 15.0049018 6.0844641 3.1306313 6.6102057 7.8950147
## [31] 10.7346436 9.1565518 5.5388756 10.8181706 8.5524678 3.5539979
## [37] 16.6743873 6.9982764 4.8731966 14.9232451 10.2241280 7.9694293
## [43] 6.4936541 7.9736688 3.9046038 14.7360438 10.7858478 4.5406075
## [49] 5.0894589 13.4638855 13.8309084 20.9232236 4.2603010 2.4720946
## [55] 10.2932691 7.9438198 5.5453398 5.9998440 5.9040434 7.1772288
## [61] 3.7581599 6.3792973 13.5839566 3.5923162 3.1587432 4.8223273
## [67] 4.9826712 7.3713460 9.4263662 5.7775503 9.9736907 6.3435371
## [73] 8.9128877 5.2505756 5.7824520 8.3556780 4.2112478 13.2000408
## [79] 6.1159542 3.2792891 4.3604725 7.2033059 3.8261178 9.0405494
## [85] 2.7857124 5.4836518 8.9607651 5.0853329 10.0103830 4.2738710
## [91] 6.9594904 15.9911458 3.1571680 4.9877278 9.6966577 2.6520203
## [97] 12.0263779 2.2400526 3.5192522 2.7421090 11.6698525 3.2378110
## [103] 10.5462814 7.9698849 11.0825712 15.2254854 3.2741317 8.0885014
## [109] 19.0921350 18.2373247 3.6651315 5.0726255 5.5209575 4.1076657
## [115] 8.5885771 14.0926682 9.2050759 4.0106631 5.4466465 8.9675091
## [121] 11.5833192 9.2126304 8.8367148 4.9698257 7.6773599 15.6127398
## [127] 10.3127493 6.2359507 13.5056764 5.6553934 4.3784340 7.7333545
## [133] 3.4274672 5.7184716 6.2468507 5.1902025 9.5285134 8.1377637
## [139] 6.2294786 27.5994576 9.4282168 8.7040908 6.7661404 4.7709801
## [145] 5.4260478 5.7574555 3.9931828 4.6413702 7.9895221 5.3482936
## [151] 5.6954848 6.1934787 16.4640605 5.1158846 10.6205212 14.5476209
## [157] 23.8607793 4.7168679 10.2102844 2.5204610 2.4786708 7.2233898
## [163] 6.7826927 7.4733615 10.5291972 5.2505664 13.7887525 11.4160311
## [169] 9.9423789 2.7747676 3.7582888 5.3688925 6.7343001 9.3589086
## [175] 12.0418183 5.3401096 3.5177690 22.5918022 5.3991377 4.5008918
## [181] 7.3851713 6.0893259 5.4160335 5.9547959 4.8898981 3.2237311
## [187] 12.5459467 10.4365924 3.7118039 14.1739692 5.8116044 9.6623123
## [193] 12.0579117 6.3392214 3.7767504 2.0902003 11.5773622 7.1338428
## [199] 8.9367475 14.9886345 3.9021284 15.0836176 7.5018978 7.5998167
## [205] 6.4617356 6.5530866 8.8066316 10.1529944 4.2809131 2.6054555
## [211] 8.4681546 2.8604296 7.9049195 12.0281441 9.6904430 8.5375710
## [217] 14.1147020 8.0379506 6.8622165 13.9289175 9.0559326 4.2280659
## [223] 4.7551374 9.9278604 6.3637638 6.5749661 5.1286855 2.8537825
## [229] 6.1378419 13.5155649 11.7378829 3.5024327 5.6001951 8.1525270
## [235] 7.5014783 7.8074637 5.8130113 11.8191247 5.0319654 4.9436368
## [241] 7.9716370 9.4933501 7.0799517 12.1845840 7.3998645 5.5150612
## [247] 4.1211208 9.9359021 10.6429140 8.9984642 2.8233564 11.8692458
## [253] 9.2192631 3.8800795 5.2208494 0.5938596 1.6312496 4.9188354
## [259] 4.6598917 4.8869127 5.1906547 10.5614456 3.0617997 2.4205159
## [265] 7.1953545 24.1576634 7.9757167 2.8392247 17.3771545 10.0795334
## [271] 6.0232778 9.6466159 3.9849242 8.7168276 13.5830348 10.6453194
## [277] 4.8747797 0.7918424 23.3103226 8.8547116 19.0726502 7.0572730
## [283] 16.7711571 6.2070696 8.6289077 8.1031799 13.6412839 10.9826331
## [289] 5.6354023 7.7917089 2.9899585 9.8021493 6.4580536 6.5596956
## [295] 10.5969008 10.6448111 15.1300989 3.6212757 8.2581216 9.4017294
## [301] 14.3921763 4.3349960 5.8180828 9.3544525 9.1016202 15.5142290
## [307] 1.6327220 4.8321304 11.2762628 12.0672935 2.6084940 5.8320500
## [313] 23.9497452 9.0393956 7.4084142 8.4797102 9.1004144 3.2095860
## [319] 7.5144691 4.7124294 15.9517083 5.8684476 4.3925480 8.5166412
## [325] 13.0578867 6.8818397 7.6952186 8.9673182 12.9520050 6.6364808
## [331] 7.0833372 8.4442365 12.6123764 8.2994755 6.7210548 6.7813830
## [337] 5.6587262 10.6037873 10.2289377 6.5754382 9.0285032 11.0896894
## [343] 4.3017095 6.5065273 9.7480167 13.0980864 10.7605613 5.7840344
## [349] 4.6960098 8.8152731 10.6567438 6.1741720 7.6064950 5.1995691
## [355] 8.5950775 4.1269539 5.8677219 9.1045870 5.7737141 15.3624583
## [361] 11.3090622 1.3878538 10.5375931 5.2554509 4.6248857 7.2729721
## [367] 6.8375484 13.2975837 8.2660524 7.6064121 18.6317832 14.2801389
## [373] 6.4757402 7.4043664 8.3982933 11.9235301 5.1396686 4.6259153
## [379] 4.9566287 6.5560520 11.3496810 3.6829029 9.6747074 3.0509986
## [385] 10.7761128 6.8944632 6.0223278 12.5107279 6.7770163 10.5378113
## [391] 4.2599359 7.3677405 6.9564882 8.1520402 9.4611108 7.6638570
## [397] 4.0112300 7.5443325 14.3307338 2.4250988 5.0267273 11.1326096
## [403] 6.6557424 2.7149929 4.6054977 9.9202397 10.1826288 7.4940255
## [409] 2.0251718 12.5100789 14.1148244 4.4522267 4.6120831 4.3423725
## [415] 6.4930328 2.5555905 4.7190110 8.9034096 11.6473912 12.9151267
## [421] 4.5931130 10.8436031 7.1915125 7.3086419 14.6221074 15.2240206
## [427] 10.6795119 17.1487053 2.1401041 14.3292276 4.1695270 13.1504336
## [433] 7.5168846 2.0822917 4.6161888 8.1268904 13.5171612 6.6983973
## [439] 9.5746788 13.7921462 7.2933054 11.2315294 8.8372453 3.3763870
## [445] 5.9113825 4.7300274 6.9351465 6.8082747 6.1970784 10.2625338
## [451] 2.9300014 16.2966178 3.3979242 9.2230187 11.6317457 2.8926108
## [457] 10.1297785 6.0648765 8.5035978 8.4545755 4.2541530 11.5714253
## [463] 9.3530969 9.1425471 5.5160875 15.4348621 15.4612742 5.8671016
## [469] 6.7030239 9.1055556 4.1592710 10.8312285 3.8638719 11.2372182
## [475] 12.3070885 24.6815597 10.3703432 10.2980551 12.3866305 9.8700784
## [481] 8.3171358 9.6440167 7.6437307 1.2635788 6.8091178 2.3172340
## [487] 6.0331254 9.3796946 9.4850370 5.5547034 9.3482617 9.3870322
## [493] 3.8884985 4.8150153 4.3473336 7.1234029 4.7778185 6.4419553
## [499] 8.5784295 6.2074896
mean(dtgamma)
## [1] 8.058266
var(dtgamma)
## [1] 17.04812
hist(dtgamma, main = "Gamma Distribution")
abline(v=mean(dtgamma), lty=2, lwd=3, col="blue")
set.seed(221016)
g5<-sample(dtgamma,size=5);g5
## [1] 9.039396 8.960765 6.894463 4.228066 24.157663
g10<-sample(dtgamma,size=10);g10
## [1] 6.703024 4.011230 6.343537 4.211248 9.354453 4.874780 6.023278
## [8] 8.651601 13.583035 7.367740
g30<-sample(dtgamma,size=30);g30
## [1] 6.8944632 12.0281441 6.9351465 8.5166412 13.5830348 14.9232451
## [7] 6.7661404 5.2208494 7.4043664 7.6064121 7.2233898 5.8320500
## [13] 0.7918424 7.9438198 8.0379506 7.9694293 6.5560520 15.3624583
## [19] 8.5375710 0.5938596 17.1487053 2.3172340 5.3991377 4.2809131
## [25] 14.5476209 16.2966178 4.4522267 8.3171358 5.5209575 10.0103830
mean(g5)
## [1] 10.65607
mean(g10)
## [1] 7.112393
mean(g30)
## [1] 8.233927
var(g5)
## [1] 60.81241
var(g10)
## [1] 8.25734
var(g30)
## [1] 18.82518
set.seed(221016)
iterasi<-100
n<-5
means.g5<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n)
means.g5[i]<-mean(p)
}
hist(means.g5,main = "Histogram Rata-rata dari 5 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 5 Sampel")
abline(v=mean(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(means.g5), lty=2, lwd=3, col="red")
# Uji Normalitas
library(nortest)
nortest::lillie.test(means.g5)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: means.g5
## D = 0.070505, p-value = 0.2561
set.seed(221016)
iterasi<-100
n<-10
means.g10<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n)
means.g10[i]<-mean(p)
}
hist(means.g10,main = "Histogram Rata-rata dari 10 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 10 Sampel")
abline(v=mean(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(means.g10), lty=2, lwd=3, col="red")
# Uji Normalitas
library(nortest)
nortest::lillie.test(means.g10)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: means.g10
## D = 0.06113, p-value = 0.4743
set.seed(221016)
iterasi<-100
n<-30
means.g30<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n)
means.g30[i]<-mean(p)
}
hist(means.g30,main = "Histogram Rata-rata dari 30 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 30 Sampel")
abline(v=mean(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(means.g30), lty=2, lwd=3, col="red")
# Uji Normalitas
library(nortest)
nortest::lillie.test(means.g30)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: means.g30
## D = 0.069418, p-value = 0.2774
mean(means.g5)
## [1] 8.152948
mean(means.g10)
## [1] 7.940048
mean(means.g30)
## [1] 8.064358
set.seed(221016)
iterasi<-100
n<-5
var.g5<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n)
var.g5[i]<-var(p)
}
hist(var.g5,main = "Histogram Varians dari 5 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Varians 5 Sampel")
abline(v=var(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(var.g5), lty=2, lwd=3, col="red")
set.seed(221016)
iterasi<-100
n<-10
var.g10<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n)
var.g10[i]<-var(p)
}
hist(var.g10,main = "Histogram Varians dari 10 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Varians 10 Sampel")
abline(v=var(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(var.g10), lty=2, lwd=3, col="red")
set.seed(221016)
iterasi<-100
n<-30
var.g30<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n)
var.g30[i]<-var(p)
}
hist(var.g5,main = "Histogram Varians dari 30 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Varians 30 Sampel")
abline(v=var(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(var.g30), lty=2, lwd=3, col="red")
mean(var.g5)
## [1] 18.76951
mean(var.g10)
## [1] 16.75298
mean(var.g30)
## [1] 16.94216
GABUNGAN DISTRIBUSI NORMAL
set.seed(221016)
dtgamma<-rgamma(n=500,shape = 4,scale = 2)
rata.pop.gamma<-mean(dtgamma)
var.pop.gamma<-var(dtgamma)
rata_gamma <- c(mean(g5),mean(g10),mean(g30))
ragam_gamma <- c(var(g5),var(g10),var(g30))
ukuran.contoh <- c("n=5","n=10","n=30")
rata.pengulangan.100x.gamma<-c(mean(means.g5),mean(means.g10),mean(means.g30))
hasil2 <- cbind(ukuran.contoh,rata.pop.gamma,var.pop.gamma,rata_gamma,ragam_gamma,rata.pengulangan.100x.gamma)
colnames(hasil2)<-c("Ukuran Contoh","Rata Populasi Gamma","Ragam Populasi Gamma","Rata Contoh","Ragam Contoh","Rata Pengulangan 100x")
as.data.frame(hasil2)
## Ukuran Contoh Rata Populasi Gamma Ragam Populasi Gamma Rata Contoh
## 1 n=5 8.05826567577401 17.0481246750786 10.6560706402587
## 2 n=10 8.05826567577401 17.0481246750786 7.11239254804058
## 3 n=30 8.05826567577401 17.0481246750786 8.2339265937647
## Ragam Contoh Rata Pengulangan 100x
## 1 60.8124103621543 8.15294759263906
## 2 8.25734045108724 7.94004810218726
## 3 18.8251755796941 8.06435835048531
# Histogram Rata-rata Sampel dengan Pengulangan 100x
par(mfrow=c(3,1))
set.seed(221016)
iterasi<-100
n1<-5
n2<-10
n3<-30
means.g5<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n1)
means.g5[i]<-mean(p)
}
hist(means.g5,main = "Histogram Rata-rata dari 5 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 5 Sampel")
abline(v=mean(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(means.g5), lty=2, lwd=3, col="red")
means.g10<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n2)
means.g10[i]<-mean(p)
}
hist(means.g10,main = "Histogram Rata-rata dari 10 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 10 Sampel")
abline(v=mean(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(means.g10), lty=2, lwd=3, col="red")
means.g30<-rep(NA, iterasi)
for (i in 1:iterasi){
p<-sample(dtgamma, n3)
means.g30[i]<-mean(p)
}
hist(means.g30,main = "Histogram Rata-rata dari 30 Sampel dengan Pengulangan Sebanyak 100x",xlab = "Rata-rata 30 Sampel")
abline(v=mean(dtgamma), lty=2, lwd=3, col="blue")
abline(v=mean(means.g30), lty=2, lwd=3, col="red")
# Uji Normalitas-Liliefors (Kolmogorov Smirnov)
gs5<-nortest::lillie.test(means.g5)$p.value
gs10<-nortest::lillie.test(means.g10)$p.value
gs30<-nortest::lillie.test(means.g30)$p.value
p.value<-c(gs5,gs10,gs30)
uji.normal1 <- cbind(ukuran.contoh,p.value)
colnames(uji.normal1)<-c("Ukuran Contoh","Nilai P-value Liliefors")
as.data.frame(uji.normal1)
## Ukuran Contoh Nilai P-value Liliefors
## 1 n=5 0.256053461971558
## 2 n=10 0.528855649264766
## 3 n=30 0.224694796638531