set.seed(221016)
n1<-rnorm(n=300,mean=100,sd=4)
n2<-rnorm(n=150,mean=500,sd=5)
n3<-rnorm(n=50,mean=1000,sd=6)dtpopulasi<-c(n1,n2,n3);dtpopulasi## [1] 96.95875 95.03914 96.73495 105.23011 99.22354 98.93319
## [7] 97.35375 102.95294 97.27778 96.88068 97.73295 95.12661
## [13] 104.71383 101.38348 101.48594 101.73075 101.67222 100.98280
## [19] 100.77520 98.04030 107.61185 99.31551 100.29827 99.93887
## [25] 95.51251 100.63758 100.25044 101.53952 94.02382 104.61342
## [31] 96.22441 106.94585 98.98697 96.75169 107.76376 99.57732
## [37] 103.69807 98.46553 105.43250 101.57443 99.18080 103.63932
## [43] 101.57662 95.69770 101.26130 99.16981 94.58581 97.52106
## [49] 102.19443 99.92953 97.00932 103.73073 96.92912 101.00702
## [55] 96.21135 106.28850 99.33563 93.73950 104.79601 97.79514
## [61] 102.97254 104.70459 98.04081 99.87831 99.40489 98.36239
## [67] 96.70940 100.13914 104.21207 93.29688 96.89165 98.58928
## [73] 94.59987 100.97709 98.35444 95.96803 97.43062 98.77853
## [79] 99.29803 99.87813 101.64788 99.32104 103.16074 98.87861
## [85] 104.10644 95.08721 98.63867 97.43141 97.66054 95.92635
## [91] 99.95378 98.71494 92.79382 102.89837 99.28094 96.31177
## [97] 104.17317 98.72733 98.63625 107.11682 106.31699 97.92810
## [103] 105.58577 99.02303 102.65375 94.13620 96.84587 99.64812
## [109] 100.24570 94.65547 102.04212 94.47492 101.10085 101.09565
## [115] 91.05045 97.81574 102.30464 96.27536 97.58165 102.93120
## [121] 96.72798 98.44723 95.90860 107.65453 95.08471 99.93821
## [127] 99.61532 102.64851 94.24557 98.97428 106.52725 92.22859
## [133] 95.64544 96.82718 93.51406 104.35784 95.21227 100.80514
## [139] 104.01333 90.45112 95.84164 93.98924 100.23385 101.12163
## [145] 109.75074 109.19107 95.86315 100.01027 92.44228 98.32512
## [151] 102.17717 102.13241 101.61150 106.26932 102.19619 96.36215
## [157] 99.44357 99.68058 101.97327 104.28606 102.20323 101.84928
## [163] 97.64426 106.82420 107.21680 102.99001 96.93145 103.86751
## [169] 105.82235 98.48598 99.76477 97.78333 100.76447 95.50614
## [175] 103.15108 98.58600 99.17194 103.93796 100.40856 102.49511
## [181] 101.17055 99.15227 98.32383 97.43496 96.11360 102.40297
## [187] 101.72262 99.74787 101.01208 101.83249 94.46777 93.54436
## [193] 96.16246 99.76678 98.00035 100.12457 96.33743 99.77498
## [199] 91.58906 101.02289 98.11563 100.21475 100.18712 99.11141
## [205] 110.21716 98.82903 100.41222 103.46857 106.60937 96.97780
## [211] 90.72520 100.22422 99.98373 103.10902 94.01417 102.87538
## [217] 107.05885 100.23694 99.76586 96.09238 104.71203 99.92762
## [223] 97.48337 106.03909 104.14653 102.87030 94.45635 97.72435
## [229] 93.25325 98.14079 97.65561 99.76908 102.33901 104.66340
## [235] 98.10561 95.73206 101.52570 89.33788 97.93437 101.76408
## [241] 104.36527 97.03456 102.41727 93.83892 107.69361 98.19821
## [247] 98.40750 95.19272 97.54244 101.84732 100.67881 105.07009
## [253] 103.30824 95.93189 98.63980 92.20577 100.41952 98.67050
## [259] 92.06594 98.18889 103.39955 102.97427 96.02682 103.63048
## [265] 97.34502 104.28111 100.14241 101.94419 106.93397 96.20781
## [271] 104.03251 102.73831 94.83716 100.30037 100.52726 100.62805
## [277] 99.41306 95.64711 104.77514 101.82063 103.05824 96.73761
## [283] 94.38926 96.01390 101.49487 94.60070 99.22423 98.20920
## [289] 102.39167 103.33230 96.08148 99.14358 100.60775 98.98831
## [295] 106.14558 102.05659 96.66514 106.12045 105.29207 102.85727
## [301] 499.12954 503.98863 498.10694 497.30528 498.70187 500.62897
## [307] 498.81005 500.19713 501.79500 497.61566 498.32112 498.02521
## [313] 503.48500 485.07063 504.69349 498.29853 497.17553 505.60130
## [319] 497.15356 504.83025 497.94909 501.25623 503.07858 500.10654
## [325] 492.47896 489.44899 498.69189 510.47460 500.27887 495.64637
## [331] 506.06657 499.10692 499.98807 496.73118 497.73354 505.65279
## [337] 502.76177 495.22024 494.15816 494.91243 486.74084 510.36624
## [343] 497.24883 496.97424 496.08966 503.11194 496.92327 494.63333
## [349] 503.54399 504.27154 493.66467 509.31427 501.76906 500.25926
## [355] 516.04633 501.26134 493.20646 496.36457 494.54664 508.71892
## [361] 501.03223 503.74114 498.64579 503.09700 498.25828 502.73705
## [367] 502.16853 507.35225 504.36260 496.90386 501.21848 498.37062
## [373] 515.43137 502.33300 512.17265 500.07638 487.81555 495.68400
## [379] 502.30061 490.50557 502.06298 501.42027 495.58290 490.09834
## [385] 498.29715 506.06108 510.15902 501.02963 493.51865 500.07340
## [391] 495.26376 499.83953 503.65470 497.07633 495.22884 496.21905
## [397] 502.86302 499.40206 495.65342 502.66373 496.30411 492.92703
## [403] 493.84079 501.61180 502.97319 508.11724 496.01414 497.25644
## [409] 503.44502 502.91861 502.62434 491.23008 495.85828 495.46313
## [415] 505.76396 500.16542 496.83541 501.57794 500.67367 501.66625
## [421] 495.64901 498.36809 501.13353 498.76803 500.23105 502.46185
## [427] 504.23402 503.43924 500.29476 500.67530 496.64168 497.41588
## [433] 508.21662 494.16113 496.11154 502.23870 495.61824 502.69582
## [439] 507.05548 504.63286 499.84143 504.02495 502.84257 498.92395
## [445] 499.91218 494.37260 500.11103 501.83951 506.40384 501.66262
## [451] 999.54815 995.75899 999.84685 997.73491 983.87245 1001.91740
## [457] 1000.17677 994.38297 1003.04618 1005.80705 995.14901 994.65852
## [463] 1009.17409 1004.04264 1008.25937 1005.38457 997.95717 1006.77507
## [469] 1004.76643 1002.74330 988.27591 997.81658 998.13756 999.42518
## [475] 1000.95236 996.89867 1006.18040 994.91102 996.67696 998.10428
## [481] 1009.03121 1009.50924 989.99559 1010.80809 1006.08517 987.54633
## [487] 1007.47087 995.43082 997.01881 995.79812 995.31473 989.57687
## [493] 985.49000 997.80952 996.20611 998.78093 999.73797 996.38815
## [499] 1004.30388 995.87640set.seed(221016)
ssrs<-sample(dtpopulasi,size=30);ssrs## [1] 485.07063 98.63867 506.06108 99.92762 104.28111 1004.76643
## [7] 502.86302 98.58928 97.43062 497.30528 99.41306 104.03251
## [13] 101.38348 100.52726 499.83953 996.38815 103.10902 500.11103
## [19] 500.10654 1007.47087 101.57443 100.23385 98.63980 502.33300
## [25] 496.90386 101.84928 498.02521 95.64711 106.28850 100.23694rat_srs<-mean(ssrs);rat_srs## [1] 323.6349var_srs<-var(ssrs);var_srs## [1] 87467.35set.seed(221016)
ssbn1<-sample(n1,size=10);ssbn1## [1] 98.63867 99.92762 104.28111 98.58928 97.43062 99.41306 104.03251
## [8] 101.38348 100.52726 103.10902ssbn2<-sample(n2,size=10);ssbn2## [1] 507.3523 497.1536 494.9124 502.8426 501.5779 495.4631 501.2613 501.2562
## [9] 498.9239 510.3662ssbn3<-sample(n3,size=10);ssbn3## [1] 995.7981 999.4252 997.8095 996.8987 995.4308 1010.8081 997.9572
## [8] 994.9110 997.7349 985.4900rat_ssbn1<-mean(ssbn1);rat_ssbn1## [1] 100.7333rat_ssbn2<-mean(ssbn2);rat_ssbn2## [1] 501.111rat_ssbn3<-mean(ssbn3);rat_ssbn3## [1] 997.2264var_ssbn1<-var(ssbn1);var_ssbn1## [1] 5.762794var_ssbn2<-var(ssbn2);var_ssbn2## [1] 24.29311var_ssbn3<-var(ssbn3);var_ssbn3## [1] 37.65614dtsampel1<-c(ssbn1,ssbn2,ssbn3)# Rata2
rat_sbtot <- 1/500*((300*rat_ssbn1)+(150*rat_ssbn2)+(50*rat_ssbn3))
rat_sbtot## [1] 310.4959# Ragam
s1 <- (300*(300-10)*(var_ssbn1/10))
s1## [1] 50136.31s2 <- (150*(150-10)*(var_ssbn2/10))
s2## [1] 51015.52s3 <- (50*(50-10)*(var_ssbn3/10))
s3## [1] 7531.228var_sbtot <- 1/(500^2)*(s1+s2+s3)
var_sbtot## [1] 0.4347322set.seed(221016)
spn1<-sample(n1,size=12);spn1## [1] 98.63867 99.92762 104.28111 98.58928 97.43062 99.41306 104.03251
## [8] 101.38348 100.52726 103.10902 99.14358 101.57443spn2<-sample(n2,size=6);spn2## [1] 502.8426 501.5779 495.4631 501.2613 501.2562 500.1110spn3<-sample(n3,size=3);spn3## [1] 996.8987 989.5769 995.7981rat_spn1<-mean(spn1);rat_spn1## [1] 100.6709rat_spn2<-mean(spn2);rat_spn2## [1] 500.4187rat_spn3<-mean(spn3);rat_spn3## [1] 994.0912var_spn1<-var(spn1);var_spn1## [1] 5.004826var_spn2<-var(spn2);var_spn2## [1] 6.656554var_spn3<-var(spn3);var_spn3## [1] 15.58732dtsampel2<-c(spn1,spn2,spn3)rat_ptot <- 1/500*((300*rat_spn1)+(150*rat_spn2)+(50*rat_spn3))
rat_ptot## [1] 309.9373s11 <- (300*(300-12)*(var_spn1/12))
s11## [1] 36034.75s22 <- (150*(150-6)*(var_spn2/6))
s22## [1] 23963.6s33 <- (50*(50-2)*(var_spn3/2))
s33## [1] 18704.79var_ptot <- 1/(500^2)*(s11+s22+s33)
var_ptot## [1] 0.3148125set.seed(221016)
n1<-rnorm(n=300,mean=100,sd=4)
n2<-rnorm(n=150,mean=500,sd=5)
n3<-rnorm(n=50,mean=1000,sd=6)
dtpopulasi<-c(n1,n2,n3)
rat_pop<-mean(dtpopulasi)
ragam_pop<-var(dtpopulasi)
hasil0 <- cbind(rat_pop,ragam_pop)
colnames(hasil0)<-c("Rata Populasi","Ragam Populasi")
as.data.frame(hasil0)## Rata Populasi Ragam Populasi
## 1 309.7579 85025.1# Simple Random Sampling
ssrs<-sample(dtpopulasi,size=30)
rat_srs<-mean(ssrs)
var_srs<-var(ssrs)
ukuran.contoh <- c("n=30")
hasil1 <- cbind(ukuran.contoh,rat_srs,var_srs)
colnames(hasil1)<-c("Ukuran Contoh","Rata Contoh SRS","Ragam Contoh SRS")
as.data.frame(hasil1)## Ukuran Contoh Rata Contoh SRS Ragam Contoh SRS
## 1 n=30 306.204592571114 73068.9421063023# Stratified Sampling
## Alokasi Sama Besar
ssbn1<-sample(n1,size=10)
ssbn2<-sample(n2,size=10)
ssbn3<-sample(n3,size=10)
### Rata dan Ragam Contoh
rat_ssbn1<-mean(ssbn1)
rat_ssbn2<-mean(ssbn2)
rat_ssbn3<-mean(ssbn3)
var_ssbn1<-var(ssbn1)
var_ssbn2<-var(ssbn2)
var_ssbn3<-var(ssbn3)
### Rata dan Ragam Keseluruhan
rat_sbtot <- 1/500*((300*rat_ssbn1)+(150*rat_ssbn2)+(50*rat_ssbn3))
s1 <- (300*(300-10)*(var_ssbn1/10))
s2 <- (150*(150-10)*(var_ssbn2/10))
s3 <- (50*(50-10)*(var_ssbn3/10))
var_sbtot <- 1/(500^2)*(s1+s2+s3)
rata_seragam <- c(rat_ssbn1,rat_ssbn2,rat_ssbn3)
ragam_seragam <- c(var_ssbn1,var_ssbn2,var_ssbn3)
ukuran.contoh <- c("n=10","n=10","n=10")
hasil2 <- cbind(ukuran.contoh,rat_sbtot,var_sbtot,rata_seragam,ragam_seragam)
colnames(hasil2)<-c("Ukuran Contoh","Rata Keseluruhan Stratified Seragam","Ragam Keseluruhan Stratified Seragam","Rata Contoh Stratified Seragam","Ragam Contoh Stratified Seragam")
as.data.frame(hasil2)## Ukuran Contoh Rata Keseluruhan Stratified Seragam
## 1 n=10 309.332903878263
## 2 n=10 309.332903878263
## 3 n=10 309.332903878263
## Ragam Keseluruhan Stratified Seragam Rata Contoh Stratified Seragam
## 1 1.12058546350727 98.1199143206425
## 2 1.12058546350727 500.786072628426
## 3 1.12058546350727 1002.25133497349
## Ragam Contoh Stratified Seragam
## 1 26.9636599096724
## 2 18.2702124080928
## 3 35.9753930283642## Alokasi Proporsional
spn1<-sample(n1,size=12)
spn2<-sample(n2,size=6)
spn3<-sample(n3,size=2)
### Rata dan Ragam Contoh
rat_spn1<-mean(spn1)
rat_spn2<-mean(spn2)
rat_spn3<-mean(spn3)
var_spn1<-var(spn1)
var_spn2<-var(spn2)
var_spn3<-var(spn3)
### Rata dan Ragam Keseluruhan
rat_ptot <- 1/500*((300*rat_spn1)+(150*rat_spn2)+(50*rat_spn3))
s11 <- (300*(300-12)*(var_spn1/12))
s22 <- (150*(150-6)*(var_spn2/6))
s33 <- (50*(50-2)*(var_spn3/2))
var_ptot <- 1/(500^2)*(s11+s22+s33)
rata_proporsional<-c(rat_spn1,rat_spn2,rat_spn3)
ragam_proporsional<-c(var_spn1,var_spn2,var_spn3)
ukuran.contoh <- c("n=12","n=6","n=2")
hasil3 <- cbind(ukuran.contoh,rat_ptot,var_ptot,rata_proporsional,ragam_proporsional)
colnames(hasil3)<-c("Ukuran Contoh","Rata Keseluruhan Stratified Proporsional","Ragam Keseluruhan Stratified Proporsional","Rata Contoh Stratified Proporsional","Ragam Contoh Stratified Proporsional")
as.data.frame(hasil3)## Ukuran Contoh Rata Keseluruhan Stratified Proporsional
## 1 n=12 308.993335593998
## 2 n=6 308.993335593998
## 3 n=2 308.993335593998
## Ragam Keseluruhan Stratified Proporsional Rata Contoh Stratified Proporsional
## 1 1.35107832732192 98.8704440194919
## 2 1.35107832732192 499.257643161624
## 3 1.35107832732192 998.937762338153
## Ragam Contoh Stratified Proporsional
## 1 27.2072448362539
## 2 38.9835074127094
## 3 1.28066026974834