Primera etapa.
conglomerados<-sample(unique(buses$Operador), size = 12)
conglomerados
## [1] "GRAN AMÉRICAS FONTIBÓN S.A.S." "ESTE ES MI BUS TINTAL ZONA FRANCA"
## [3] "SOMOS BOGOTÁ USME" "SI18 NORTE "
## [5] "BMO SUR" "E-SOMOS ALIMENTACIÓN S.A.S. "
## [7] "SI18 CALLE 80" "CONSORCIO EXPRESS USAQUEN"
## [9] "CONSORCIO EXPRESS SAN CRISTOBAL" "MASIVO CAPITAL SUBA ORIENTAL"
## [11] "MASIVO CAPITAL KENNEDY" "SUMA"
muestra_cong<-buses[buses$Operador %in% conglomerados,]
Ope_1<-subset(muestra_cong, muestra_cong[,9] == conglomerados[1])
Ope_2<-subset(muestra_cong, muestra_cong[,9] == conglomerados[2])
Ope_3<-subset(muestra_cong, muestra_cong[,9] == conglomerados[3])
Ope_4<-subset(muestra_cong, muestra_cong[,9] == conglomerados[4])
Ope_5<-subset(muestra_cong, muestra_cong[,9] == conglomerados[5])
Ope_6<-subset(muestra_cong, muestra_cong[,9] == conglomerados[6])
Ope_7<-subset(muestra_cong, muestra_cong[,9] == conglomerados[7])
Ope_8<-subset(muestra_cong, muestra_cong[,9] == conglomerados[8])
Ope_9<-subset(muestra_cong, muestra_cong[,9] == conglomerados[9])
Ope_10<-subset(muestra_cong, muestra_cong[,9] == conglomerados[10])
Ope_11<-subset(muestra_cong, muestra_cong[,9] == conglomerados[11])
Ope_12<-subset(muestra_cong, muestra_cong[,9] == conglomerados[12])
Segunda etapa.
cong_1<-sample(1:nrow(Ope_1), 12)
muest_cong_1<-Ope_1[cong_1,]
cong_1
## [1] 30 5 108 94 4 80 90 91 42 107 78 62
cong_2<-sample(1:nrow(Ope_2), 12)
muest_cong_2<-Ope_2[cong_2,]
cong_2
## [1] 93 36 150 38 10 115 78 71 43 83 35 9
cong_3<-sample(1:nrow(Ope_3), 12)
muest_cong_3<-Ope_3[cong_3,]
cong_3
## [1] 242 133 140 20 135 214 22 15 239 200 94 134
cong_4<-sample(1:nrow(Ope_4), 12)
muest_cong_4<-Ope_4[cong_4,]
cong_4
## [1] 214 49 191 113 109 114 169 168 38 2 151 41
cong_5<-sample(1:nrow(Ope_5), 12)
muest_cong_5<-Ope_5[cong_5,]
cong_5
## [1] 176 337 229 398 8 421 261 197 235 297 406 227
cong_6<-sample(1:nrow(Ope_6), 12)
muest_cong_6<-Ope_6[cong_6,]
cong_6
## [1] 53 61 52 119 106 118 83 51 34 127 131 54
cong_7<-sample(1:nrow(Ope_7), 12)
muest_cong_7<-Ope_7[cong_7,]
cong_7
## [1] 49 81 75 3 79 63 66 105 52 32 62 20
cong_8<-sample(1:nrow(Ope_8), 12)
muest_cong_8<-Ope_8[cong_8,]
cong_8
## [1] 760 815 270 600 1198 1334 668 351 4 43 791 821
cong_9<-sample(1:nrow(Ope_9), 12)
muest_cong_9<-Ope_9[cong_9,]
cong_9
## [1] 531 227 344 641 52 175 675 708 797 602 360 538
cong_10<-sample(1:nrow(Ope_10), 12)
muest_cong_10<-Ope_10[cong_10,]
cong_10
## [1] 217 43 54 123 137 262 129 149 117 37 235 173
cong_11<-sample(1:nrow(Ope_11), 12)
muest_cong_11<-Ope_11[cong_11,]
cong_11
## [1] 523 331 251 626 569 76 154 68 864 134 245 616
cong_12<-sample(1:nrow(Ope_12), 12)
muest_cong_12<-Ope_12[cong_12,]
cong_12
## [1] 174 545 685 142 520 573 422 649 718 125 566 456
Promedios de calificación.
Cal1<-unlist(muest_cong_1[,13])
Yi1<-mean(Cal1)
Yi1
## [1] 9.009167
Cal2<-unlist(muest_cong_2[,13])
Yi2<-mean(Cal2)
Yi2
## [1] 6.155833
Cal3<-unlist(muest_cong_3[,13])
Yi3<-mean(Cal3)
Yi3
## [1] 8.695833
Cal4<-unlist(muest_cong_4[,13])
Yi4<-mean(Cal4)
Yi4
## [1] 8.345833
Cal5<-unlist(muest_cong_5[,13])
Yi5<-mean(Cal5)
Yi5
## [1] 8.29
Cal6<-unlist(muest_cong_6[,13])
Yi6<-mean(Cal6)
Yi6
## [1] 8.806667
Cal7<-unlist(muest_cong_7[,13])
Yi7<-mean(Cal7)
Yi7
## [1] 8.698333
Cal8<-unlist(muest_cong_8[,13])
Yi8<-mean(Cal8)
Yi8
## [1] 5.810833
Cal9<-unlist(muest_cong_9[,13])
Yi9<-mean(Cal9)
Yi9
## [1] 5.516667
Cal10<-unlist(muest_cong_10[,13])
Yi10<-mean(Cal10)
Yi10
## [1] 5.784167
Cal11<-unlist(muest_cong_11[,13])
Yi11<-mean(Cal11)
Yi11
## [1] 6.843333
Cal12<-unlist(muest_cong_12[,13])
Yi12<-mean(Cal12)
Yi12
## [1] 6.965