Tugas Business Intelegence
Rajni Shrinishaa
2023-11-10
Nama : Rajni Shrinishaa, NRP : 5006211082, Kelas : Business Intelligence
Tugas Business Intelligence
Data yang digunakan adalah data Fish Market. Terdapat variabel-variabel berupa
- Species = nama-nama spesies ikan
- Weight = Berat Ikan yang didapat
- Length1 = Panjang ikan pada pengukuran pertama
- Length2 = Panjang ikan pada pengukuran kedua
- Length3 = Panjang ikan pada pengukuran ketiga
- Height = Tinggi ikan
- Width = lebar ikan
variabel dependen pada data adalah Weight(Y) dan variabel independen berupa Length, Height, dan Width (X1, X2, X3).
Pertama, import data yang akan digunakan.
#import file
fish<-read.csv("D:/BINTEL/Fish.csv")
fish## Species Weight Length1 Length2 Length3 Height Width
## 1 Bream 242.0 23.2 25.4 30.0 11.5200 4.0200
## 2 Bream 290.0 24.0 26.3 31.2 12.4800 4.3056
## 3 Bream 340.0 23.9 26.5 31.1 12.3778 4.6961
## 4 Bream 363.0 26.3 29.0 33.5 12.7300 4.4555
## 5 Bream 430.0 26.5 29.0 34.0 12.4440 5.1340
## 6 Bream 450.0 26.8 29.7 34.7 13.6024 4.9274
## 7 Bream 500.0 26.8 29.7 34.5 14.1795 5.2785
## 8 Bream 390.0 27.6 30.0 35.0 12.6700 4.6900
## 9 Bream 450.0 27.6 30.0 35.1 14.0049 4.8438
## 10 Bream 500.0 28.5 30.7 36.2 14.2266 4.9594
## 11 Bream 475.0 28.4 31.0 36.2 14.2628 5.1042
## 12 Bream 500.0 28.7 31.0 36.2 14.3714 4.8146
## 13 Bream 500.0 29.1 31.5 36.4 13.7592 4.3680
## 14 Bream 340.0 29.5 32.0 37.3 13.9129 5.0728
## 15 Bream 600.0 29.4 32.0 37.2 14.9544 5.1708
## 16 Bream 600.0 29.4 32.0 37.2 15.4380 5.5800
## 17 Bream 700.0 30.4 33.0 38.3 14.8604 5.2854
## 18 Bream 700.0 30.4 33.0 38.5 14.9380 5.1975
## 19 Bream 610.0 30.9 33.5 38.6 15.6330 5.1338
## 20 Bream 650.0 31.0 33.5 38.7 14.4738 5.7276
## 21 Bream 575.0 31.3 34.0 39.5 15.1285 5.5695
## 22 Bream 685.0 31.4 34.0 39.2 15.9936 5.3704
## 23 Bream 620.0 31.5 34.5 39.7 15.5227 5.2801
## 24 Bream 680.0 31.8 35.0 40.6 15.4686 6.1306
## 25 Bream 700.0 31.9 35.0 40.5 16.2405 5.5890
## 26 Bream 725.0 31.8 35.0 40.9 16.3600 6.0532
## 27 Bream 720.0 32.0 35.0 40.6 16.3618 6.0900
## 28 Bream 714.0 32.7 36.0 41.5 16.5170 5.8515
## 29 Bream 850.0 32.8 36.0 41.6 16.8896 6.1984
## 30 Bream 1000.0 33.5 37.0 42.6 18.9570 6.6030
## 31 Bream 920.0 35.0 38.5 44.1 18.0369 6.3063
## 32 Bream 955.0 35.0 38.5 44.0 18.0840 6.2920
## 33 Bream 925.0 36.2 39.5 45.3 18.7542 6.7497
## 34 Bream 975.0 37.4 41.0 45.9 18.6354 6.7473
## 35 Bream 950.0 38.0 41.0 46.5 17.6235 6.3705
## 36 Roach 40.0 12.9 14.1 16.2 4.1472 2.2680
## 37 Roach 69.0 16.5 18.2 20.3 5.2983 2.8217
## 38 Roach 78.0 17.5 18.8 21.2 5.5756 2.9044
## 39 Roach 87.0 18.2 19.8 22.2 5.6166 3.1746
## 40 Roach 120.0 18.6 20.0 22.2 6.2160 3.5742
## 41 Roach 0.0 19.0 20.5 22.8 6.4752 3.3516
## 42 Roach 110.0 19.1 20.8 23.1 6.1677 3.3957
## 43 Roach 120.0 19.4 21.0 23.7 6.1146 3.2943
## 44 Roach 150.0 20.4 22.0 24.7 5.8045 3.7544
## 45 Roach 145.0 20.5 22.0 24.3 6.6339 3.5478
## 46 Roach 160.0 20.5 22.5 25.3 7.0334 3.8203
## 47 Roach 140.0 21.0 22.5 25.0 6.5500 3.3250
## 48 Roach 160.0 21.1 22.5 25.0 6.4000 3.8000
## 49 Roach 169.0 22.0 24.0 27.2 7.5344 3.8352
## 50 Roach 161.0 22.0 23.4 26.7 6.9153 3.6312
## 51 Roach 200.0 22.1 23.5 26.8 7.3968 4.1272
## 52 Roach 180.0 23.6 25.2 27.9 7.0866 3.9060
## 53 Roach 290.0 24.0 26.0 29.2 8.8768 4.4968
## 54 Roach 272.0 25.0 27.0 30.6 8.5680 4.7736
## 55 Roach 390.0 29.5 31.7 35.0 9.4850 5.3550
## 56 Whitefish 270.0 23.6 26.0 28.7 8.3804 4.2476
## 57 Whitefish 270.0 24.1 26.5 29.3 8.1454 4.2485
## 58 Whitefish 306.0 25.6 28.0 30.8 8.7780 4.6816
## 59 Whitefish 540.0 28.5 31.0 34.0 10.7440 6.5620
## 60 Whitefish 800.0 33.7 36.4 39.6 11.7612 6.5736
## 61 Whitefish 1000.0 37.3 40.0 43.5 12.3540 6.5250
## 62 Parkki 55.0 13.5 14.7 16.5 6.8475 2.3265
## 63 Parkki 60.0 14.3 15.5 17.4 6.5772 2.3142
## 64 Parkki 90.0 16.3 17.7 19.8 7.4052 2.6730
## 65 Parkki 120.0 17.5 19.0 21.3 8.3922 2.9181
## 66 Parkki 150.0 18.4 20.0 22.4 8.8928 3.2928
## 67 Parkki 140.0 19.0 20.7 23.2 8.5376 3.2944
## 68 Parkki 170.0 19.0 20.7 23.2 9.3960 3.4104
## 69 Parkki 145.0 19.8 21.5 24.1 9.7364 3.1571
## 70 Parkki 200.0 21.2 23.0 25.8 10.3458 3.6636
## 71 Parkki 273.0 23.0 25.0 28.0 11.0880 4.1440
## 72 Parkki 300.0 24.0 26.0 29.0 11.3680 4.2340
## 73 Perch 5.9 7.5 8.4 8.8 2.1120 1.4080
## 74 Perch 32.0 12.5 13.7 14.7 3.5280 1.9992
## 75 Perch 40.0 13.8 15.0 16.0 3.8240 2.4320
## 76 Perch 51.5 15.0 16.2 17.2 4.5924 2.6316
## 77 Perch 70.0 15.7 17.4 18.5 4.5880 2.9415
## 78 Perch 100.0 16.2 18.0 19.2 5.2224 3.3216
## 79 Perch 78.0 16.8 18.7 19.4 5.1992 3.1234
## 80 Perch 80.0 17.2 19.0 20.2 5.6358 3.0502
## 81 Perch 85.0 17.8 19.6 20.8 5.1376 3.0368
## 82 Perch 85.0 18.2 20.0 21.0 5.0820 2.7720
## 83 Perch 110.0 19.0 21.0 22.5 5.6925 3.5550
## 84 Perch 115.0 19.0 21.0 22.5 5.9175 3.3075
## 85 Perch 125.0 19.0 21.0 22.5 5.6925 3.6675
## 86 Perch 130.0 19.3 21.3 22.8 6.3840 3.5340
## 87 Perch 120.0 20.0 22.0 23.5 6.1100 3.4075
## 88 Perch 120.0 20.0 22.0 23.5 5.6400 3.5250
## 89 Perch 130.0 20.0 22.0 23.5 6.1100 3.5250
## 90 Perch 135.0 20.0 22.0 23.5 5.8750 3.5250
## 91 Perch 110.0 20.0 22.0 23.5 5.5225 3.9950
## 92 Perch 130.0 20.5 22.5 24.0 5.8560 3.6240
## 93 Perch 150.0 20.5 22.5 24.0 6.7920 3.6240
## 94 Perch 145.0 20.7 22.7 24.2 5.9532 3.6300
## 95 Perch 150.0 21.0 23.0 24.5 5.2185 3.6260
## 96 Perch 170.0 21.5 23.5 25.0 6.2750 3.7250
## 97 Perch 225.0 22.0 24.0 25.5 7.2930 3.7230
## 98 Perch 145.0 22.0 24.0 25.5 6.3750 3.8250
## 99 Perch 188.0 22.6 24.6 26.2 6.7334 4.1658
## 100 Perch 180.0 23.0 25.0 26.5 6.4395 3.6835
## 101 Perch 197.0 23.5 25.6 27.0 6.5610 4.2390
## 102 Perch 218.0 25.0 26.5 28.0 7.1680 4.1440
## 103 Perch 300.0 25.2 27.3 28.7 8.3230 5.1373
## 104 Perch 260.0 25.4 27.5 28.9 7.1672 4.3350
## 105 Perch 265.0 25.4 27.5 28.9 7.0516 4.3350
## 106 Perch 250.0 25.4 27.5 28.9 7.2828 4.5662
## 107 Perch 250.0 25.9 28.0 29.4 7.8204 4.2042
## 108 Perch 300.0 26.9 28.7 30.1 7.5852 4.6354
## 109 Perch 320.0 27.8 30.0 31.6 7.6156 4.7716
## 110 Perch 514.0 30.5 32.8 34.0 10.0300 6.0180
## 111 Perch 556.0 32.0 34.5 36.5 10.2565 6.3875
## 112 Perch 840.0 32.5 35.0 37.3 11.4884 7.7957
## 113 Perch 685.0 34.0 36.5 39.0 10.8810 6.8640
## 114 Perch 700.0 34.0 36.0 38.3 10.6091 6.7408
## 115 Perch 700.0 34.5 37.0 39.4 10.8350 6.2646
## 116 Perch 690.0 34.6 37.0 39.3 10.5717 6.3666
## 117 Perch 900.0 36.5 39.0 41.4 11.1366 7.4934
## 118 Perch 650.0 36.5 39.0 41.4 11.1366 6.0030
## 119 Perch 820.0 36.6 39.0 41.3 12.4313 7.3514
## 120 Perch 850.0 36.9 40.0 42.3 11.9286 7.1064
## 121 Perch 900.0 37.0 40.0 42.5 11.7300 7.2250
## 122 Perch 1015.0 37.0 40.0 42.4 12.3808 7.4624
## 123 Perch 820.0 37.1 40.0 42.5 11.1350 6.6300
## 124 Perch 1100.0 39.0 42.0 44.6 12.8002 6.8684
## 125 Perch 1000.0 39.8 43.0 45.2 11.9328 7.2772
## 126 Perch 1100.0 40.1 43.0 45.5 12.5125 7.4165
## 127 Perch 1000.0 40.2 43.5 46.0 12.6040 8.1420
## 128 Perch 1000.0 41.1 44.0 46.6 12.4888 7.5958
## 129 Pike 200.0 30.0 32.3 34.8 5.5680 3.3756
## 130 Pike 300.0 31.7 34.0 37.8 5.7078 4.1580
## 131 Pike 300.0 32.7 35.0 38.8 5.9364 4.3844
## 132 Pike 300.0 34.8 37.3 39.8 6.2884 4.0198
## 133 Pike 430.0 35.5 38.0 40.5 7.2900 4.5765
## 134 Pike 345.0 36.0 38.5 41.0 6.3960 3.9770
## 135 Pike 456.0 40.0 42.5 45.5 7.2800 4.3225
## 136 Pike 510.0 40.0 42.5 45.5 6.8250 4.4590
## 137 Pike 540.0 40.1 43.0 45.8 7.7860 5.1296
## 138 Pike 500.0 42.0 45.0 48.0 6.9600 4.8960
## 139 Pike 567.0 43.2 46.0 48.7 7.7920 4.8700
## 140 Pike 770.0 44.8 48.0 51.2 7.6800 5.3760
## 141 Pike 950.0 48.3 51.7 55.1 8.9262 6.1712
## 142 Pike 1250.0 52.0 56.0 59.7 10.6863 6.9849
## 143 Pike 1600.0 56.0 60.0 64.0 9.6000 6.1440
## 144 Pike 1550.0 56.0 60.0 64.0 9.6000 6.1440
## 145 Pike 1650.0 59.0 63.4 68.0 10.8120 7.4800
## 146 Smelt 6.7 9.3 9.8 10.8 1.7388 1.0476
## 147 Smelt 7.5 10.0 10.5 11.6 1.9720 1.1600
## 148 Smelt 7.0 10.1 10.6 11.6 1.7284 1.1484
## 149 Smelt 9.7 10.4 11.0 12.0 2.1960 1.3800
## 150 Smelt 9.8 10.7 11.2 12.4 2.0832 1.2772
## 151 Smelt 8.7 10.8 11.3 12.6 1.9782 1.2852
## 152 Smelt 10.0 11.3 11.8 13.1 2.2139 1.2838
## 153 Smelt 9.9 11.3 11.8 13.1 2.2139 1.1659
## 154 Smelt 9.8 11.4 12.0 13.2 2.2044 1.1484
## 155 Smelt 12.2 11.5 12.2 13.4 2.0904 1.3936
## 156 Smelt 13.4 11.7 12.4 13.5 2.4300 1.2690
## 157 Smelt 12.2 12.1 13.0 13.8 2.2770 1.2558
## 158 Smelt 19.7 13.2 14.3 15.2 2.8728 2.0672
## 159 Smelt 19.9 13.8 15.0 16.2 2.9322 1.8792Untuk data Length dijadikan satu berupa rata-rata dari Length1, 2, dan 3. Kemudian variabel menjadi:
- Weigth = Y
- Length = X1
- Height = X2
- Width = X3
#rata-rata array length dijadikan satu
Length<-numeric(159)
i=1
for (i in 1:159){
Length[i]=(fish$Length1[i]+fish$Length2[i]+fish$Length3[i])/3
}
Length## [1] 26.200000 27.166667 27.166667 29.600000 29.833333 30.400000 30.333333
## [8] 30.866667 30.900000 31.800000 31.866667 31.966667 32.333333 32.933333
## [15] 32.866667 32.866667 33.900000 33.966667 34.333333 34.400000 34.933333
## [22] 34.866667 35.233333 35.800000 35.800000 35.900000 35.866667 36.733333
## [29] 36.800000 37.700000 39.200000 39.166667 40.333333 41.433333 41.833333
## [36] 14.400000 18.333333 19.166667 20.066667 20.266667 20.766667 21.000000
## [43] 21.366667 22.366667 22.266667 22.766667 22.833333 22.866667 24.400000
## [50] 24.033333 24.133333 25.566667 26.400000 27.533333 32.066667 26.100000
## [57] 26.633333 28.133333 31.166667 36.566667 40.266667 14.900000 15.733333
## [64] 17.933333 19.266667 20.266667 20.966667 20.966667 21.800000 23.333333
## [71] 25.333333 26.333333 8.233333 13.633333 14.933333 16.133333 17.200000
## [78] 17.800000 18.300000 18.800000 19.400000 19.733333 20.833333 20.833333
## [85] 20.833333 21.133333 21.833333 21.833333 21.833333 21.833333 21.833333
## [92] 22.333333 22.333333 22.533333 22.833333 23.333333 23.833333 23.833333
## [99] 24.466667 24.833333 25.366667 26.500000 27.066667 27.266667 27.266667
## [106] 27.266667 27.766667 28.566667 29.800000 32.433333 34.333333 34.933333
## [113] 36.500000 36.100000 36.966667 36.966667 38.966667 38.966667 38.966667
## [120] 39.733333 39.833333 39.800000 39.866667 41.866667 42.666667 42.866667
## [127] 43.233333 43.900000 32.366667 34.500000 35.500000 37.300000 38.000000
## [134] 38.500000 42.666667 42.666667 42.966667 45.000000 45.966667 48.000000
## [141] 51.700000 55.900000 60.000000 60.000000 63.466667 9.966667 10.700000
## [148] 10.766667 11.133333 11.433333 11.566667 12.066667 12.066667 12.200000
## [155] 12.366667 12.533333 12.966667 14.233333 15.000000#buat dataframe baru dengan data length yang baru
datafish<-data.frame(fish$Species, fish$Weight, Length,fish$Height, fish$Width)
colnames(datafish)<-c("Species","Y","X1","X2","X3")
datafish## Species Y X1 X2 X3
## 1 Bream 242.0 26.200000 11.5200 4.0200
## 2 Bream 290.0 27.166667 12.4800 4.3056
## 3 Bream 340.0 27.166667 12.3778 4.6961
## 4 Bream 363.0 29.600000 12.7300 4.4555
## 5 Bream 430.0 29.833333 12.4440 5.1340
## 6 Bream 450.0 30.400000 13.6024 4.9274
## 7 Bream 500.0 30.333333 14.1795 5.2785
## 8 Bream 390.0 30.866667 12.6700 4.6900
## 9 Bream 450.0 30.900000 14.0049 4.8438
## 10 Bream 500.0 31.800000 14.2266 4.9594
## 11 Bream 475.0 31.866667 14.2628 5.1042
## 12 Bream 500.0 31.966667 14.3714 4.8146
## 13 Bream 500.0 32.333333 13.7592 4.3680
## 14 Bream 340.0 32.933333 13.9129 5.0728
## 15 Bream 600.0 32.866667 14.9544 5.1708
## 16 Bream 600.0 32.866667 15.4380 5.5800
## 17 Bream 700.0 33.900000 14.8604 5.2854
## 18 Bream 700.0 33.966667 14.9380 5.1975
## 19 Bream 610.0 34.333333 15.6330 5.1338
## 20 Bream 650.0 34.400000 14.4738 5.7276
## 21 Bream 575.0 34.933333 15.1285 5.5695
## 22 Bream 685.0 34.866667 15.9936 5.3704
## 23 Bream 620.0 35.233333 15.5227 5.2801
## 24 Bream 680.0 35.800000 15.4686 6.1306
## 25 Bream 700.0 35.800000 16.2405 5.5890
## 26 Bream 725.0 35.900000 16.3600 6.0532
## 27 Bream 720.0 35.866667 16.3618 6.0900
## 28 Bream 714.0 36.733333 16.5170 5.8515
## 29 Bream 850.0 36.800000 16.8896 6.1984
## 30 Bream 1000.0 37.700000 18.9570 6.6030
## 31 Bream 920.0 39.200000 18.0369 6.3063
## 32 Bream 955.0 39.166667 18.0840 6.2920
## 33 Bream 925.0 40.333333 18.7542 6.7497
## 34 Bream 975.0 41.433333 18.6354 6.7473
## 35 Bream 950.0 41.833333 17.6235 6.3705
## 36 Roach 40.0 14.400000 4.1472 2.2680
## 37 Roach 69.0 18.333333 5.2983 2.8217
## 38 Roach 78.0 19.166667 5.5756 2.9044
## 39 Roach 87.0 20.066667 5.6166 3.1746
## 40 Roach 120.0 20.266667 6.2160 3.5742
## 41 Roach 0.0 20.766667 6.4752 3.3516
## 42 Roach 110.0 21.000000 6.1677 3.3957
## 43 Roach 120.0 21.366667 6.1146 3.2943
## 44 Roach 150.0 22.366667 5.8045 3.7544
## 45 Roach 145.0 22.266667 6.6339 3.5478
## 46 Roach 160.0 22.766667 7.0334 3.8203
## 47 Roach 140.0 22.833333 6.5500 3.3250
## 48 Roach 160.0 22.866667 6.4000 3.8000
## 49 Roach 169.0 24.400000 7.5344 3.8352
## 50 Roach 161.0 24.033333 6.9153 3.6312
## 51 Roach 200.0 24.133333 7.3968 4.1272
## 52 Roach 180.0 25.566667 7.0866 3.9060
## 53 Roach 290.0 26.400000 8.8768 4.4968
## 54 Roach 272.0 27.533333 8.5680 4.7736
## 55 Roach 390.0 32.066667 9.4850 5.3550
## 56 Whitefish 270.0 26.100000 8.3804 4.2476
## 57 Whitefish 270.0 26.633333 8.1454 4.2485
## 58 Whitefish 306.0 28.133333 8.7780 4.6816
## 59 Whitefish 540.0 31.166667 10.7440 6.5620
## 60 Whitefish 800.0 36.566667 11.7612 6.5736
## 61 Whitefish 1000.0 40.266667 12.3540 6.5250
## 62 Parkki 55.0 14.900000 6.8475 2.3265
## 63 Parkki 60.0 15.733333 6.5772 2.3142
## 64 Parkki 90.0 17.933333 7.4052 2.6730
## 65 Parkki 120.0 19.266667 8.3922 2.9181
## 66 Parkki 150.0 20.266667 8.8928 3.2928
## 67 Parkki 140.0 20.966667 8.5376 3.2944
## 68 Parkki 170.0 20.966667 9.3960 3.4104
## 69 Parkki 145.0 21.800000 9.7364 3.1571
## 70 Parkki 200.0 23.333333 10.3458 3.6636
## 71 Parkki 273.0 25.333333 11.0880 4.1440
## 72 Parkki 300.0 26.333333 11.3680 4.2340
## 73 Perch 5.9 8.233333 2.1120 1.4080
## 74 Perch 32.0 13.633333 3.5280 1.9992
## 75 Perch 40.0 14.933333 3.8240 2.4320
## 76 Perch 51.5 16.133333 4.5924 2.6316
## 77 Perch 70.0 17.200000 4.5880 2.9415
## 78 Perch 100.0 17.800000 5.2224 3.3216
## 79 Perch 78.0 18.300000 5.1992 3.1234
## 80 Perch 80.0 18.800000 5.6358 3.0502
## 81 Perch 85.0 19.400000 5.1376 3.0368
## 82 Perch 85.0 19.733333 5.0820 2.7720
## 83 Perch 110.0 20.833333 5.6925 3.5550
## 84 Perch 115.0 20.833333 5.9175 3.3075
## 85 Perch 125.0 20.833333 5.6925 3.6675
## 86 Perch 130.0 21.133333 6.3840 3.5340
## 87 Perch 120.0 21.833333 6.1100 3.4075
## 88 Perch 120.0 21.833333 5.6400 3.5250
## 89 Perch 130.0 21.833333 6.1100 3.5250
## 90 Perch 135.0 21.833333 5.8750 3.5250
## 91 Perch 110.0 21.833333 5.5225 3.9950
## 92 Perch 130.0 22.333333 5.8560 3.6240
## 93 Perch 150.0 22.333333 6.7920 3.6240
## 94 Perch 145.0 22.533333 5.9532 3.6300
## 95 Perch 150.0 22.833333 5.2185 3.6260
## 96 Perch 170.0 23.333333 6.2750 3.7250
## 97 Perch 225.0 23.833333 7.2930 3.7230
## 98 Perch 145.0 23.833333 6.3750 3.8250
## 99 Perch 188.0 24.466667 6.7334 4.1658
## 100 Perch 180.0 24.833333 6.4395 3.6835
## 101 Perch 197.0 25.366667 6.5610 4.2390
## 102 Perch 218.0 26.500000 7.1680 4.1440
## 103 Perch 300.0 27.066667 8.3230 5.1373
## 104 Perch 260.0 27.266667 7.1672 4.3350
## 105 Perch 265.0 27.266667 7.0516 4.3350
## 106 Perch 250.0 27.266667 7.2828 4.5662
## 107 Perch 250.0 27.766667 7.8204 4.2042
## 108 Perch 300.0 28.566667 7.5852 4.6354
## 109 Perch 320.0 29.800000 7.6156 4.7716
## 110 Perch 514.0 32.433333 10.0300 6.0180
## 111 Perch 556.0 34.333333 10.2565 6.3875
## 112 Perch 840.0 34.933333 11.4884 7.7957
## 113 Perch 685.0 36.500000 10.8810 6.8640
## 114 Perch 700.0 36.100000 10.6091 6.7408
## 115 Perch 700.0 36.966667 10.8350 6.2646
## 116 Perch 690.0 36.966667 10.5717 6.3666
## 117 Perch 900.0 38.966667 11.1366 7.4934
## 118 Perch 650.0 38.966667 11.1366 6.0030
## 119 Perch 820.0 38.966667 12.4313 7.3514
## 120 Perch 850.0 39.733333 11.9286 7.1064
## 121 Perch 900.0 39.833333 11.7300 7.2250
## 122 Perch 1015.0 39.800000 12.3808 7.4624
## 123 Perch 820.0 39.866667 11.1350 6.6300
## 124 Perch 1100.0 41.866667 12.8002 6.8684
## 125 Perch 1000.0 42.666667 11.9328 7.2772
## 126 Perch 1100.0 42.866667 12.5125 7.4165
## 127 Perch 1000.0 43.233333 12.6040 8.1420
## 128 Perch 1000.0 43.900000 12.4888 7.5958
## 129 Pike 200.0 32.366667 5.5680 3.3756
## 130 Pike 300.0 34.500000 5.7078 4.1580
## 131 Pike 300.0 35.500000 5.9364 4.3844
## 132 Pike 300.0 37.300000 6.2884 4.0198
## 133 Pike 430.0 38.000000 7.2900 4.5765
## 134 Pike 345.0 38.500000 6.3960 3.9770
## 135 Pike 456.0 42.666667 7.2800 4.3225
## 136 Pike 510.0 42.666667 6.8250 4.4590
## 137 Pike 540.0 42.966667 7.7860 5.1296
## 138 Pike 500.0 45.000000 6.9600 4.8960
## 139 Pike 567.0 45.966667 7.7920 4.8700
## 140 Pike 770.0 48.000000 7.6800 5.3760
## 141 Pike 950.0 51.700000 8.9262 6.1712
## 142 Pike 1250.0 55.900000 10.6863 6.9849
## 143 Pike 1600.0 60.000000 9.6000 6.1440
## 144 Pike 1550.0 60.000000 9.6000 6.1440
## 145 Pike 1650.0 63.466667 10.8120 7.4800
## 146 Smelt 6.7 9.966667 1.7388 1.0476
## 147 Smelt 7.5 10.700000 1.9720 1.1600
## 148 Smelt 7.0 10.766667 1.7284 1.1484
## 149 Smelt 9.7 11.133333 2.1960 1.3800
## 150 Smelt 9.8 11.433333 2.0832 1.2772
## 151 Smelt 8.7 11.566667 1.9782 1.2852
## 152 Smelt 10.0 12.066667 2.2139 1.2838
## 153 Smelt 9.9 12.066667 2.2139 1.1659
## 154 Smelt 9.8 12.200000 2.2044 1.1484
## 155 Smelt 12.2 12.366667 2.0904 1.3936
## 156 Smelt 13.4 12.533333 2.4300 1.2690
## 157 Smelt 12.2 12.966667 2.2770 1.2558
## 158 Smelt 19.7 14.233333 2.8728 2.0672
## 159 Smelt 19.9 15.000000 2.9322 1.8792Statistika Deskriptif
hal ini dilakukan untuk mengetahui deskripsi sederhana data
## 'data.frame': 159 obs. of 5 variables:
## $ Species: chr "Bream" "Bream" "Bream" "Bream" ...
## $ Y : num 242 290 340 363 430 450 500 390 450 500 ...
## $ X1 : num 26.2 27.2 27.2 29.6 29.8 ...
## $ X2 : num 11.5 12.5 12.4 12.7 12.4 ...
## $ X3 : num 4.02 4.31 4.7 4.46 5.13 ...## Species Y X1 X2
## Length:159 Min. : 0.0 Min. : 8.233 Min. : 1.728
## Class :character 1st Qu.: 120.0 1st Qu.:20.983 1st Qu.: 5.945
## Mode :character Median : 273.0 Median :27.267 Median : 7.786
## Mean : 398.3 Mean :28.630 Mean : 8.971
## 3rd Qu.: 650.0 3rd Qu.:36.000 3rd Qu.:12.366
## Max. :1650.0 Max. :63.467 Max. :18.957
## X3
## Min. :1.048
## 1st Qu.:3.386
## Median :4.248
## Mean :4.417
## 3rd Qu.:5.585
## Max. :8.142Visualisasi Data
#Visualisasi variabel Independen (Length, Heigth, Width) dengan Dependen (Weight)
library(ggplot2)
#menggunakan point
ggplot(data=datafish)+geom_point(mapping=aes(x=Y, y=X1))
ggplot(data=datafish)+geom_point(mapping=aes(x=Y, y=X2))
ggplot(data=datafish)+geom_point(mapping=aes(x=Y, y=X3))
ggplot(data = datafish, aes(x = Y))+geom_histogram(col="Blue", fill="Pink")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = datafish, aes(x = X1))+geom_histogram(col="Red", fill="White")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = datafish, aes(x = X2))+geom_histogram(col="Green", fill="Yellow")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = datafish, aes(x = X3))+geom_histogram(col="Pink", fill="Black", size=2)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dalam visualisasi data, digunakan point dan histogram. semakin besar
panjang lebar dan tinggi ikan, maka berat ikan semakin besar
Analisis Korelasi
#analisis Korelasi
cor(datafish[,-1])## Y X1 X2 X3
## Y 1.0000000 0.9208174 0.7243453 0.8865066
## X1 0.9208174 1.0000000 0.6594806 0.8747570
## X2 0.7243453 0.6594806 1.0000000 0.7928810
## X3 0.8865066 0.8747570 0.7928810 1.0000000Berdasarkan matriks korelasi yang didapat, tidak ada nilai negatif maupun 0 pada nilai data korelasi, sehingga dapat disimpulkan semua variabel independen kepada dependen memiliki korelasi positif yang kuat. Jika nilai panjang lebar dan tinggi ikan semakin naik, maka berat ikan cenderung semakin naik.
Model Regresi
karena variabel independen (X) lebih dari satu, maka model regresi yang digunakan adalah model regresi berganda
#model regresi (berganda)
library(car)## Loading required package: carDatamodel <- lm(Y ~ X1 + X2 + X3, data = datafish)
vif(model)## X1 X2 X3
## 4.316500 2.729347 6.568623summary(model)##
## Call:
## lm(formula = Y ~ X1 + X2 + X3, data = datafish)
##
## Residuals:
## Min 1Q Median 3Q Max
## -249.43 -74.34 -31.97 77.72 444.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -517.976 28.865 -17.945 < 2e-16 ***
## X1 21.161 1.917 11.036 < 2e-16 ***
## X2 9.701 3.826 2.535 0.01223 *
## X3 50.579 15.092 3.351 0.00101 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 124.8 on 155 degrees of freedom
## Multiple R-squared: 0.8808, Adjusted R-squared: 0.8785
## F-statistic: 381.8 on 3 and 155 DF, p-value: < 2.2e-16Didapatkan p-value kecil (p-value <0.05), maka dapat disimpulkan bahwa koefisien model signifikan dan mempengaruhi variabel dependen (panjang lebar dan tinggi ikan mempengaruhi berat ikan). dan nilai VIF pada variabel < 10 sehingga asumsi terbukti.