#Mengambil dataset yang sudah ada dalam R
dataset <- airquality
dataset
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## 11 7 NA 6.9 74 5 11
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12.0 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12.0 61 5 24
## 25 NA 66 16.6 57 5 25
## 26 NA 266 14.9 58 5 26
## 27 NA NA 8.0 57 5 27
## 28 23 13 12.0 67 5 28
## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
## 32 NA 286 8.6 78 6 1
## 33 NA 287 9.7 74 6 2
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 36 NA 220 8.6 85 6 5
## 37 NA 264 14.3 79 6 6
## 38 29 127 9.7 82 6 7
## 39 NA 273 6.9 87 6 8
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 42 NA 259 10.9 93 6 11
## 43 NA 250 9.2 92 6 12
## 44 23 148 8.0 82 6 13
## 45 NA 332 13.8 80 6 14
## 46 NA 322 11.5 79 6 15
## 47 21 191 14.9 77 6 16
## 48 37 284 20.7 72 6 17
## 49 20 37 9.2 65 6 18
## 50 12 120 11.5 73 6 19
## 51 13 137 10.3 76 6 20
## 52 NA 150 6.3 77 6 21
## 53 NA 59 1.7 76 6 22
## 54 NA 91 4.6 76 6 23
## 55 NA 250 6.3 76 6 24
## 56 NA 135 8.0 75 6 25
## 57 NA 127 8.0 78 6 26
## 58 NA 47 10.3 73 6 27
## 59 NA 98 11.5 80 6 28
## 60 NA 31 14.9 77 6 29
## 61 NA 138 8.0 83 6 30
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 39 83 6.9 81 8 1
## 94 9 24 13.8 81 8 2
## 95 16 77 7.4 82 8 3
## 96 78 NA 6.9 86 8 4
## 97 35 NA 7.4 85 8 5
## 98 66 NA 4.6 87 8 6
## 99 122 255 4.0 89 8 7
## 100 89 229 10.3 90 8 8
## 101 110 207 8.0 90 8 9
## 102 NA 222 8.6 92 8 10
## 103 NA 137 11.5 86 8 11
## 104 44 192 11.5 86 8 12
## 105 28 273 11.5 82 8 13
## 106 65 157 9.7 80 8 14
## 107 NA 64 11.5 79 8 15
## 108 22 71 10.3 77 8 16
## 109 59 51 6.3 79 8 17
## 110 23 115 7.4 76 8 18
## 111 31 244 10.9 78 8 19
## 112 44 190 10.3 78 8 20
## 113 21 259 15.5 77 8 21
## 114 9 36 14.3 72 8 22
## 115 NA 255 12.6 75 8 23
## 116 45 212 9.7 79 8 24
## 117 168 238 3.4 81 8 25
## 118 73 215 8.0 86 8 26
## 119 NA 153 5.7 88 8 27
## 120 76 203 9.7 97 8 28
## 121 118 225 2.3 94 8 29
## 122 84 237 6.3 96 8 30
## 123 85 188 6.3 94 8 31
## 124 96 167 6.9 91 9 1
## 125 78 197 5.1 92 9 2
## 126 73 183 2.8 93 9 3
## 127 91 189 4.6 93 9 4
## 128 47 95 7.4 87 9 5
## 129 32 92 15.5 84 9 6
## 130 20 252 10.9 80 9 7
## 131 23 220 10.3 78 9 8
## 132 21 230 10.9 75 9 9
## 133 24 259 9.7 73 9 10
## 134 44 236 14.9 81 9 11
## 135 21 259 15.5 76 9 12
## 136 28 238 6.3 77 9 13
## 137 9 24 10.9 71 9 14
## 138 13 112 11.5 71 9 15
## 139 46 237 6.9 78 9 16
## 140 18 224 13.8 67 9 17
## 141 13 27 10.3 76 9 18
## 142 24 238 10.3 68 9 19
## 143 16 201 8.0 82 9 20
## 144 13 238 12.6 64 9 21
## 145 23 14 9.2 71 9 22
## 146 36 139 10.3 81 9 23
## 147 7 49 10.3 69 9 24
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 150 NA 145 13.2 77 9 27
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 30
#Mengecek Struktur data pada dataset airquality
summary (dataset)
## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
## Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## NA's :37 NA's :7
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
##
#Mengganti missing value yang ada dengan mengisi nya menggunakan nilai mean
dataset$Ozone[is.na(dataset$Ozone)]<-42.13
dataset$Solar.R[is.na(dataset$Solar.R)]<-185.9
datasetnew <- dataset
datasetnew
## Ozone Solar.R Wind Temp Month Day
## 1 41.00 190.0 7.4 67 5 1
## 2 36.00 118.0 8.0 72 5 2
## 3 12.00 149.0 12.6 74 5 3
## 4 18.00 313.0 11.5 62 5 4
## 5 42.13 185.9 14.3 56 5 5
## 6 28.00 185.9 14.9 66 5 6
## 7 23.00 299.0 8.6 65 5 7
## 8 19.00 99.0 13.8 59 5 8
## 9 8.00 19.0 20.1 61 5 9
## 10 42.13 194.0 8.6 69 5 10
## 11 7.00 185.9 6.9 74 5 11
## 12 16.00 256.0 9.7 69 5 12
## 13 11.00 290.0 9.2 66 5 13
## 14 14.00 274.0 10.9 68 5 14
## 15 18.00 65.0 13.2 58 5 15
## 16 14.00 334.0 11.5 64 5 16
## 17 34.00 307.0 12.0 66 5 17
## 18 6.00 78.0 18.4 57 5 18
## 19 30.00 322.0 11.5 68 5 19
## 20 11.00 44.0 9.7 62 5 20
## 21 1.00 8.0 9.7 59 5 21
## 22 11.00 320.0 16.6 73 5 22
## 23 4.00 25.0 9.7 61 5 23
## 24 32.00 92.0 12.0 61 5 24
## 25 42.13 66.0 16.6 57 5 25
## 26 42.13 266.0 14.9 58 5 26
## 27 42.13 185.9 8.0 57 5 27
## 28 23.00 13.0 12.0 67 5 28
## 29 45.00 252.0 14.9 81 5 29
## 30 115.00 223.0 5.7 79 5 30
## 31 37.00 279.0 7.4 76 5 31
## 32 42.13 286.0 8.6 78 6 1
## 33 42.13 287.0 9.7 74 6 2
## 34 42.13 242.0 16.1 67 6 3
## 35 42.13 186.0 9.2 84 6 4
## 36 42.13 220.0 8.6 85 6 5
## 37 42.13 264.0 14.3 79 6 6
## 38 29.00 127.0 9.7 82 6 7
## 39 42.13 273.0 6.9 87 6 8
## 40 71.00 291.0 13.8 90 6 9
## 41 39.00 323.0 11.5 87 6 10
## 42 42.13 259.0 10.9 93 6 11
## 43 42.13 250.0 9.2 92 6 12
## 44 23.00 148.0 8.0 82 6 13
## 45 42.13 332.0 13.8 80 6 14
## 46 42.13 322.0 11.5 79 6 15
## 47 21.00 191.0 14.9 77 6 16
## 48 37.00 284.0 20.7 72 6 17
## 49 20.00 37.0 9.2 65 6 18
## 50 12.00 120.0 11.5 73 6 19
## 51 13.00 137.0 10.3 76 6 20
## 52 42.13 150.0 6.3 77 6 21
## 53 42.13 59.0 1.7 76 6 22
## 54 42.13 91.0 4.6 76 6 23
## 55 42.13 250.0 6.3 76 6 24
## 56 42.13 135.0 8.0 75 6 25
## 57 42.13 127.0 8.0 78 6 26
## 58 42.13 47.0 10.3 73 6 27
## 59 42.13 98.0 11.5 80 6 28
## 60 42.13 31.0 14.9 77 6 29
## 61 42.13 138.0 8.0 83 6 30
## 62 135.00 269.0 4.1 84 7 1
## 63 49.00 248.0 9.2 85 7 2
## 64 32.00 236.0 9.2 81 7 3
## 65 42.13 101.0 10.9 84 7 4
## 66 64.00 175.0 4.6 83 7 5
## 67 40.00 314.0 10.9 83 7 6
## 68 77.00 276.0 5.1 88 7 7
## 69 97.00 267.0 6.3 92 7 8
## 70 97.00 272.0 5.7 92 7 9
## 71 85.00 175.0 7.4 89 7 10
## 72 42.13 139.0 8.6 82 7 11
## 73 10.00 264.0 14.3 73 7 12
## 74 27.00 175.0 14.9 81 7 13
## 75 42.13 291.0 14.9 91 7 14
## 76 7.00 48.0 14.3 80 7 15
## 77 48.00 260.0 6.9 81 7 16
## 78 35.00 274.0 10.3 82 7 17
## 79 61.00 285.0 6.3 84 7 18
## 80 79.00 187.0 5.1 87 7 19
## 81 63.00 220.0 11.5 85 7 20
## 82 16.00 7.0 6.9 74 7 21
## 83 42.13 258.0 9.7 81 7 22
## 84 42.13 295.0 11.5 82 7 23
## 85 80.00 294.0 8.6 86 7 24
## 86 108.00 223.0 8.0 85 7 25
## 87 20.00 81.0 8.6 82 7 26
## 88 52.00 82.0 12.0 86 7 27
## 89 82.00 213.0 7.4 88 7 28
## 90 50.00 275.0 7.4 86 7 29
## 91 64.00 253.0 7.4 83 7 30
## 92 59.00 254.0 9.2 81 7 31
## 93 39.00 83.0 6.9 81 8 1
## 94 9.00 24.0 13.8 81 8 2
## 95 16.00 77.0 7.4 82 8 3
## 96 78.00 185.9 6.9 86 8 4
## 97 35.00 185.9 7.4 85 8 5
## 98 66.00 185.9 4.6 87 8 6
## 99 122.00 255.0 4.0 89 8 7
## 100 89.00 229.0 10.3 90 8 8
## 101 110.00 207.0 8.0 90 8 9
## 102 42.13 222.0 8.6 92 8 10
## 103 42.13 137.0 11.5 86 8 11
## 104 44.00 192.0 11.5 86 8 12
## 105 28.00 273.0 11.5 82 8 13
## 106 65.00 157.0 9.7 80 8 14
## 107 42.13 64.0 11.5 79 8 15
## 108 22.00 71.0 10.3 77 8 16
## 109 59.00 51.0 6.3 79 8 17
## 110 23.00 115.0 7.4 76 8 18
## 111 31.00 244.0 10.9 78 8 19
## 112 44.00 190.0 10.3 78 8 20
## 113 21.00 259.0 15.5 77 8 21
## 114 9.00 36.0 14.3 72 8 22
## 115 42.13 255.0 12.6 75 8 23
## 116 45.00 212.0 9.7 79 8 24
## 117 168.00 238.0 3.4 81 8 25
## 118 73.00 215.0 8.0 86 8 26
## 119 42.13 153.0 5.7 88 8 27
## 120 76.00 203.0 9.7 97 8 28
## 121 118.00 225.0 2.3 94 8 29
## 122 84.00 237.0 6.3 96 8 30
## 123 85.00 188.0 6.3 94 8 31
## 124 96.00 167.0 6.9 91 9 1
## 125 78.00 197.0 5.1 92 9 2
## 126 73.00 183.0 2.8 93 9 3
## 127 91.00 189.0 4.6 93 9 4
## 128 47.00 95.0 7.4 87 9 5
## 129 32.00 92.0 15.5 84 9 6
## 130 20.00 252.0 10.9 80 9 7
## 131 23.00 220.0 10.3 78 9 8
## 132 21.00 230.0 10.9 75 9 9
## 133 24.00 259.0 9.7 73 9 10
## 134 44.00 236.0 14.9 81 9 11
## 135 21.00 259.0 15.5 76 9 12
## 136 28.00 238.0 6.3 77 9 13
## 137 9.00 24.0 10.9 71 9 14
## 138 13.00 112.0 11.5 71 9 15
## 139 46.00 237.0 6.9 78 9 16
## 140 18.00 224.0 13.8 67 9 17
## 141 13.00 27.0 10.3 76 9 18
## 142 24.00 238.0 10.3 68 9 19
## 143 16.00 201.0 8.0 82 9 20
## 144 13.00 238.0 12.6 64 9 21
## 145 23.00 14.0 9.2 71 9 22
## 146 36.00 139.0 10.3 81 9 23
## 147 7.00 49.0 10.3 69 9 24
## 148 14.00 20.0 16.6 63 9 25
## 149 30.00 193.0 6.9 70 9 26
## 150 42.13 145.0 13.2 77 9 27
## 151 14.00 191.0 14.3 75 9 28
## 152 18.00 131.0 8.0 76 9 29
## 153 20.00 223.0 11.5 68 9 30
summary (datasetnew)
## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 21.00 1st Qu.:120.0 1st Qu.: 7.400 1st Qu.:72.00
## Median : 42.13 Median :194.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 46.00 3rd Qu.:256.0 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
#Split data by coloums
data1 <- datasetnew [, c("Ozone","Solar.R")]#Mencari hubungan antara konsentrasi ozon dalam udara dan radiasi matahari.
data1
## Ozone Solar.R
## 1 41.00 190.0
## 2 36.00 118.0
## 3 12.00 149.0
## 4 18.00 313.0
## 5 42.13 185.9
## 6 28.00 185.9
## 7 23.00 299.0
## 8 19.00 99.0
## 9 8.00 19.0
## 10 42.13 194.0
## 11 7.00 185.9
## 12 16.00 256.0
## 13 11.00 290.0
## 14 14.00 274.0
## 15 18.00 65.0
## 16 14.00 334.0
## 17 34.00 307.0
## 18 6.00 78.0
## 19 30.00 322.0
## 20 11.00 44.0
## 21 1.00 8.0
## 22 11.00 320.0
## 23 4.00 25.0
## 24 32.00 92.0
## 25 42.13 66.0
## 26 42.13 266.0
## 27 42.13 185.9
## 28 23.00 13.0
## 29 45.00 252.0
## 30 115.00 223.0
## 31 37.00 279.0
## 32 42.13 286.0
## 33 42.13 287.0
## 34 42.13 242.0
## 35 42.13 186.0
## 36 42.13 220.0
## 37 42.13 264.0
## 38 29.00 127.0
## 39 42.13 273.0
## 40 71.00 291.0
## 41 39.00 323.0
## 42 42.13 259.0
## 43 42.13 250.0
## 44 23.00 148.0
## 45 42.13 332.0
## 46 42.13 322.0
## 47 21.00 191.0
## 48 37.00 284.0
## 49 20.00 37.0
## 50 12.00 120.0
## 51 13.00 137.0
## 52 42.13 150.0
## 53 42.13 59.0
## 54 42.13 91.0
## 55 42.13 250.0
## 56 42.13 135.0
## 57 42.13 127.0
## 58 42.13 47.0
## 59 42.13 98.0
## 60 42.13 31.0
## 61 42.13 138.0
## 62 135.00 269.0
## 63 49.00 248.0
## 64 32.00 236.0
## 65 42.13 101.0
## 66 64.00 175.0
## 67 40.00 314.0
## 68 77.00 276.0
## 69 97.00 267.0
## 70 97.00 272.0
## 71 85.00 175.0
## 72 42.13 139.0
## 73 10.00 264.0
## 74 27.00 175.0
## 75 42.13 291.0
## 76 7.00 48.0
## 77 48.00 260.0
## 78 35.00 274.0
## 79 61.00 285.0
## 80 79.00 187.0
## 81 63.00 220.0
## 82 16.00 7.0
## 83 42.13 258.0
## 84 42.13 295.0
## 85 80.00 294.0
## 86 108.00 223.0
## 87 20.00 81.0
## 88 52.00 82.0
## 89 82.00 213.0
## 90 50.00 275.0
## 91 64.00 253.0
## 92 59.00 254.0
## 93 39.00 83.0
## 94 9.00 24.0
## 95 16.00 77.0
## 96 78.00 185.9
## 97 35.00 185.9
## 98 66.00 185.9
## 99 122.00 255.0
## 100 89.00 229.0
## 101 110.00 207.0
## 102 42.13 222.0
## 103 42.13 137.0
## 104 44.00 192.0
## 105 28.00 273.0
## 106 65.00 157.0
## 107 42.13 64.0
## 108 22.00 71.0
## 109 59.00 51.0
## 110 23.00 115.0
## 111 31.00 244.0
## 112 44.00 190.0
## 113 21.00 259.0
## 114 9.00 36.0
## 115 42.13 255.0
## 116 45.00 212.0
## 117 168.00 238.0
## 118 73.00 215.0
## 119 42.13 153.0
## 120 76.00 203.0
## 121 118.00 225.0
## 122 84.00 237.0
## 123 85.00 188.0
## 124 96.00 167.0
## 125 78.00 197.0
## 126 73.00 183.0
## 127 91.00 189.0
## 128 47.00 95.0
## 129 32.00 92.0
## 130 20.00 252.0
## 131 23.00 220.0
## 132 21.00 230.0
## 133 24.00 259.0
## 134 44.00 236.0
## 135 21.00 259.0
## 136 28.00 238.0
## 137 9.00 24.0
## 138 13.00 112.0
## 139 46.00 237.0
## 140 18.00 224.0
## 141 13.00 27.0
## 142 24.00 238.0
## 143 16.00 201.0
## 144 13.00 238.0
## 145 23.00 14.0
## 146 36.00 139.0
## 147 7.00 49.0
## 148 14.00 20.0
## 149 30.00 193.0
## 150 42.13 145.0
## 151 14.00 191.0
## 152 18.00 131.0
## 153 20.00 223.0
data2 <- datasetnew [, c("Ozone","Wind")]#Mencari hubungan antara konsentrasi ozon dalam udara dan Angin.
data2
## Ozone Wind
## 1 41.00 7.4
## 2 36.00 8.0
## 3 12.00 12.6
## 4 18.00 11.5
## 5 42.13 14.3
## 6 28.00 14.9
## 7 23.00 8.6
## 8 19.00 13.8
## 9 8.00 20.1
## 10 42.13 8.6
## 11 7.00 6.9
## 12 16.00 9.7
## 13 11.00 9.2
## 14 14.00 10.9
## 15 18.00 13.2
## 16 14.00 11.5
## 17 34.00 12.0
## 18 6.00 18.4
## 19 30.00 11.5
## 20 11.00 9.7
## 21 1.00 9.7
## 22 11.00 16.6
## 23 4.00 9.7
## 24 32.00 12.0
## 25 42.13 16.6
## 26 42.13 14.9
## 27 42.13 8.0
## 28 23.00 12.0
## 29 45.00 14.9
## 30 115.00 5.7
## 31 37.00 7.4
## 32 42.13 8.6
## 33 42.13 9.7
## 34 42.13 16.1
## 35 42.13 9.2
## 36 42.13 8.6
## 37 42.13 14.3
## 38 29.00 9.7
## 39 42.13 6.9
## 40 71.00 13.8
## 41 39.00 11.5
## 42 42.13 10.9
## 43 42.13 9.2
## 44 23.00 8.0
## 45 42.13 13.8
## 46 42.13 11.5
## 47 21.00 14.9
## 48 37.00 20.7
## 49 20.00 9.2
## 50 12.00 11.5
## 51 13.00 10.3
## 52 42.13 6.3
## 53 42.13 1.7
## 54 42.13 4.6
## 55 42.13 6.3
## 56 42.13 8.0
## 57 42.13 8.0
## 58 42.13 10.3
## 59 42.13 11.5
## 60 42.13 14.9
## 61 42.13 8.0
## 62 135.00 4.1
## 63 49.00 9.2
## 64 32.00 9.2
## 65 42.13 10.9
## 66 64.00 4.6
## 67 40.00 10.9
## 68 77.00 5.1
## 69 97.00 6.3
## 70 97.00 5.7
## 71 85.00 7.4
## 72 42.13 8.6
## 73 10.00 14.3
## 74 27.00 14.9
## 75 42.13 14.9
## 76 7.00 14.3
## 77 48.00 6.9
## 78 35.00 10.3
## 79 61.00 6.3
## 80 79.00 5.1
## 81 63.00 11.5
## 82 16.00 6.9
## 83 42.13 9.7
## 84 42.13 11.5
## 85 80.00 8.6
## 86 108.00 8.0
## 87 20.00 8.6
## 88 52.00 12.0
## 89 82.00 7.4
## 90 50.00 7.4
## 91 64.00 7.4
## 92 59.00 9.2
## 93 39.00 6.9
## 94 9.00 13.8
## 95 16.00 7.4
## 96 78.00 6.9
## 97 35.00 7.4
## 98 66.00 4.6
## 99 122.00 4.0
## 100 89.00 10.3
## 101 110.00 8.0
## 102 42.13 8.6
## 103 42.13 11.5
## 104 44.00 11.5
## 105 28.00 11.5
## 106 65.00 9.7
## 107 42.13 11.5
## 108 22.00 10.3
## 109 59.00 6.3
## 110 23.00 7.4
## 111 31.00 10.9
## 112 44.00 10.3
## 113 21.00 15.5
## 114 9.00 14.3
## 115 42.13 12.6
## 116 45.00 9.7
## 117 168.00 3.4
## 118 73.00 8.0
## 119 42.13 5.7
## 120 76.00 9.7
## 121 118.00 2.3
## 122 84.00 6.3
## 123 85.00 6.3
## 124 96.00 6.9
## 125 78.00 5.1
## 126 73.00 2.8
## 127 91.00 4.6
## 128 47.00 7.4
## 129 32.00 15.5
## 130 20.00 10.9
## 131 23.00 10.3
## 132 21.00 10.9
## 133 24.00 9.7
## 134 44.00 14.9
## 135 21.00 15.5
## 136 28.00 6.3
## 137 9.00 10.9
## 138 13.00 11.5
## 139 46.00 6.9
## 140 18.00 13.8
## 141 13.00 10.3
## 142 24.00 10.3
## 143 16.00 8.0
## 144 13.00 12.6
## 145 23.00 9.2
## 146 36.00 10.3
## 147 7.00 10.3
## 148 14.00 16.6
## 149 30.00 6.9
## 150 42.13 13.2
## 151 14.00 14.3
## 152 18.00 8.0
## 153 20.00 11.5
data3 <- datasetnew [, c("Ozone","Temp")]#Mencari hubungan antara konsentrasi ozon dalam udara dan temperatur
data3
## Ozone Temp
## 1 41.00 67
## 2 36.00 72
## 3 12.00 74
## 4 18.00 62
## 5 42.13 56
## 6 28.00 66
## 7 23.00 65
## 8 19.00 59
## 9 8.00 61
## 10 42.13 69
## 11 7.00 74
## 12 16.00 69
## 13 11.00 66
## 14 14.00 68
## 15 18.00 58
## 16 14.00 64
## 17 34.00 66
## 18 6.00 57
## 19 30.00 68
## 20 11.00 62
## 21 1.00 59
## 22 11.00 73
## 23 4.00 61
## 24 32.00 61
## 25 42.13 57
## 26 42.13 58
## 27 42.13 57
## 28 23.00 67
## 29 45.00 81
## 30 115.00 79
## 31 37.00 76
## 32 42.13 78
## 33 42.13 74
## 34 42.13 67
## 35 42.13 84
## 36 42.13 85
## 37 42.13 79
## 38 29.00 82
## 39 42.13 87
## 40 71.00 90
## 41 39.00 87
## 42 42.13 93
## 43 42.13 92
## 44 23.00 82
## 45 42.13 80
## 46 42.13 79
## 47 21.00 77
## 48 37.00 72
## 49 20.00 65
## 50 12.00 73
## 51 13.00 76
## 52 42.13 77
## 53 42.13 76
## 54 42.13 76
## 55 42.13 76
## 56 42.13 75
## 57 42.13 78
## 58 42.13 73
## 59 42.13 80
## 60 42.13 77
## 61 42.13 83
## 62 135.00 84
## 63 49.00 85
## 64 32.00 81
## 65 42.13 84
## 66 64.00 83
## 67 40.00 83
## 68 77.00 88
## 69 97.00 92
## 70 97.00 92
## 71 85.00 89
## 72 42.13 82
## 73 10.00 73
## 74 27.00 81
## 75 42.13 91
## 76 7.00 80
## 77 48.00 81
## 78 35.00 82
## 79 61.00 84
## 80 79.00 87
## 81 63.00 85
## 82 16.00 74
## 83 42.13 81
## 84 42.13 82
## 85 80.00 86
## 86 108.00 85
## 87 20.00 82
## 88 52.00 86
## 89 82.00 88
## 90 50.00 86
## 91 64.00 83
## 92 59.00 81
## 93 39.00 81
## 94 9.00 81
## 95 16.00 82
## 96 78.00 86
## 97 35.00 85
## 98 66.00 87
## 99 122.00 89
## 100 89.00 90
## 101 110.00 90
## 102 42.13 92
## 103 42.13 86
## 104 44.00 86
## 105 28.00 82
## 106 65.00 80
## 107 42.13 79
## 108 22.00 77
## 109 59.00 79
## 110 23.00 76
## 111 31.00 78
## 112 44.00 78
## 113 21.00 77
## 114 9.00 72
## 115 42.13 75
## 116 45.00 79
## 117 168.00 81
## 118 73.00 86
## 119 42.13 88
## 120 76.00 97
## 121 118.00 94
## 122 84.00 96
## 123 85.00 94
## 124 96.00 91
## 125 78.00 92
## 126 73.00 93
## 127 91.00 93
## 128 47.00 87
## 129 32.00 84
## 130 20.00 80
## 131 23.00 78
## 132 21.00 75
## 133 24.00 73
## 134 44.00 81
## 135 21.00 76
## 136 28.00 77
## 137 9.00 71
## 138 13.00 71
## 139 46.00 78
## 140 18.00 67
## 141 13.00 76
## 142 24.00 68
## 143 16.00 82
## 144 13.00 64
## 145 23.00 71
## 146 36.00 81
## 147 7.00 69
## 148 14.00 63
## 149 30.00 70
## 150 42.13 77
## 151 14.00 75
## 152 18.00 76
## 153 20.00 68
data4 <- datasetnew [, c("Wind","Temp")]#Mencari hubungan antara kecepatan angin dan temperatur.
data4
## Wind Temp
## 1 7.4 67
## 2 8.0 72
## 3 12.6 74
## 4 11.5 62
## 5 14.3 56
## 6 14.9 66
## 7 8.6 65
## 8 13.8 59
## 9 20.1 61
## 10 8.6 69
## 11 6.9 74
## 12 9.7 69
## 13 9.2 66
## 14 10.9 68
## 15 13.2 58
## 16 11.5 64
## 17 12.0 66
## 18 18.4 57
## 19 11.5 68
## 20 9.7 62
## 21 9.7 59
## 22 16.6 73
## 23 9.7 61
## 24 12.0 61
## 25 16.6 57
## 26 14.9 58
## 27 8.0 57
## 28 12.0 67
## 29 14.9 81
## 30 5.7 79
## 31 7.4 76
## 32 8.6 78
## 33 9.7 74
## 34 16.1 67
## 35 9.2 84
## 36 8.6 85
## 37 14.3 79
## 38 9.7 82
## 39 6.9 87
## 40 13.8 90
## 41 11.5 87
## 42 10.9 93
## 43 9.2 92
## 44 8.0 82
## 45 13.8 80
## 46 11.5 79
## 47 14.9 77
## 48 20.7 72
## 49 9.2 65
## 50 11.5 73
## 51 10.3 76
## 52 6.3 77
## 53 1.7 76
## 54 4.6 76
## 55 6.3 76
## 56 8.0 75
## 57 8.0 78
## 58 10.3 73
## 59 11.5 80
## 60 14.9 77
## 61 8.0 83
## 62 4.1 84
## 63 9.2 85
## 64 9.2 81
## 65 10.9 84
## 66 4.6 83
## 67 10.9 83
## 68 5.1 88
## 69 6.3 92
## 70 5.7 92
## 71 7.4 89
## 72 8.6 82
## 73 14.3 73
## 74 14.9 81
## 75 14.9 91
## 76 14.3 80
## 77 6.9 81
## 78 10.3 82
## 79 6.3 84
## 80 5.1 87
## 81 11.5 85
## 82 6.9 74
## 83 9.7 81
## 84 11.5 82
## 85 8.6 86
## 86 8.0 85
## 87 8.6 82
## 88 12.0 86
## 89 7.4 88
## 90 7.4 86
## 91 7.4 83
## 92 9.2 81
## 93 6.9 81
## 94 13.8 81
## 95 7.4 82
## 96 6.9 86
## 97 7.4 85
## 98 4.6 87
## 99 4.0 89
## 100 10.3 90
## 101 8.0 90
## 102 8.6 92
## 103 11.5 86
## 104 11.5 86
## 105 11.5 82
## 106 9.7 80
## 107 11.5 79
## 108 10.3 77
## 109 6.3 79
## 110 7.4 76
## 111 10.9 78
## 112 10.3 78
## 113 15.5 77
## 114 14.3 72
## 115 12.6 75
## 116 9.7 79
## 117 3.4 81
## 118 8.0 86
## 119 5.7 88
## 120 9.7 97
## 121 2.3 94
## 122 6.3 96
## 123 6.3 94
## 124 6.9 91
## 125 5.1 92
## 126 2.8 93
## 127 4.6 93
## 128 7.4 87
## 129 15.5 84
## 130 10.9 80
## 131 10.3 78
## 132 10.9 75
## 133 9.7 73
## 134 14.9 81
## 135 15.5 76
## 136 6.3 77
## 137 10.9 71
## 138 11.5 71
## 139 6.9 78
## 140 13.8 67
## 141 10.3 76
## 142 10.3 68
## 143 8.0 82
## 144 12.6 64
## 145 9.2 71
## 146 10.3 81
## 147 10.3 69
## 148 16.6 63
## 149 6.9 70
## 150 13.2 77
## 151 14.3 75
## 152 8.0 76
## 153 11.5 68
library(ggplot2)
ggplot(data1, aes(Ozone,Solar.R))+ geom_point()+ggtitle("plot data 1")+xlab("Ozone")+ylab("Solar.R")
ggplot(data2, aes(Ozone,Wind))+ geom_point()+ggtitle("plot data 2")+xlab("Ozone")+ylab("Wind")
ggplot(data3, aes(Ozone, Temp))+ geom_point()+ggtitle("plot data 3")+xlab("Ozone")+ylab("Temp")
ggplot(data4, aes(Wind, Temp))+ geom_point()+ggtitle("plot data 3")+xlab("Wind")+ylab("Temp")
#Nilai Korelasi
cor(data1$Ozone, data1$Solar.R)
## [1] 0.3029695
cor(data2$Ozone, data2$Wind)
## [1] -0.5309353
cor(data3$Ozone, data3$Temp)
## [1] 0.608742
cor(data4$Wind, data4$Temp)
## [1] -0.4579879
#Regresi
model1 <- lm(Solar.R ~ Ozone, data = data1)
model1
##
## Call:
## lm(formula = Solar.R ~ Ozone, data = data1)
##
## Coefficients:
## (Intercept) Ozone
## 146.8019 0.9288
summary(model1)
##
## Call:
## lm(formula = Solar.R ~ Ozone, data = data1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -155.163 -58.931 0.069 68.618 174.195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 146.8019 12.1059 12.127 < 2e-16 ***
## Ozone 0.9288 0.2377 3.907 0.000141 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 84.1 on 151 degrees of freedom
## Multiple R-squared: 0.09179, Adjusted R-squared: 0.08578
## F-statistic: 15.26 on 1 and 151 DF, p-value: 0.0001409
model2 <- lm(Wind ~ Ozone, data = data2)
model2
##
## Call:
## lm(formula = Wind ~ Ozone, data = data2)
##
## Coefficients:
## (Intercept) Ozone
## 12.70389 -0.06519
summary(model2)
##
## Call:
## lm(formula = Wind ~ Ozone, data = data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.2575 -2.2001 -0.2912 1.6644 10.4081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.703886 0.431145 29.465 < 2e-16 ***
## Ozone -0.065189 0.008467 -7.699 1.67e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.995 on 151 degrees of freedom
## Multiple R-squared: 0.2819, Adjusted R-squared: 0.2771
## F-statistic: 59.27 on 1 and 151 DF, p-value: 1.667e-12
model3 <- lm(Temp ~ Ozone, data = data3)
model3
##
## Call:
## lm(formula = Temp ~ Ozone, data = data3)
##
## Coefficients:
## (Intercept) Ozone
## 69.4223 0.2008
summary(model3)
##
## Call:
## lm(formula = Temp ~ Ozone, data = data3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.158 -4.234 1.369 5.117 15.117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.4223 1.0845 64.014 <2e-16 ***
## Ozone 0.2008 0.0213 9.429 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.534 on 151 degrees of freedom
## Multiple R-squared: 0.3706, Adjusted R-squared: 0.3664
## F-statistic: 88.9 on 1 and 151 DF, p-value: < 2.2e-16
model4 <- lm(Temp ~ Wind, data = data4)
model4
##
## Call:
## lm(formula = Temp ~ Wind, data = data4)
##
## Coefficients:
## (Intercept) Wind
## 90.13 -1.23
summary(model4)
##
## Call:
## lm(formula = Temp ~ Wind, data = data4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.291 -5.723 1.709 6.016 19.199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 90.1349 2.0522 43.921 < 2e-16 ***
## Wind -1.2305 0.1944 -6.331 2.64e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.442 on 151 degrees of freedom
## Multiple R-squared: 0.2098, Adjusted R-squared: 0.2045
## F-statistic: 40.08 on 1 and 151 DF, p-value: 2.642e-09
#Menampilkan plotnya garis regresi
ggplot(data1, aes(Ozone, Solar.R))+ geom_point()+ggtitle("plot data 1")+xlab("Ozone")+ylab("Solar.R") + geom_smooth(method = "lm", se= FALSE)
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data2, aes(Ozone, Wind))+ geom_point()+ggtitle("plot data 2")+xlab("Ozone")+ylab("Wind")+ geom_smooth(method = "lm", se= FALSE)
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data3, aes(Ozone, Temp))+ geom_point()+ggtitle("plot data 3")+xlab("Ozone")+ylab("Temp")+ geom_smooth(method = "lm", se= FALSE)
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data4, aes(Wind, Temp))+ geom_point()+ggtitle("plot data 4")+xlab("Wind")+ylab("Temp")+ geom_smooth(method = "lm", se= FALSE)
## `geom_smooth()` using formula = 'y ~ x'
#Prediksi
data_baru1 <- data.frame (Ozone=c(12,24,30,39))
data_baru1
## Ozone
## 1 12
## 2 24
## 3 30
## 4 39
hasil_prediksi1 <- predict(model1, data_baru1)
hasil_prediksi1
## 1 2 3 4
## 157.9470 169.0921 174.6647 183.0235
data_baru2 <- data.frame (Ozone=c(12,24,30,39))
data_baru2
## Ozone
## 1 12
## 2 24
## 3 30
## 4 39
hasil_prediksi2 <- predict(model2, data_baru2)
hasil_prediksi2
## 1 2 3 4
## 11.92162 11.13935 10.74822 10.16152
data_baru3 <- data.frame (Ozone=c(12,24,30,39))
data_baru3
## Ozone
## 1 12
## 2 24
## 3 30
## 4 39
hasil_prediksi3 <- predict(model3, data_baru3)
hasil_prediksi3
## 1 2 3 4
## 71.83206 74.24178 75.44664 77.25392
data_baru4 <- data.frame (Wind=c(12,24,30,39))
data_baru4
## Wind
## 1 12
## 2 24
## 3 30
## 4 39
hasil_prediksi4 <- predict(model4, data_baru4)
hasil_prediksi4
## 1 2 3 4
## 75.36912 60.60337 53.22050 42.14619