Start Up Funding
Naufal Harith,Ibrahim Frosly Alesandro,Alisha Deana Tabina
2025-05-19
Persiapan Paket & Data
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library(tidyr)
library(dplyr)
df <- read.csv("C:\\KULIAH\\Andro sems 4\\ANMUL\\startup_data.csv")
glimpse(df)## Rows: 500
## Columns: 12
## $ Startup.Name <chr> "Startup_1", "Startup_2", "Startup_3", "Startup…
## $ Industry <chr> "IoT", "EdTech", "EdTech", "Gaming", "IoT", "AI…
## $ Funding.Rounds <int> 1, 1, 1, 5, 4, 5, 4, 5, 5, 3, 5, 2, 3, 5, 1, 2,…
## $ Funding.Amount..M.USD. <dbl> 101.09, 247.62, 109.24, 10.75, 249.28, 103.89, …
## $ Valuation..M.USD. <dbl> 844.75, 3310.83, 1059.37, 101.90, 850.11, 1541.…
## $ Revenue..M.USD. <dbl> 67.87, 75.65, 84.21, 47.08, 50.25, 12.56, 38.60…
## $ Employees <int> 1468, 3280, 4933, 1059, 1905, 1462, 1404, 3420,…
## $ Market.Share.... <dbl> 5.20, 8.10, 2.61, 2.53, 4.09, 8.96, 0.10, 9.02,…
## $ Profitable <int> 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1,…
## $ Year.Founded <int> 2006, 2003, 1995, 2003, 1997, 2004, 2016, 2002,…
## $ Region <chr> "Europe", "South America", "South America", "So…
## $ Exit.Status <chr> "Private", "Private", "Private", "Private", "Ac…summary(df)## Startup.Name Industry Funding.Rounds Funding.Amount..M.USD.
## Length:500 Length:500 Min. :1.000 Min. : 0.57
## Class :character Class :character 1st Qu.:2.000 1st Qu.: 79.21
## Mode :character Mode :character Median :3.000 Median :156.00
## Mean :2.958 Mean :152.66
## 3rd Qu.:4.000 3rd Qu.:226.45
## Max. :5.000 Max. :299.81
## Valuation..M.USD. Revenue..M.USD. Employees Market.Share....
## Min. : 2.43 Min. : 0.12 Min. : 12 Min. : 0.100
## 1st Qu.: 557.03 1st Qu.:22.80 1st Qu.:1383 1st Qu.: 2.760
## Median :1222.58 Median :48.80 Median :2496 Median : 5.135
## Mean :1371.81 Mean :49.32 Mean :2532 Mean : 5.093
## 3rd Qu.:2052.09 3rd Qu.:74.97 3rd Qu.:3709 3rd Qu.: 7.553
## Max. :4357.49 Max. :99.71 Max. :4984 Max. :10.000
## Profitable Year.Founded Region Exit.Status
## Min. :0.000 Min. :1990 Length:500 Length:500
## 1st Qu.:0.000 1st Qu.:1998 Class :character Class :character
## Median :0.000 Median :2006 Mode :character Mode :character
## Mean :0.432 Mean :2006
## 3rd Qu.:1.000 3rd Qu.:2014
## Max. :1.000 Max. :2022sapply(df, function(x) sum(is.na(x)))## Startup.Name Industry Funding.Rounds
## 0 0 0
## Funding.Amount..M.USD. Valuation..M.USD. Revenue..M.USD.
## 0 0 0
## Employees Market.Share.... Profitable
## 0 0 0
## Year.Founded Region Exit.Status
## 0 0 0Praproses Data
df$Startup.Age <- 2025 - as.numeric(df$Year.Founded)
df## Startup.Name Industry Funding.Rounds Funding.Amount..M.USD.
## 1 Startup_1 IoT 1 101.09
## 2 Startup_2 EdTech 1 247.62
## 3 Startup_3 EdTech 1 109.24
## 4 Startup_4 Gaming 5 10.75
## 5 Startup_5 IoT 4 249.28
## 6 Startup_6 AI 5 103.89
## 7 Startup_7 Gaming 4 232.26
## 8 Startup_8 HealthTech 5 109.15
## 9 Startup_9 Gaming 5 258.39
## 10 Startup_10 FinTech 3 66.24
## 11 Startup_11 AI 5 292.38
## 12 Startup_12 FinTech 2 234.04
## 13 Startup_13 IoT 3 34.71
## 14 Startup_14 Cybersecurity 5 169.95
## 15 Startup_15 FinTech 1 295.62
## 16 Startup_16 HealthTech 2 141.58
## 17 Startup_17 Gaming 2 55.04
## 18 Startup_18 Cybersecurity 2 145.69
## 19 Startup_19 Gaming 3 153.98
## 20 Startup_20 FinTech 5 222.64
## 21 Startup_21 Gaming 5 209.80
## 22 Startup_22 IoT 1 121.06
## 23 Startup_23 Cybersecurity 1 65.80
## 24 Startup_24 IoT 2 193.69
## 25 Startup_25 FinTech 1 126.80
## 26 Startup_26 IoT 3 40.12
## 27 Startup_27 HealthTech 5 270.22
## 28 Startup_28 FinTech 2 207.52
## 29 Startup_29 FinTech 1 205.11
## 30 Startup_30 FinTech 3 246.86
## 31 Startup_31 AI 3 159.13
## 32 Startup_32 Gaming 1 244.61
## 33 Startup_33 AI 5 149.68
## 34 Startup_34 AI 1 20.62
## 35 Startup_35 HealthTech 2 121.87
## 36 Startup_36 Cybersecurity 1 149.49
## 37 Startup_37 Gaming 3 216.20
## 38 Startup_38 IoT 1 32.42
## 39 Startup_39 EdTech 5 42.44
## 40 Startup_40 FinTech 4 79.47
## 41 Startup_41 IoT 1 80.32
## 42 Startup_42 FinTech 5 219.79
## 43 Startup_43 HealthTech 5 75.59
## 44 Startup_44 FinTech 3 190.11
## 45 Startup_45 AI 5 148.54
## 46 Startup_46 Gaming 5 172.20
## 47 Startup_47 EdTech 5 251.51
## 48 Startup_48 E-Commerce 5 121.61
## 49 Startup_49 FinTech 2 231.39
## 50 Startup_50 IoT 2 126.90
## 51 Startup_51 AI 3 106.48
## 52 Startup_52 E-Commerce 1 287.61
## 53 Startup_53 EdTech 5 55.72
## 54 Startup_54 E-Commerce 1 27.56
## 55 Startup_55 AI 1 231.41
## 56 Startup_56 Gaming 3 20.94
## 57 Startup_57 Gaming 5 251.21
## 58 Startup_58 Cybersecurity 5 131.44
## 59 Startup_59 Gaming 4 274.70
## 60 Startup_60 Cybersecurity 1 216.60
## 61 Startup_61 E-Commerce 1 183.32
## 62 Startup_62 HealthTech 2 284.82
## 63 Startup_63 IoT 4 119.30
## 64 Startup_64 IoT 2 286.25
## 65 Startup_65 E-Commerce 2 40.93
## 66 Startup_66 FinTech 2 145.38
## 67 Startup_67 FinTech 3 81.34
## 68 Startup_68 Gaming 3 162.18
## 69 Startup_69 IoT 2 49.02
## 70 Startup_70 IoT 4 252.54
## 71 Startup_71 Cybersecurity 1 254.00
## 72 Startup_72 HealthTech 4 286.59
## 73 Startup_73 AI 5 46.82
## 74 Startup_74 FinTech 3 187.19
## 75 Startup_75 IoT 1 142.53
## 76 Startup_76 Cybersecurity 1 103.64
## 77 Startup_77 EdTech 5 105.79
## 78 Startup_78 Cybersecurity 5 124.13
## 79 Startup_79 FinTech 2 211.63
## 80 Startup_80 FinTech 3 179.69
## 81 Startup_81 Cybersecurity 3 138.17
## 82 Startup_82 AI 4 23.24
## 83 Startup_83 E-Commerce 2 23.79
## 84 Startup_84 FinTech 2 1.20
## 85 Startup_85 Cybersecurity 2 290.33
## 86 Startup_86 IoT 2 2.07
## 87 Startup_87 Cybersecurity 3 31.38
## 88 Startup_88 HealthTech 3 94.72
## 89 Startup_89 E-Commerce 2 242.33
## 90 Startup_90 EdTech 4 288.65
## 91 Startup_91 AI 1 237.11
## 92 Startup_92 Gaming 1 207.32
## 93 Startup_93 EdTech 4 156.63
## 94 Startup_94 E-Commerce 2 26.47
## 95 Startup_95 E-Commerce 3 287.47
## 96 Startup_96 E-Commerce 1 227.64
## 97 Startup_97 EdTech 5 191.87
## 98 Startup_98 IoT 5 227.83
## 99 Startup_99 E-Commerce 4 217.37
## 100 Startup_100 Cybersecurity 2 191.35
## 101 Startup_101 EdTech 1 294.18
## 102 Startup_102 Gaming 2 271.06
## 103 Startup_103 E-Commerce 1 194.18
## 104 Startup_104 Cybersecurity 4 208.11
## 105 Startup_105 IoT 4 15.95
## 106 Startup_106 HealthTech 5 201.14
## 107 Startup_107 IoT 2 13.73
## 108 Startup_108 Gaming 5 176.68
## 109 Startup_109 E-Commerce 2 299.81
## 110 Startup_110 Gaming 5 166.96
## 111 Startup_111 EdTech 3 142.23
## 112 Startup_112 FinTech 3 94.20
## 113 Startup_113 E-Commerce 3 36.60
## 114 Startup_114 FinTech 3 219.44
## 115 Startup_115 Cybersecurity 1 58.18
## 116 Startup_116 HealthTech 3 35.03
## 117 Startup_117 AI 4 126.82
## 118 Startup_118 Gaming 4 238.70
## 119 Startup_119 IoT 1 223.65
## 120 Startup_120 HealthTech 3 16.94
## 121 Startup_121 E-Commerce 3 136.82
## 122 Startup_122 FinTech 3 156.97
## 123 Startup_123 IoT 5 193.54
## 124 Startup_124 FinTech 2 195.23
## 125 Startup_125 EdTech 5 109.73
## 126 Startup_126 FinTech 2 168.42
## 127 Startup_127 EdTech 3 144.58
## 128 Startup_128 Cybersecurity 3 265.56
## 129 Startup_129 IoT 5 159.31
## 130 Startup_130 IoT 5 132.59
## 131 Startup_131 AI 2 121.64
## 132 Startup_132 E-Commerce 4 171.94
## 133 Startup_133 EdTech 2 241.29
## 134 Startup_134 AI 5 161.75
## 135 Startup_135 FinTech 5 198.67
## 136 Startup_136 FinTech 1 221.49
## 137 Startup_137 AI 5 156.29
## 138 Startup_138 IoT 1 128.33
## 139 Startup_139 FinTech 4 263.06
## 140 Startup_140 Gaming 2 125.54
## 141 Startup_141 Cybersecurity 2 138.85
## 142 Startup_142 Cybersecurity 1 297.11
## 143 Startup_143 Gaming 2 0.57
## 144 Startup_144 AI 5 56.05
## 145 Startup_145 Gaming 3 115.50
## 146 Startup_146 EdTech 1 279.81
## 147 Startup_147 EdTech 2 21.47
## 148 Startup_148 AI 1 3.34
## 149 Startup_149 Gaming 1 16.28
## 150 Startup_150 EdTech 3 27.02
## 151 Startup_151 IoT 5 11.76
## 152 Startup_152 Gaming 1 144.19
## 153 Startup_153 IoT 2 174.44
## 154 Startup_154 EdTech 4 81.87
## 155 Startup_155 Cybersecurity 1 119.79
## 156 Startup_156 EdTech 1 27.96
## 157 Startup_157 IoT 3 101.24
## 158 Startup_158 HealthTech 5 156.99
## 159 Startup_159 E-Commerce 4 219.82
## 160 Startup_160 FinTech 2 1.50
## 161 Startup_161 Gaming 4 140.46
## 162 Startup_162 HealthTech 2 89.45
## 163 Startup_163 EdTech 5 255.92
## 164 Startup_164 Cybersecurity 2 214.81
## 165 Startup_165 AI 3 176.94
## 166 Startup_166 EdTech 3 83.54
## 167 Startup_167 Cybersecurity 3 273.15
## 168 Startup_168 E-Commerce 3 14.01
## 169 Startup_169 EdTech 4 33.29
## 170 Startup_170 IoT 5 117.79
## 171 Startup_171 AI 2 37.77
## 172 Startup_172 Cybersecurity 2 286.95
## 173 Startup_173 Cybersecurity 3 239.50
## 174 Startup_174 Cybersecurity 3 78.07
## 175 Startup_175 E-Commerce 1 176.77
## 176 Startup_176 EdTech 5 294.71
## 177 Startup_177 E-Commerce 4 265.32
## 178 Startup_178 Gaming 2 180.44
## 179 Startup_179 FinTech 1 271.17
## 180 Startup_180 FinTech 1 296.76
## 181 Startup_181 Gaming 2 223.13
## 182 Startup_182 EdTech 4 19.96
## 183 Startup_183 IoT 1 120.77
## 184 Startup_184 E-Commerce 1 251.43
## 185 Startup_185 AI 5 69.56
## 186 Startup_186 Cybersecurity 4 249.28
## 187 Startup_187 Cybersecurity 1 36.54
## 188 Startup_188 EdTech 4 14.56
## 189 Startup_189 IoT 2 115.54
## 190 Startup_190 FinTech 3 11.50
## 191 Startup_191 FinTech 1 287.20
## 192 Startup_192 Cybersecurity 5 247.95
## 193 Startup_193 EdTech 2 240.33
## 194 Startup_194 E-Commerce 4 189.12
## 195 Startup_195 FinTech 2 65.29
## 196 Startup_196 E-Commerce 1 155.72
## 197 Startup_197 IoT 4 179.38
## 198 Startup_198 FinTech 3 157.75
## 199 Startup_199 FinTech 2 78.44
## 200 Startup_200 HealthTech 1 154.82
## 201 Startup_201 FinTech 5 147.61
## 202 Startup_202 Gaming 4 298.90
## 203 Startup_203 AI 2 269.28
## 204 Startup_204 Gaming 2 139.28
## 205 Startup_205 EdTech 3 187.07
## 206 Startup_206 EdTech 3 224.49
## 207 Startup_207 E-Commerce 5 10.94
## 208 Startup_208 Gaming 5 268.53
## 209 Startup_209 Gaming 1 258.11
## 210 Startup_210 Gaming 1 137.50
## 211 Startup_211 IoT 5 117.66
## 212 Startup_212 FinTech 5 82.25
## 213 Startup_213 AI 4 143.26
## 214 Startup_214 E-Commerce 3 13.79
## 215 Startup_215 Gaming 1 255.73
## 216 Startup_216 Gaming 3 11.11
## 217 Startup_217 HealthTech 3 99.05
## 218 Startup_218 E-Commerce 5 279.51
## 219 Startup_219 Gaming 4 220.70
## 220 Startup_220 E-Commerce 2 84.78
## 221 Startup_221 FinTech 4 76.17
## 222 Startup_222 HealthTech 4 2.02
## 223 Startup_223 E-Commerce 3 208.87
## 224 Startup_224 AI 4 15.11
## 225 Startup_225 AI 1 160.01
## 226 Startup_226 EdTech 3 288.66
## 227 Startup_227 HealthTech 1 33.29
## 228 Startup_228 HealthTech 2 268.40
## 229 Startup_229 EdTech 3 297.45
## 230 Startup_230 E-Commerce 2 18.92
## 231 Startup_231 HealthTech 3 264.93
## 232 Startup_232 AI 5 155.07
## 233 Startup_233 AI 4 272.00
## 234 Startup_234 Gaming 5 171.61
## 235 Startup_235 FinTech 2 198.80
## 236 Startup_236 HealthTech 4 163.36
## 237 Startup_237 E-Commerce 3 259.42
## 238 Startup_238 EdTech 4 220.31
## 239 Startup_239 EdTech 1 156.62
## 240 Startup_240 E-Commerce 4 260.51
## 241 Startup_241 IoT 1 73.96
## 242 Startup_242 Cybersecurity 4 47.62
## 243 Startup_243 FinTech 1 48.54
## 244 Startup_244 EdTech 2 100.54
## 245 Startup_245 HealthTech 5 71.99
## 246 Startup_246 FinTech 3 279.06
## 247 Startup_247 HealthTech 4 32.20
## 248 Startup_248 IoT 5 209.85
## 249 Startup_249 AI 3 57.17
## 250 Startup_250 FinTech 3 264.55
## 251 Startup_251 Gaming 1 288.41
## 252 Startup_252 EdTech 2 139.91
## 253 Startup_253 FinTech 2 263.78
## 254 Startup_254 FinTech 5 2.04
## 255 Startup_255 FinTech 2 246.07
## 256 Startup_256 Cybersecurity 4 68.97
## 257 Startup_257 FinTech 2 272.66
## 258 Startup_258 HealthTech 5 0.84
## 259 Startup_259 AI 2 167.08
## 260 Startup_260 IoT 4 227.11
## 261 Startup_261 E-Commerce 1 77.98
## 262 Startup_262 FinTech 5 58.34
## 263 Startup_263 EdTech 1 25.69
## 264 Startup_264 FinTech 1 98.27
## 265 Startup_265 EdTech 1 168.17
## 266 Startup_266 HealthTech 3 35.20
## 267 Startup_267 EdTech 3 113.32
## 268 Startup_268 Gaming 1 3.22
## 269 Startup_269 E-Commerce 5 245.69
## 270 Startup_270 AI 4 38.97
## 271 Startup_271 EdTech 4 85.36
## 272 Startup_272 EdTech 2 6.02
## 273 Startup_273 AI 5 129.96
## 274 Startup_274 E-Commerce 3 252.12
## 275 Startup_275 FinTech 1 117.95
## 276 Startup_276 EdTech 2 54.26
## 277 Startup_277 AI 4 226.23
## 278 Startup_278 Gaming 3 236.75
## 279 Startup_279 Gaming 3 52.54
## 280 Startup_280 HealthTech 1 231.37
## 281 Startup_281 AI 4 284.22
## 282 Startup_282 HealthTech 5 50.33
## 283 Startup_283 Gaming 3 231.41
## 284 Startup_284 E-Commerce 1 212.72
## 285 Startup_285 FinTech 5 142.34
## 286 Startup_286 AI 4 17.85
## 287 Startup_287 IoT 3 22.87
## 288 Startup_288 Gaming 5 255.80
## 289 Startup_289 Gaming 5 297.05
## 290 Startup_290 IoT 5 202.98
## 291 Startup_291 Cybersecurity 3 134.06
## 292 Startup_292 Gaming 3 194.45
## 293 Startup_293 EdTech 2 178.52
## 294 Startup_294 E-Commerce 4 115.39
## 295 Startup_295 AI 1 20.16
## 296 Startup_296 IoT 5 246.54
## 297 Startup_297 EdTech 2 111.86
## 298 Startup_298 IoT 1 245.73
## 299 Startup_299 AI 2 76.13
## 300 Startup_300 HealthTech 3 0.81
## 301 Startup_301 AI 5 48.10
## 302 Startup_302 Cybersecurity 1 140.83
## 303 Startup_303 AI 1 283.32
## 304 Startup_304 AI 1 31.11
## 305 Startup_305 Cybersecurity 1 256.34
## 306 Startup_306 E-Commerce 1 158.34
## 307 Startup_307 Gaming 2 247.86
## 308 Startup_308 AI 5 89.86
## 309 Startup_309 Cybersecurity 3 252.06
## 310 Startup_310 EdTech 3 231.96
## 311 Startup_311 HealthTech 3 69.40
## 312 Startup_312 HealthTech 3 39.83
## 313 Startup_313 AI 2 260.48
## 314 Startup_314 E-Commerce 4 288.99
## 315 Startup_315 E-Commerce 5 9.88
## 316 Startup_316 EdTech 1 219.52
## 317 Startup_317 AI 4 272.90
## 318 Startup_318 HealthTech 3 257.28
## 319 Startup_319 E-Commerce 4 289.37
## 320 Startup_320 AI 5 228.15
## 321 Startup_321 IoT 1 132.97
## 322 Startup_322 E-Commerce 1 93.93
## 323 Startup_323 Cybersecurity 5 96.56
## 324 Startup_324 IoT 5 217.46
## 325 Startup_325 Gaming 3 223.88
## 326 Startup_326 E-Commerce 1 60.51
## 327 Startup_327 HealthTech 4 260.50
## 328 Startup_328 FinTech 2 214.62
## 329 Startup_329 IoT 2 169.04
## 330 Startup_330 Gaming 5 141.72
## 331 Startup_331 EdTech 3 163.20
## 332 Startup_332 EdTech 2 48.19
## 333 Startup_333 HealthTech 1 61.51
## 334 Startup_334 E-Commerce 5 27.80
## 335 Startup_335 AI 4 46.52
## 336 Startup_336 E-Commerce 5 136.38
## 337 Startup_337 HealthTech 4 159.34
## 338 Startup_338 EdTech 5 5.85
## 339 Startup_339 E-Commerce 5 239.14
## 340 Startup_340 EdTech 5 88.60
## 341 Startup_341 IoT 4 273.59
## 342 Startup_342 Gaming 4 290.27
## 343 Startup_343 AI 5 56.04
## 344 Startup_344 IoT 2 138.90
## 345 Startup_345 EdTech 3 0.87
## 346 Startup_346 EdTech 3 179.70
## 347 Startup_347 FinTech 3 182.39
## 348 Startup_348 FinTech 4 74.64
## 349 Startup_349 IoT 4 167.74
## 350 Startup_350 Cybersecurity 2 68.05
## 351 Startup_351 IoT 3 48.67
## 352 Startup_352 FinTech 3 195.63
## 353 Startup_353 EdTech 5 98.11
## 354 Startup_354 HealthTech 4 177.28
## 355 Startup_355 IoT 3 130.26
## 356 Startup_356 EdTech 2 295.31
## 357 Startup_357 HealthTech 2 45.30
## 358 Startup_358 HealthTech 3 139.65
## 359 Startup_359 FinTech 4 204.30
## 360 Startup_360 IoT 2 118.97
## 361 Startup_361 E-Commerce 5 292.42
## 362 Startup_362 AI 2 216.71
## 363 Startup_363 FinTech 1 197.27
## 364 Startup_364 FinTech 1 227.24
## 365 Startup_365 E-Commerce 4 285.82
## 366 Startup_366 AI 3 272.39
## 367 Startup_367 EdTech 4 217.42
## 368 Startup_368 Cybersecurity 4 224.87
## 369 Startup_369 IoT 4 44.20
## 370 Startup_370 Cybersecurity 4 31.29
## 371 Startup_371 EdTech 5 171.49
## 372 Startup_372 Gaming 5 6.00
## 373 Startup_373 FinTech 2 144.97
## 374 Startup_374 AI 1 63.50
## 375 Startup_375 FinTech 4 104.13
## 376 Startup_376 HealthTech 2 232.85
## 377 Startup_377 FinTech 1 282.40
## 378 Startup_378 FinTech 3 22.19
## 379 Startup_379 EdTech 5 160.51
## 380 Startup_380 EdTech 1 264.82
## 381 Startup_381 EdTech 3 25.21
## 382 Startup_382 Cybersecurity 1 247.44
## 383 Startup_383 HealthTech 5 107.66
## 384 Startup_384 Gaming 5 96.97
## 385 Startup_385 Gaming 2 293.25
## 386 Startup_386 EdTech 4 60.66
## 387 Startup_387 AI 1 208.11
## 388 Startup_388 Cybersecurity 1 246.52
## 389 Startup_389 E-Commerce 3 12.83
## 390 Startup_390 AI 5 201.28
## 391 Startup_391 IoT 1 285.49
## 392 Startup_392 IoT 5 37.70
## 393 Startup_393 AI 1 268.92
## 394 Startup_394 EdTech 2 178.64
## 395 Startup_395 E-Commerce 4 185.17
## 396 Startup_396 E-Commerce 1 184.08
## 397 Startup_397 Cybersecurity 5 90.00
## 398 Startup_398 E-Commerce 2 280.59
## 399 Startup_399 IoT 1 284.23
## 400 Startup_400 HealthTech 3 187.78
## 401 Startup_401 EdTech 4 235.14
## 402 Startup_402 E-Commerce 1 183.87
## 403 Startup_403 Cybersecurity 2 145.58
## 404 Startup_404 IoT 3 198.42
## 405 Startup_405 HealthTech 2 166.12
## 406 Startup_406 FinTech 2 179.44
## 407 Startup_407 FinTech 3 235.26
## 408 Startup_408 HealthTech 2 144.53
## 409 Startup_409 IoT 3 12.71
## 410 Startup_410 Cybersecurity 1 49.62
## 411 Startup_411 HealthTech 3 131.86
## 412 Startup_412 EdTech 3 217.59
## 413 Startup_413 EdTech 4 178.19
## 414 Startup_414 FinTech 3 195.26
## 415 Startup_415 E-Commerce 3 144.04
## 416 Startup_416 Cybersecurity 1 184.91
## 417 Startup_417 FinTech 4 99.04
## 418 Startup_418 E-Commerce 5 141.32
## 419 Startup_419 E-Commerce 1 223.41
## 420 Startup_420 Gaming 1 261.61
## 421 Startup_421 EdTech 5 130.38
## 422 Startup_422 IoT 3 13.58
## 423 Startup_423 AI 2 48.92
## 424 Startup_424 IoT 4 6.75
## 425 Startup_425 AI 2 196.15
## 426 Startup_426 HealthTech 5 100.03
## 427 Startup_427 EdTech 1 162.97
## 428 Startup_428 E-Commerce 1 148.24
## 429 Startup_429 Cybersecurity 4 24.92
## 430 Startup_430 AI 1 121.28
## 431 Startup_431 IoT 1 66.56
## 432 Startup_432 E-Commerce 4 125.81
## 433 Startup_433 AI 4 61.47
## 434 Startup_434 AI 5 252.22
## 435 Startup_435 Gaming 1 52.67
## 436 Startup_436 AI 1 288.25
## 437 Startup_437 Cybersecurity 5 125.06
## 438 Startup_438 EdTech 3 253.95
## 439 Startup_439 Cybersecurity 3 188.82
## 440 Startup_440 FinTech 3 220.74
## 441 Startup_441 E-Commerce 5 230.43
## 442 Startup_442 EdTech 1 151.70
## 443 Startup_443 Gaming 5 163.22
## 444 Startup_444 EdTech 5 154.44
## 445 Startup_445 EdTech 3 176.62
## 446 Startup_446 IoT 2 9.30
## 447 Startup_447 EdTech 1 118.96
## 448 Startup_448 Gaming 3 182.05
## 449 Startup_449 Cybersecurity 2 179.52
## 450 Startup_450 EdTech 2 237.99
## 451 Startup_451 HealthTech 4 196.68
## 452 Startup_452 E-Commerce 1 297.64
## 453 Startup_453 EdTech 2 85.51
## 454 Startup_454 E-Commerce 3 108.07
## 455 Startup_455 HealthTech 4 115.46
## 456 Startup_456 HealthTech 1 140.01
## 457 Startup_457 E-Commerce 5 250.88
## 458 Startup_458 AI 3 71.12
## 459 Startup_459 FinTech 2 232.91
## 460 Startup_460 IoT 2 121.89
## 461 Startup_461 AI 3 180.76
## 462 Startup_462 AI 2 189.95
## 463 Startup_463 Cybersecurity 4 112.17
## 464 Startup_464 IoT 1 154.03
## 465 Startup_465 IoT 4 124.20
## 466 Startup_466 AI 2 21.21
## 467 Startup_467 EdTech 5 131.43
## 468 Startup_468 Cybersecurity 1 29.67
## 469 Startup_469 Cybersecurity 1 121.80
## 470 Startup_470 HealthTech 3 178.08
## 471 Startup_471 Gaming 3 174.03
## 472 Startup_472 IoT 5 198.96
## 473 Startup_473 AI 1 29.03
## 474 Startup_474 HealthTech 5 195.81
## 475 Startup_475 E-Commerce 3 93.99
## 476 Startup_476 FinTech 3 120.01
## 477 Startup_477 Gaming 1 257.47
## 478 Startup_478 Gaming 4 3.20
## 479 Startup_479 Gaming 2 176.73
## 480 Startup_480 Cybersecurity 3 179.81
## 481 Startup_481 E-Commerce 3 119.30
## 482 Startup_482 Gaming 4 71.49
## 483 Startup_483 E-Commerce 4 70.78
## 484 Startup_484 EdTech 5 265.17
## 485 Startup_485 Gaming 5 132.89
## 486 Startup_486 FinTech 2 99.60
## 487 Startup_487 E-Commerce 2 134.91
## 488 Startup_488 E-Commerce 1 200.98
## 489 Startup_489 FinTech 5 44.15
## 490 Startup_490 Gaming 3 135.11
## 491 Startup_491 FinTech 5 176.57
## 492 Startup_492 E-Commerce 3 284.60
## 493 Startup_493 FinTech 4 76.13
## 494 Startup_494 E-Commerce 5 108.10
## 495 Startup_495 E-Commerce 4 114.12
## 496 Startup_496 EdTech 2 181.86
## 497 Startup_497 AI 2 107.34
## 498 Startup_498 E-Commerce 1 160.29
## 499 Startup_499 Gaming 5 234.65
## 500 Startup_500 HealthTech 4 211.76
## Valuation..M.USD. Revenue..M.USD. Employees Market.Share.... Profitable
## 1 844.75 67.87 1468 5.20 0
## 2 3310.83 75.65 3280 8.10 1
## 3 1059.37 84.21 4933 2.61 1
## 4 101.90 47.08 1059 2.53 0
## 5 850.11 50.25 1905 4.09 0
## 6 1541.76 12.56 1462 8.96 1
## 7 1039.51 38.60 1404 0.10 0
## 8 630.19 64.37 3420 9.02 1
## 9 935.47 78.06 447 1.62 0
## 10 707.45 21.80 3655 9.90 0
## 11 1665.52 13.87 4657 9.55 0
## 12 1207.68 66.34 1183 5.00 0
## 13 141.02 0.29 4463 5.18 1
## 14 720.54 17.92 1262 4.87 0
## 15 2960.94 3.00 3639 6.39 1
## 16 1593.98 16.77 1846 1.00 1
## 17 427.58 18.21 2314 0.42 0
## 18 1276.06 38.83 915 0.67 1
## 19 1729.44 40.29 4345 4.50 0
## 20 714.35 87.44 4642 2.83 0
## 21 1442.91 51.06 4014 6.26 1
## 22 1776.82 72.37 2174 9.93 0
## 23 655.46 10.50 2177 3.92 0
## 24 1171.96 22.62 128 5.78 0
## 25 1174.26 70.32 3163 4.21 0
## 26 285.33 5.38 790 8.49 1
## 27 2536.54 48.71 2421 9.03 1
## 28 935.69 59.98 801 8.79 1
## 29 941.98 60.25 4177 9.75 1
## 30 1165.80 2.39 4159 0.37 0
## 31 2272.03 32.28 1941 8.41 0
## 32 2885.12 63.69 2161 7.54 0
## 33 740.70 14.99 3812 2.36 0
## 34 108.47 69.57 1306 9.57 0
## 35 1434.40 74.62 4168 1.57 1
## 36 728.61 98.20 3145 6.83 0
## 37 1257.15 21.06 3241 2.33 0
## 38 472.04 82.53 2357 4.37 0
## 39 257.34 52.08 1937 7.22 1
## 40 869.37 37.41 2726 5.68 1
## 41 1184.20 12.33 369 4.33 0
## 42 1646.57 73.58 4921 3.33 0
## 43 242.66 23.66 2395 8.41 1
## 44 1127.31 37.21 951 1.52 0
## 45 1926.64 6.35 1382 4.36 0
## 46 2303.23 80.16 1444 9.05 1
## 47 3074.01 76.68 3185 8.47 1
## 48 529.89 73.39 4862 4.07 0
## 49 2903.05 30.01 345 7.65 1
## 50 1779.66 17.43 2994 8.25 1
## 51 1086.87 63.79 387 8.90 0
## 52 4110.09 49.67 1561 5.49 1
## 53 720.02 17.91 304 2.12 1
## 54 303.26 68.62 143 2.63 1
## 55 2297.03 99.11 363 3.03 1
## 56 150.41 74.75 4936 5.98 0
## 57 1233.81 74.02 1527 0.42 0
## 58 1348.50 95.04 3461 6.40 0
## 59 1872.72 20.37 1632 3.71 0
## 60 2901.85 56.60 1308 3.02 0
## 61 2735.14 97.97 1139 8.81 1
## 62 3426.14 16.61 2191 4.65 1
## 63 487.43 70.06 3175 1.04 1
## 64 1850.39 58.62 3840 8.28 1
## 65 176.38 58.69 2156 0.57 1
## 66 1571.00 69.80 1242 7.29 0
## 67 990.91 92.97 3740 7.76 0
## 68 2288.05 78.28 2839 3.59 0
## 69 404.81 5.71 4265 8.27 1
## 70 3489.22 85.29 1003 5.21 0
## 71 3437.81 22.63 2572 4.69 1
## 72 1950.63 37.75 1312 0.55 0
## 73 628.64 46.23 661 2.97 1
## 74 1405.44 48.14 2665 9.32 1
## 75 935.34 43.57 2216 3.98 1
## 76 1118.16 93.74 1914 5.51 1
## 77 734.24 70.63 3735 5.37 1
## 78 1311.12 63.15 2051 5.95 1
## 79 2183.59 5.08 3167 0.71 1
## 80 1754.00 90.40 1145 3.69 1
## 81 1045.26 90.95 271 7.26 1
## 82 222.69 78.04 1850 2.09 0
## 83 241.65 0.80 1713 2.12 0
## 84 11.46 5.82 3635 0.20 0
## 85 2419.33 22.60 3216 4.43 0
## 86 6.57 13.22 2298 8.84 0
## 87 315.20 39.94 3737 0.61 1
## 88 475.62 92.27 3834 1.99 1
## 89 2595.58 47.26 1515 3.17 1
## 90 3496.17 1.44 480 7.34 0
## 91 2135.26 28.35 981 6.44 0
## 92 1971.27 4.56 1212 4.22 0
## 93 2258.25 3.73 1965 7.61 0
## 94 348.50 27.64 562 6.14 0
## 95 4016.20 27.63 887 8.97 0
## 96 2905.87 78.73 2199 8.20 1
## 97 826.39 97.49 4766 2.60 1
## 98 1090.83 37.18 2672 2.79 0
## 99 2025.78 42.95 4964 1.34 0
## 100 1139.08 84.49 4682 3.44 0
## 101 2565.35 26.89 4795 10.00 0
## 102 2095.94 15.72 1615 4.12 1
## 103 1862.76 85.32 3493 2.28 0
## 104 2489.52 4.75 2459 7.65 0
## 105 184.79 62.01 4499 5.47 0
## 106 1861.51 56.24 1951 1.58 0
## 107 176.69 34.66 4815 3.87 1
## 108 1443.54 31.59 4804 4.20 1
## 109 4125.56 77.21 2813 7.56 1
## 110 643.69 8.73 3830 0.30 0
## 111 2056.07 23.75 3541 2.76 0
## 112 1022.67 73.27 3807 5.22 0
## 113 276.73 54.88 4182 2.76 1
## 114 1177.48 12.89 4704 6.32 0
## 115 509.09 88.23 1975 4.87 0
## 116 186.87 39.41 3827 3.68 1
## 117 633.59 82.80 2824 9.50 1
## 118 2484.44 78.79 907 6.36 0
## 119 2074.80 62.48 369 1.39 0
## 120 187.04 49.50 4263 5.64 0
## 121 1936.72 97.96 3124 7.83 1
## 122 1473.50 14.58 2563 7.11 0
## 123 1929.82 72.54 1868 1.80 1
## 124 795.66 19.20 120 9.73 0
## 125 1274.14 33.89 2389 4.54 1
## 126 1313.48 30.94 4981 1.67 1
## 127 2128.32 94.71 3940 1.88 0
## 128 3419.91 96.74 4940 1.07 1
## 129 1476.77 18.38 2495 8.05 0
## 130 593.83 2.30 947 1.31 1
## 131 1619.39 72.03 1729 1.34 1
## 132 2165.99 41.86 2339 8.52 0
## 133 1258.33 8.98 2772 3.51 1
## 134 1058.39 68.41 1142 0.41 1
## 135 2487.52 54.56 2532 0.61 0
## 136 1156.78 98.05 4122 1.51 0
## 137 2190.10 22.82 3425 2.21 1
## 138 1038.75 40.46 4783 6.68 1
## 139 1063.94 54.30 3911 1.66 0
## 140 1605.53 79.19 3642 1.28 0
## 141 1038.89 54.93 2958 7.67 0
## 142 2768.47 61.33 2372 6.74 0
## 143 2.48 22.69 4226 4.41 0
## 144 169.89 84.71 281 5.54 1
## 145 1230.75 42.92 2004 3.19 0
## 146 2569.24 14.18 1232 4.88 1
## 147 217.33 66.45 3757 5.12 0
## 148 41.83 21.69 3740 3.93 0
## 149 175.16 9.85 700 9.08 0
## 150 137.73 86.21 1314 0.26 0
## 151 86.19 46.07 4157 7.84 1
## 152 2000.02 74.95 3239 1.33 1
## 153 2135.03 29.13 4119 8.00 1
## 154 792.47 78.72 2316 6.20 1
## 155 726.85 11.28 2846 8.45 1
## 156 185.23 35.66 4213 1.07 0
## 157 1192.96 97.81 3564 5.63 1
## 158 2074.48 79.42 3403 5.06 1
## 159 2430.57 49.78 546 7.09 0
## 160 19.07 34.04 2699 5.39 1
## 161 1713.59 87.23 4679 5.45 0
## 162 429.04 54.53 1093 4.35 0
## 163 2069.71 75.87 3368 3.16 1
## 164 1038.78 75.44 29 4.87 0
## 165 1030.29 1.89 1577 1.27 0
## 166 1122.04 12.71 2963 9.62 1
## 167 2369.50 66.00 4241 7.17 1
## 168 132.03 16.25 1772 9.53 0
## 169 171.16 25.62 4748 2.34 1
## 170 794.76 94.67 2240 8.81 0
## 171 217.67 56.06 205 8.11 0
## 172 1570.83 75.10 3203 6.80 1
## 173 2552.00 55.84 261 6.29 1
## 174 474.87 55.30 3752 3.23 0
## 175 1870.97 9.51 4647 8.18 1
## 176 1090.86 34.43 2246 5.07 1
## 177 1247.35 28.31 3180 0.80 0
## 178 1135.78 39.13 3611 3.20 0
## 179 2756.32 79.85 3341 9.36 0
## 180 3297.03 37.26 4902 8.52 1
## 181 1854.79 45.22 418 9.82 1
## 182 166.66 39.80 2019 6.32 1
## 183 1412.31 9.43 1386 2.55 1
## 184 802.02 44.16 1103 1.71 1
## 185 493.39 19.64 3431 1.95 0
## 186 3196.89 18.75 3660 0.30 0
## 187 374.12 9.49 914 3.97 0
## 188 102.60 35.73 192 2.00 0
## 189 1602.11 75.77 4049 4.18 1
## 190 141.12 27.72 3761 9.36 1
## 191 1774.54 31.30 2020 4.36 0
## 192 2663.85 64.30 2097 8.79 0
## 193 2439.11 19.66 3649 6.09 1
## 194 1477.83 89.71 2135 2.55 0
## 195 483.37 52.07 362 3.62 0
## 196 1109.15 44.82 2427 0.93 0
## 197 1866.60 55.83 4262 3.40 0
## 198 1310.73 16.69 4550 6.81 1
## 199 1018.34 61.39 1502 0.81 0
## 200 1708.47 36.37 2163 2.13 0
## 201 566.70 89.73 2560 3.88 0
## 202 3192.86 16.97 2347 9.82 1
## 203 1684.01 50.75 2956 7.87 0
## 204 1408.64 59.85 1243 7.36 0
## 205 2088.00 3.44 978 1.25 0
## 206 1295.30 36.43 4732 7.83 0
## 207 92.80 73.29 3970 7.87 0
## 208 1418.89 82.06 1055 6.05 1
## 209 936.44 31.38 3706 9.90 0
## 210 1036.99 79.88 2785 3.32 0
## 211 865.43 96.78 1593 9.42 1
## 212 1229.29 85.70 3883 9.16 0
## 213 1197.11 11.85 2373 0.21 0
## 214 96.57 74.00 441 4.56 0
## 215 2548.53 32.03 2833 9.77 1
## 216 45.16 98.84 1561 1.12 0
## 217 1440.19 48.59 3863 5.52 1
## 218 2968.26 12.33 1309 9.79 1
## 219 2661.21 72.55 2389 5.60 0
## 220 427.78 32.37 187 1.98 1
## 221 602.79 99.71 1594 5.86 0
## 222 16.31 97.98 1592 2.12 0
## 223 2686.42 65.65 664 5.25 1
## 224 45.55 13.07 164 0.83 1
## 225 1057.70 68.72 3958 6.09 1
## 226 4264.90 9.39 1016 5.22 0
## 227 289.58 3.03 57 7.43 0
## 228 2037.24 82.31 2312 6.34 1
## 229 1320.07 21.05 1233 5.54 0
## 230 277.93 37.96 1040 6.93 0
## 231 858.46 97.35 3707 1.53 0
## 232 1836.98 33.75 4050 5.99 0
## 233 3324.71 29.72 4950 1.80 1
## 234 1016.60 91.83 4454 9.26 0
## 235 2198.47 11.16 1269 0.78 1
## 236 672.25 75.98 1889 4.36 0
## 237 1578.46 98.81 1814 6.99 1
## 238 2743.92 90.14 3172 4.63 0
## 239 2094.99 65.20 3802 1.01 0
## 240 2683.73 31.47 2346 5.58 0
## 241 671.71 25.51 3419 6.45 0
## 242 233.89 81.94 822 7.07 0
## 243 182.38 87.99 2442 3.80 1
## 244 443.56 52.59 893 5.68 1
## 245 412.38 24.51 672 4.56 0
## 246 1981.35 11.38 4733 6.75 0
## 247 243.78 8.81 2629 5.88 0
## 248 1501.66 71.94 4237 5.30 0
## 249 335.51 42.07 4528 2.57 1
## 250 2411.82 90.55 823 0.12 1
## 251 3721.48 73.99 2193 1.82 0
## 252 889.33 19.21 1111 1.26 0
## 253 3774.33 61.42 2229 4.55 0
## 254 27.50 43.61 202 6.16 1
## 255 1215.87 57.77 2676 2.21 0
## 256 857.66 73.83 3497 9.40 1
## 257 2697.98 80.64 2453 0.17 1
## 258 8.23 84.39 475 7.75 0
## 259 1285.27 25.47 1953 6.29 1
## 260 2047.13 3.21 3904 3.80 0
## 261 605.60 73.96 2015 0.60 1
## 262 552.88 10.72 3805 6.31 0
## 263 270.65 31.56 4123 3.92 0
## 264 1089.98 0.48 3801 9.37 1
## 265 1579.08 68.20 3993 5.47 1
## 266 410.33 16.80 239 9.40 1
## 267 532.08 65.56 933 2.28 0
## 268 17.85 99.18 3612 7.28 1
## 269 3150.93 7.36 527 6.21 0
## 270 267.74 7.80 3528 7.42 1
## 271 279.53 34.10 4531 2.23 0
## 272 29.17 86.65 4893 6.68 1
## 273 738.61 21.63 1634 5.03 1
## 274 3652.68 36.10 4633 7.45 1
## 275 1164.93 6.94 1321 3.34 0
## 276 770.10 76.34 2862 8.91 0
## 277 2240.12 40.89 1129 0.93 1
## 278 2072.10 75.95 2592 8.09 0
## 279 355.42 90.37 1028 8.63 1
## 280 2377.66 23.35 1383 8.19 0
## 281 1327.41 80.76 3379 5.18 0
## 282 693.50 6.00 1524 4.44 1
## 283 2997.22 89.89 231 9.96 1
## 284 1448.70 65.11 4973 2.37 0
## 285 2054.47 87.44 896 5.51 0
## 286 230.31 0.12 4077 6.74 0
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## 288 1141.16 39.04 2793 2.12 0
## 289 2595.86 38.01 401 6.57 1
## 290 1841.70 22.75 2083 8.20 1
## 291 776.29 38.22 4749 0.60 0
## 292 769.94 12.97 4429 9.12 1
## 293 2199.49 41.33 3242 0.39 0
## 294 515.15 80.26 3898 3.62 0
## 295 204.25 47.96 2010 0.26 1
## 296 3510.09 1.90 2021 0.86 0
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## 300 2.43 12.83 1201 2.23 0
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## 310 2420.26 53.31 1466 4.03 0
## 311 455.84 63.55 360 5.18 0
## 312 325.17 45.04 3609 2.61 1
## 313 1314.90 21.92 3328 9.88 1
## 314 1125.78 12.11 239 2.99 1
## 315 122.44 6.93 1553 7.73 1
## 316 1357.65 26.41 2101 4.33 1
## 317 1400.85 4.71 4218 2.14 0
## 318 2907.11 56.76 2106 8.02 0
## 319 3014.26 28.45 1678 3.40 1
## 320 2418.00 82.47 2105 8.67 1
## 321 764.14 7.52 3662 9.16 1
## 322 544.02 22.71 4658 8.61 1
## 323 545.53 73.05 3601 0.45 0
## 324 1105.79 59.92 3753 7.89 1
## 325 2433.28 6.54 3178 4.17 1
## 326 797.29 87.74 1820 8.75 0
## 327 3045.72 87.74 3846 6.42 1
## 328 2683.85 33.11 2010 8.35 0
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## 359 1209.81 44.58 3467 4.49 0
## 360 1287.32 78.90 4068 5.58 1
## 361 3721.99 64.00 1529 2.62 1
## 362 2247.06 67.67 4765 7.45 1
## 363 2626.61 13.26 37 0.86 0
## 364 1751.73 23.50 4030 8.74 0
## 365 4137.15 78.70 2571 8.30 1
## 366 1252.38 97.08 2808 2.24 0
## 367 918.77 26.34 3901 8.11 0
## 368 2452.05 27.23 3714 7.88 0
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## 382 2080.34 62.29 648 4.67 0
## 383 1463.05 47.03 4716 4.20 1
## 384 413.90 44.80 199 0.23 0
## 385 4357.49 47.48 4001 3.03 0
## 386 504.65 59.13 493 4.65 1
## 387 743.78 53.75 2209 4.17 0
## 388 852.04 85.43 2662 7.25 0
## 389 153.38 5.04 3281 3.91 0
## 390 2195.65 83.35 1107 4.94 1
## 391 3604.47 84.41 1246 4.35 0
## 392 160.77 28.56 520 7.43 0
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## 397 1340.92 4.53 2660 5.46 0
## 398 4110.36 27.85 984 3.52 1
## 399 4066.70 81.69 4534 4.20 1
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## 403 1396.90 88.00 237 6.38 0
## 404 2572.80 57.52 1557 5.86 1
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## 411 756.10 39.05 3436 3.42 0
## 412 3201.16 73.01 3488 7.52 1
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## 419 1952.39 95.41 1597 4.18 1
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## 451 2899.21 82.92 1551 8.75 0
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## 454 558.41 69.52 1610 2.93 1
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## 456 957.77 44.94 4680 5.15 0
## 457 3020.16 8.53 1895 8.22 1
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## 459 2206.75 91.02 2748 4.90 0
## 460 1430.42 80.82 2498 0.23 1
## 461 776.42 21.68 361 4.16 0
## 462 2131.92 46.71 874 6.51 0
## 463 460.17 81.69 890 0.25 0
## 464 2246.00 6.83 2006 2.88 0
## 465 1100.50 38.80 1288 2.16 0
## 466 101.98 85.20 624 0.52 0
## 467 934.28 40.45 887 4.84 1
## 468 236.20 7.85 801 1.90 1
## 469 1473.56 54.30 3647 7.46 1
## 470 2122.91 86.19 3430 8.49 1
## 471 1800.04 59.07 1301 5.50 1
## 472 849.79 22.71 4719 9.82 0
## 473 139.89 38.49 249 4.56 1
## 474 807.73 45.07 2900 5.18 1
## 475 932.95 57.97 934 9.89 1
## 476 1623.21 75.96 524 2.39 0
## 477 2112.70 9.98 3217 9.43 0
## 478 15.78 88.98 2414 0.52 0
## 479 1713.66 1.33 12 4.52 1
## 480 2290.96 37.07 2530 0.79 0
## 481 1419.73 2.84 3428 7.32 1
## 482 970.65 39.36 2024 6.12 0
## 483 448.38 52.63 4595 7.10 0
## 484 1412.68 51.22 4287 3.81 0
## 485 1801.70 16.65 3672 3.76 0
## 486 1084.72 64.61 3676 0.55 0
## 487 908.10 4.57 2806 3.56 0
## 488 2393.31 76.21 90 9.30 1
## 489 564.30 94.67 3120 3.85 0
## 490 1402.41 34.04 4237 8.33 0
## 491 1987.23 51.77 2473 8.44 1
## 492 2135.39 37.95 3624 3.80 1
## 493 720.52 0.77 1641 6.36 1
## 494 1004.24 38.65 3588 1.04 0
## 495 1503.71 79.19 4014 1.13 1
## 496 2378.65 59.64 3331 0.58 1
## 497 1394.58 10.22 2223 5.85 0
## 498 502.09 84.73 2222 4.32 0
## 499 2814.52 53.16 4972 5.53 0
## 500 2563.17 84.19 2374 5.26 0
## Year.Founded Region Exit.Status Startup.Age
## 1 2006 Europe Private 19
## 2 2003 South America Private 22
## 3 1995 South America Private 30
## 4 2003 South America Private 22
## 5 1997 Europe Acquired 28
## 6 2004 Europe IPO 21
## 7 2016 Australia Private 9
## 8 2002 Australia IPO 23
## 9 2020 North America Acquired 5
## 10 1995 Europe Private 30
## 11 1994 Europe Private 31
## 12 2011 Europe Private 14
## 13 2012 Australia Acquired 13
## 14 2014 South America Acquired 11
## 15 2015 Asia Acquired 10
## 16 1994 Australia Private 31
## 17 2015 Europe Private 10
## 18 2009 Australia Private 16
## 19 1995 North America Acquired 30
## 20 1995 Europe Private 30
## 21 2007 North America Private 18
## 22 1993 Europe Private 32
## 23 2006 Australia Private 19
## 24 2003 South America Private 22
## 25 2003 Asia Acquired 22
## 26 2002 North America Acquired 23
## 27 1996 Australia Private 29
## 28 2004 South America IPO 21
## 29 2008 Australia Acquired 17
## 30 2006 North America Acquired 19
## 31 2008 Asia Acquired 17
## 32 2009 Australia Private 16
## 33 2021 Europe Private 4
## 34 2013 South America Private 12
## 35 2006 Australia Private 19
## 36 1996 South America Private 29
## 37 1990 Europe Private 35
## 38 2021 Asia Private 4
## 39 2002 Asia Private 23
## 40 1995 Europe Acquired 30
## 41 2003 South America Private 22
## 42 2004 Australia Private 21
## 43 1993 Europe Private 32
## 44 2005 North America Private 20
## 45 2017 South America Private 8
## 46 2005 Asia Private 20
## 47 1993 Europe IPO 32
## 48 2015 Asia Private 10
## 49 2017 South America Private 8
## 50 2021 Asia Private 4
## 51 1991 Europe Acquired 34
## 52 2020 South America Private 5
## 53 1992 North America Private 33
## 54 1997 Europe Acquired 28
## 55 2013 Australia Private 12
## 56 2004 Europe IPO 21
## 57 2020 South America Private 5
## 58 1998 Europe Private 27
## 59 1991 North America Private 34
## 60 1997 North America Acquired 28
## 61 1996 Australia Private 29
## 62 2005 Asia IPO 20
## 63 2019 South America IPO 6
## 64 1999 South America Acquired 26
## 65 2007 Asia Acquired 18
## 66 1994 North America Private 31
## 67 2018 North America IPO 7
## 68 2012 Europe Private 13
## 69 1994 Australia Private 31
## 70 2019 Australia Private 6
## 71 1995 Australia Private 30
## 72 1993 Europe Private 32
## 73 2018 Australia Private 7
## 74 2012 Asia Acquired 13
## 75 1995 Europe Private 30
## 76 2021 Asia Acquired 4
## 77 2007 Europe IPO 18
## 78 2004 Australia IPO 21
## 79 2021 Asia Private 4
## 80 1998 Asia Acquired 27
## 81 1999 Asia Private 26
## 82 2009 Europe Acquired 16
## 83 1995 South America Private 30
## 84 1992 Australia Acquired 33
## 85 2000 Asia Acquired 25
## 86 2006 South America Private 19
## 87 2021 Australia Acquired 4
## 88 1993 Australia Private 32
## 89 1993 Asia Private 32
## 90 2006 Asia Acquired 19
## 91 2008 Europe Private 17
## 92 1994 South America IPO 31
## 93 2021 South America Private 4
## 94 2011 Asia Acquired 14
## 95 1995 Australia Private 30
## 96 2011 Asia Acquired 14
## 97 1990 Australia Private 35
## 98 2009 Australia Private 16
## 99 2011 Asia Private 14
## 100 2000 North America Private 25
## 101 2002 South America Private 23
## 102 1992 Australia Private 33
## 103 1998 Australia Acquired 27
## 104 2001 North America IPO 24
## 105 2012 Europe IPO 13
## 106 2003 North America Private 22
## 107 2022 North America Private 3
## 108 2007 Australia Private 18
## 109 1998 South America Acquired 27
## 110 2012 North America Private 13
## 111 1999 Europe Acquired 26
## 112 2008 Asia Private 17
## 113 1994 North America Private 31
## 114 2012 North America Private 13
## 115 2019 Europe Private 6
## 116 2014 South America Private 11
## 117 2017 North America Acquired 8
## 118 2014 North America Private 11
## 119 1999 South America Acquired 26
## 120 2003 Europe Private 22
## 121 2019 North America Private 6
## 122 2014 South America Acquired 11
## 123 2003 North America IPO 22
## 124 1999 Asia Private 26
## 125 2000 Australia Private 25
## 126 2000 Asia Private 25
## 127 2010 North America Acquired 15
## 128 2010 Europe Acquired 15
## 129 2011 Australia Private 14
## 130 1996 Asia Private 29
## 131 2006 South America Acquired 19
## 132 1991 Europe Private 34
## 133 2001 Asia Acquired 24
## 134 2007 South America Private 18
## 135 2000 North America Private 25
## 136 2005 South America Private 20
## 137 2007 Asia Private 18
## 138 2015 Asia Private 10
## 139 2005 North America Acquired 20
## 140 1995 South America Acquired 30
## 141 2001 South America Private 24
## 142 2002 North America Private 23
## 143 1993 South America Private 32
## 144 1991 North America Private 34
## 145 2015 Europe Private 10
## 146 2016 Asia Acquired 9
## 147 2016 Europe Private 9
## 148 2004 Australia Private 21
## 149 1997 Australia Private 28
## 150 1996 Australia Private 29
## 151 2001 North America Acquired 24
## 152 2002 South America Private 23
## 153 2006 Europe Private 19
## 154 2006 Europe Private 19
## 155 2010 Europe Acquired 15
## 156 1997 Europe Acquired 28
## 157 1994 Australia IPO 31
## 158 2015 Europe IPO 10
## 159 2005 South America Private 20
## 160 2017 Australia Private 8
## 161 2018 Europe Private 7
## 162 2001 North America Private 24
## 163 2019 Asia Private 6
## 164 2012 Asia IPO 13
## 165 2005 Asia Private 20
## 166 2003 Australia Private 22
## 167 1999 Europe Acquired 26
## 168 1996 Australia Acquired 29
## 169 2018 South America Private 7
## 170 1992 North America Private 33
## 171 2004 North America Private 21
## 172 2008 Asia Acquired 17
## 173 2002 Asia Private 23
## 174 2010 Asia Private 15
## 175 2016 Europe Acquired 9
## 176 2018 South America Private 7
## 177 2009 South America IPO 16
## 178 2014 South America Private 11
## 179 1995 South America Acquired 30
## 180 1991 Asia Acquired 34
## 181 2011 North America IPO 14
## 182 2021 Asia Acquired 4
## 183 2005 South America Private 20
## 184 2007 Asia Acquired 18
## 185 1994 South America Private 31
## 186 2019 Australia Acquired 6
## 187 2008 North America Private 17
## 188 2012 Australia Private 13
## 189 2002 Europe Acquired 23
## 190 2018 Asia Private 7
## 191 2011 Europe IPO 14
## 192 1998 Asia Private 27
## 193 2008 Australia Private 17
## 194 1995 South America Private 30
## 195 2004 South America Private 21
## 196 2009 Asia Private 16
## 197 1998 South America Acquired 27
## 198 2021 Australia Acquired 4
## 199 1993 South America Private 32
## 200 2007 Australia Private 18
## 201 1994 Europe Private 31
## 202 2009 Australia Private 16
## 203 2012 North America Private 13
## 204 2000 North America Private 25
## 205 2011 North America Private 14
## 206 1993 Asia Private 32
## 207 2018 Asia Private 7
## 208 2002 Asia Acquired 23
## 209 1998 Australia IPO 27
## 210 2020 Asia Private 5
## 211 1996 Europe IPO 29
## 212 1993 North America Private 32
## 213 2008 Australia Private 17
## 214 2019 North America Private 6
## 215 2020 Asia Private 5
## 216 2010 Australia Private 15
## 217 2020 South America Private 5
## 218 2008 Europe Private 17
## 219 2020 Asia Private 5
## 220 2021 Asia Private 4
## 221 1991 Europe Private 34
## 222 1999 Europe Private 26
## 223 1991 South America Private 34
## 224 1991 Asia Private 34
## 225 2013 Asia Private 12
## 226 2006 North America Private 19
## 227 2021 Australia Private 4
## 228 2005 Australia Private 20
## 229 2005 North America Private 20
## 230 2020 Australia Private 5
## 231 2013 Europe Private 12
## 232 1990 Australia Private 35
## 233 2000 Australia Private 25
## 234 2010 North America Private 15
## 235 1993 South America Acquired 32
## 236 1996 Australia Private 29
## 237 2015 Australia Private 10
## 238 2001 South America Private 24
## 239 2020 North America Private 5
## 240 2006 South America Private 19
## 241 2022 North America Private 3
## 242 2000 Europe Acquired 25
## 243 2017 North America Private 8
## 244 2021 Europe Private 4
## 245 1990 Europe IPO 35
## 246 2017 North America IPO 8
## 247 1996 Asia Private 29
## 248 1991 North America Private 34
## 249 2013 Australia IPO 12
## 250 1993 Australia Private 32
## 251 2008 Asia Private 17
## 252 2015 Europe Private 10
## 253 2000 Asia Private 25
## 254 2000 Australia Private 25
## 255 1992 Asia Private 33
## 256 2001 North America Private 24
## 257 1992 Asia Private 33
## 258 2016 Europe IPO 9
## 259 1991 North America Private 34
## 260 2005 South America Acquired 20
## 261 1991 Australia IPO 34
## 262 2019 Australia Acquired 6
## 263 2009 Asia Acquired 16
## 264 2017 North America Private 8
## 265 2014 South America IPO 11
## 266 2021 North America Private 4
## 267 1999 Australia Private 26
## 268 2022 North America Private 3
## 269 2002 South America Acquired 23
## 270 2005 Asia Private 20
## 271 2011 Europe Private 14
## 272 2009 North America Private 16
## 273 2016 Europe Private 9
## 274 2005 Australia Private 20
## 275 2021 Asia Private 4
## 276 2015 Asia Private 10
## 277 2014 North America Private 11
## 278 1991 Asia Acquired 34
## 279 2001 North America Private 24
## 280 1998 North America Acquired 27
## 281 2001 Europe Private 24
## 282 2015 Asia Private 10
## 283 1998 North America Private 27
## 284 1999 Europe Private 26
## 285 2021 South America Private 4
## 286 2009 Europe Private 16
## 287 1991 Europe Private 34
## 288 1990 North America Private 35
## 289 2013 Australia Acquired 12
## 290 2016 Australia Private 9
## 291 1991 Australia Private 34
## 292 2020 Europe Acquired 5
## 293 2003 North America Private 22
## 294 1991 Asia Acquired 34
## 295 2018 Europe Private 7
## 296 2004 Europe Private 21
## 297 2001 North America Private 24
## 298 2017 South America Acquired 8
## 299 2009 Europe IPO 16
## 300 2013 South America Acquired 12
## 301 2010 South America Private 15
## 302 1999 Australia Private 26
## 303 2003 North America Private 22
## 304 2007 South America Private 18
## 305 2013 North America Private 12
## 306 2003 Australia Private 22
## 307 2001 North America Private 24
## 308 2011 Europe Private 14
## 309 1995 South America Private 30
## 310 2011 North America IPO 14
## 311 2003 Asia Acquired 22
## 312 2005 North America Private 20
## 313 2014 South America Acquired 11
## 314 2021 Australia Private 4
## 315 2022 Australia Private 3
## 316 2009 South America Private 16
## 317 2007 Europe Private 18
## 318 1992 Europe Acquired 33
## 319 2012 Australia Private 13
## 320 2021 Australia Private 4
## 321 2005 South America Private 20
## 322 1994 Europe Private 31
## 323 1993 Australia Private 32
## 324 2010 Europe Private 15
## 325 2003 Australia Private 22
## 326 2020 North America Acquired 5
## 327 2003 Asia Private 22
## 328 1995 North America Private 30
## 329 2001 Australia Private 24
## 330 2005 Asia Private 20
## 331 2006 Asia Private 19
## 332 2012 Australia Private 13
## 333 2003 Australia Private 22
## 334 1996 Europe Acquired 29
## 335 2021 Australia Private 4
## 336 2004 South America Acquired 21
## 337 2006 Australia Acquired 19
## 338 1994 South America Private 31
## 339 2016 Australia Private 9
## 340 2012 Europe Private 13
## 341 2003 South America IPO 22
## 342 2004 North America Private 21
## 343 2022 South America Private 3
## 344 2020 Europe Private 5
## 345 2006 South America Private 19
## 346 2017 Australia Private 8
## 347 2008 Australia Private 17
## 348 2012 Europe Private 13
## 349 2021 Asia Acquired 4
## 350 2002 Australia Private 23
## 351 2009 North America Private 16
## 352 1994 Europe Private 31
## 353 1998 South America Acquired 27
## 354 2018 South America Private 7
## 355 1995 North America Private 30
## 356 2014 Australia Private 11
## 357 2020 Europe Acquired 5
## 358 2011 North America Private 14
## 359 1991 Australia Private 34
## 360 1994 Europe Private 31
## 361 2016 South America IPO 9
## 362 2014 Europe Private 11
## 363 1997 Asia Private 28
## 364 1990 Europe Acquired 35
## 365 2009 Asia Acquired 16
## 366 2012 Europe Private 13
## 367 2021 Asia IPO 4
## 368 2000 South America Acquired 25
## 369 2013 Australia Private 12
## 370 2000 Asia Private 25
## 371 2018 Asia Acquired 7
## 372 2015 South America Private 10
## 373 2005 Asia Acquired 20
## 374 2009 North America Acquired 16
## 375 1992 South America Acquired 33
## 376 1990 North America Private 35
## 377 1990 South America Acquired 35
## 378 2006 Asia Acquired 19
## 379 1991 North America Private 34
## 380 2018 Asia Private 7
## 381 2009 South America IPO 16
## 382 1995 Asia Private 30
## 383 1997 North America Acquired 28
## 384 2018 North America Private 7
## 385 2022 Asia Private 3
## 386 1992 Europe Private 33
## 387 1994 South America Private 31
## 388 2016 South America Private 9
## 389 2001 Asia Acquired 24
## 390 2009 Europe Private 16
## 391 2009 North America Acquired 16
## 392 1998 Australia Private 27
## 393 1993 Australia Private 32
## 394 2008 Asia Private 17
## 395 2016 Europe Private 9
## 396 2002 Europe Private 23
## 397 2002 South America Private 23
## 398 2011 North America IPO 14
## 399 2017 Europe Private 8
## 400 2011 Australia Private 14
## 401 2008 North America Private 17
## 402 1997 North America Private 28
## 403 1997 Asia IPO 28
## 404 2015 Europe Private 10
## 405 2015 Asia Private 10
## 406 2012 Europe Acquired 13
## 407 2002 South America Private 23
## 408 2017 Asia Private 8
## 409 1998 Asia Private 27
## 410 2009 Australia Private 16
## 411 2008 Asia IPO 17
## 412 2001 Europe Private 24
## 413 2004 Australia Private 21
## 414 2020 Asia Private 5
## 415 2003 Asia Private 22
## 416 2000 South America Acquired 25
## 417 2015 Asia Acquired 10
## 418 2016 North America Private 9
## 419 2000 Australia Private 25
## 420 2008 North America Private 17
## 421 1995 Australia Acquired 30
## 422 2017 South America Private 8
## 423 1990 Asia Private 35
## 424 1997 Asia Private 28
## 425 1996 Asia Acquired 29
## 426 2000 Australia Private 25
## 427 1993 Asia Private 32
## 428 2014 North America Private 11
## 429 2001 Asia Private 24
## 430 2015 South America Private 10
## 431 2001 North America Private 24
## 432 2013 Asia Private 12
## 433 2005 Asia Acquired 20
## 434 2002 Europe Private 23
## 435 2014 Australia IPO 11
## 436 1993 North America Private 32
## 437 2000 Europe Private 25
## 438 2021 South America Private 4
## 439 1996 Australia Private 29
## 440 2000 North America Acquired 25
## 441 2017 Asia Private 8
## 442 2012 North America Private 13
## 443 2012 Asia IPO 13
## 444 2017 Australia Private 8
## 445 2015 Asia Acquired 10
## 446 2014 South America Private 11
## 447 2018 Asia Private 7
## 448 2008 South America Acquired 17
## 449 2022 Australia Private 3
## 450 2003 Australia IPO 22
## 451 2003 North America Acquired 22
## 452 2019 South America IPO 6
## 453 2017 South America Private 8
## 454 2006 Europe Private 19
## 455 1992 Europe Private 33
## 456 2005 Europe Acquired 20
## 457 2019 Asia Acquired 6
## 458 2018 Europe Private 7
## 459 2004 South America Private 21
## 460 2014 Asia Private 11
## 461 2012 Australia Acquired 13
## 462 2014 North America Private 11
## 463 1996 Europe Private 29
## 464 2011 Australia Private 14
## 465 2006 South America Private 19
## 466 2007 North America Acquired 18
## 467 1999 Australia Private 26
## 468 1999 North America Private 26
## 469 1996 Europe Private 29
## 470 2016 South America Private 9
## 471 2022 North America Private 3
## 472 1990 Australia Private 35
## 473 1993 Europe IPO 32
## 474 2006 Europe Private 19
## 475 1993 Australia Private 32
## 476 2015 Australia Private 10
## 477 1993 North America Private 32
## 478 1990 Asia IPO 35
## 479 1996 Europe Private 29
## 480 2019 North America Private 6
## 481 1998 Australia Acquired 27
## 482 1997 North America Private 28
## 483 2013 Australia IPO 12
## 484 2008 South America Private 17
## 485 2016 Australia Private 9
## 486 2021 Australia Private 4
## 487 2003 Australia Private 22
## 488 2007 Asia Private 18
## 489 2017 Australia Private 8
## 490 2018 Europe Private 7
## 491 2005 Europe Private 20
## 492 2020 North America Private 5
## 493 1993 Asia Acquired 32
## 494 2006 North America Private 19
## 495 2015 Europe IPO 10
## 496 1993 Europe Private 32
## 497 2019 South America Private 6
## 498 2019 Australia Private 6
## 499 2011 Europe Private 14
## 500 2000 North America Private 25# Statistik deskriptif
summary(df) # Statistik ringkas bawaan R## Startup.Name Industry Funding.Rounds Funding.Amount..M.USD.
## Length:500 Length:500 Min. :1.000 Min. : 0.57
## Class :character Class :character 1st Qu.:2.000 1st Qu.: 79.21
## Mode :character Mode :character Median :3.000 Median :156.00
## Mean :2.958 Mean :152.66
## 3rd Qu.:4.000 3rd Qu.:226.45
## Max. :5.000 Max. :299.81
## Valuation..M.USD. Revenue..M.USD. Employees Market.Share....
## Min. : 2.43 Min. : 0.12 Min. : 12 Min. : 0.100
## 1st Qu.: 557.03 1st Qu.:22.80 1st Qu.:1383 1st Qu.: 2.760
## Median :1222.58 Median :48.80 Median :2496 Median : 5.135
## Mean :1371.81 Mean :49.32 Mean :2532 Mean : 5.093
## 3rd Qu.:2052.09 3rd Qu.:74.97 3rd Qu.:3709 3rd Qu.: 7.553
## Max. :4357.49 Max. :99.71 Max. :4984 Max. :10.000
## Profitable Year.Founded Region Exit.Status
## Min. :0.000 Min. :1990 Length:500 Length:500
## 1st Qu.:0.000 1st Qu.:1998 Class :character Class :character
## Median :0.000 Median :2006 Mode :character Mode :character
## Mean :0.432 Mean :2006
## 3rd Qu.:1.000 3rd Qu.:2014
## Max. :1.000 Max. :2022
## Startup.Age
## Min. : 3.00
## 1st Qu.:11.00
## Median :19.00
## Mean :18.96
## 3rd Qu.:27.00
## Max. :35.00describe(df) # Statistik lengkap dari psych (mean, sd, skewness, kurtosis, dll)## vars n mean sd median trimmed mad min
## Startup.Name* 1 500 250.50 144.48 250.50 250.50 185.32 1.00
## Industry* 2 500 4.46 2.22 4.00 4.44 2.97 1.00
## Funding.Rounds 3 500 2.96 1.44 3.00 2.95 1.48 1.00
## Funding.Amount..M.USD. 4 500 152.66 86.68 156.00 153.49 108.39 0.57
## Valuation..M.USD. 5 500 1371.81 978.23 1222.58 1286.02 1080.25 2.43
## Revenue..M.USD. 6 500 49.32 29.27 48.80 49.31 38.65 0.12
## Employees 7 500 2532.09 1385.43 2496.50 2537.24 1753.92 12.00
## Market.Share.... 8 500 5.09 2.81 5.14 5.11 3.55 0.10
## Profitable 9 500 0.43 0.50 0.00 0.42 0.00 0.00
## Year.Founded 10 500 2006.04 9.35 2006.00 2006.04 11.86 1990.00
## Region* 11 500 2.93 1.40 3.00 2.91 1.48 1.00
## Exit.Status* 12 500 2.48 0.82 3.00 2.60 0.00 1.00
## Startup.Age 13 500 18.96 9.35 19.00 18.96 11.86 3.00
## max range skew kurtosis se
## Startup.Name* 500.00 499.00 0.00 -1.21 6.46
## Industry* 8.00 7.00 0.04 -1.11 0.10
## Funding.Rounds 5.00 4.00 0.06 -1.33 0.06
## Funding.Amount..M.USD. 299.81 299.24 -0.09 -1.14 3.88
## Valuation..M.USD. 4357.49 4355.06 0.69 -0.12 43.75
## Revenue..M.USD. 99.71 99.59 -0.01 -1.24 1.31
## Employees 4984.00 4972.00 -0.05 -1.11 61.96
## Market.Share.... 10.00 9.90 -0.04 -1.13 0.13
## Profitable 1.00 1.00 0.27 -1.93 0.02
## Year.Founded 2022.00 32.00 0.00 -1.18 0.42
## Region* 5.00 4.00 0.07 -1.27 0.06
## Exit.Status* 3.00 2.00 -1.09 -0.64 0.04
## Startup.Age 35.00 32.00 0.00 -1.18 0.42df <- df %>%
mutate(
Industry = as.factor(Industry),
Region = as.factor(Region),
Exit.Status = as.factor(Exit.Status)
)Mengonversi kolom kategorikal (Industry, Region, Exit.Status) menjadi tipe faktor agar bisa digunakan dalam analisis statistik.
df <- df %>%
rename(
FundingAmount = `Funding.Amount..M.USD.`,
Valuation = `Valuation..M.USD.`,
MarketShareRaw = `Market.Share....`,
YearFounded = `Year.Founded`
)Mengganti nama kolom yang panjang agar lebih mudah dipanggil dalam kode berikutnya.
Menghapus Outlier
remove_outliers <- function(x) {
qnt <- quantile(x, probs = c(0.25, 0.75), na.rm = TRUE)
H <- 1.5 * IQR(x, na.rm = TRUE)
x >= (qnt[1] - H) & x <= (qnt[2] + H)
}Menghapus Outlier Fungsi remove_outliers() dibuat menggunakan metode IQR (Interquartile Range)
filter_funding <- remove_outliers(df$FundingAmount)
filter_valuation <- remove_outliers(df$Valuation)
filter_rows <- filter_funding & filter_valuation
df_clean <- df[filter_rows, ]Menyaring baris yang tidak outlier di kedua variabel dependen (Funding & Valuation)
Standarisasi Kovariat
df_clean <- df_clean %>%
mutate(
Employees = as.numeric(scale(Employees)),
StartupAge = as.numeric(scale(Startup.Age)),
MarketShare = as.numeric(scale(MarketShareRaw))
)Siapkan DV dan IV
iv1 <- df_clean$Industry
iv2 <- df_clean$Exit.Status## Menstandarisasi variabel numerik (kovariat) agar memiliki mean 0 dan standar deviasi 1, penting untuk MANCOVA.
## Normalitas Multivariat
dv <- df_clean %>% select(FundingAmount, Valuation)
mvn_result <- mvn(data = dv)Mengecek apakah variabel dependen mengikuti distribusi normal multivariat (syarat MANOVA/MANCOVA).
Box’s M Test (Homogenitas Varians-Kovarians)
boxM_industry <- boxM(dv, df_clean$Industry)
print("Box's M Test - Industry:")## [1] "Box's M Test - Industry:"print(boxM_industry)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: dv
## Chi-Sq (approx.) = 27.027, df = 21, p-value = 0.1699boxM_exitstatus <- boxM(dv, df_clean$Exit.Status)
print("Box's M Test - Exit.Status:")## [1] "Box's M Test - Exit.Status:"print(boxM_exitstatus)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: dv
## Chi-Sq (approx.) = 2.3109, df = 6, p-value = 0.889Menguji apakah matriks kovarians antar grup (faktor) adalah homogen, syarat penting dalam MANOVA.
Korelasi antar DV dan Bartlett test
library(psych)
# Misal dv berisi FundingAmount dan Valuation
dv <- df_clean[, c("FundingAmount", "Valuation")]
# Cek korelasi
print(cor(dv))## FundingAmount Valuation
## FundingAmount 1.00000 0.79467
## Valuation 0.79467 1.00000# Uji Bartlett untuk korelasi antar dv (syarat MANOVA / EFA)
bartlett_test <- cortest.bartlett(cor(dv), n = nrow(dv))
print(bartlett_test)## $chisq
## [1] 495.6637
##
## $p.value
## [1] 8.345977e-110
##
## $df
## [1] 1# Bartlett one-way: FundingAmount berdasarkan Exit.Status
bartlett.test(FundingAmount ~ Exit.Status, data = df_clean)##
## Bartlett test of homogeneity of variances
##
## data: FundingAmount by Exit.Status
## Bartlett's K-squared = 1.0183, df = 2, p-value = 0.601# Bartlett one-way: Valuation berdasarkan Industry
bartlett.test(Valuation ~ Industry, data = df_clean)##
## Bartlett test of homogeneity of variances
##
## data: Valuation by Industry
## Bartlett's K-squared = 13.303, df = 7, p-value = 0.06507# Bartlett two-way (gabungan): buat kombinasi
df_clean$Group <- interaction(df_clean$Exit.Status, df_clean$Industry)
bartlett.test(FundingAmount ~ Group, data = df_clean)##
## Bartlett test of homogeneity of variances
##
## data: FundingAmount by Group
## Bartlett's K-squared = 13.229, df = 23, p-value = 0.9469print(cor(dv))## FundingAmount Valuation
## FundingAmount 1.00000 0.79467
## Valuation 0.79467 1.00000bartlett_test <- cortest.bartlett(cor(dv), n = nrow(dv))
print(bartlett_test)## $chisq
## [1] 495.6637
##
## $p.value
## [1] 8.345977e-110
##
## $df
## [1] 1# Korelasi antar kovariat
df_clean %>%
select(Employees, YearFounded, MarketShare) %>%
cor()## Employees YearFounded MarketShare
## Employees 1.000000000 0.007788686 0.032555543
## YearFounded 0.007788686 1.000000000 -0.005930447
## MarketShare 0.032555543 -0.005930447 1.000000000shapiro.test(df_clean$Employees)##
## Shapiro-Wilk normality test
##
## data: df_clean$Employees
## W = 0.96304, p-value = 7.066e-10shapiro.test(df_clean$YearFounded)##
## Shapiro-Wilk normality test
##
## data: df_clean$YearFounded
## W = 0.95504, p-value = 3.409e-11shapiro.test(df_clean$MarketShare)##
## Shapiro-Wilk normality test
##
## data: df_clean$MarketShare
## W = 0.96021, p-value = 2.314e-10# Cek leverage dan outlier (opsional)
plot(lm(FundingAmount ~ Employees + YearFounded + MarketShare, data = df_clean), which = 4)
# Cek leverage dan outlier (opsional)
plot(lm(Valuation ~ Employees + YearFounded + MarketShare, data = df_clean), which = 4)
Mengecek apakah Funding dan Valuation berkorelasi signifikan, yang menjadi syarat untuk MANOVA.
MANOVA
manova_model <- manova(cbind(FundingAmount, Valuation) ~ Industry + Exit.Status + Industry:Exit.Status, data = df_clean)
summary(manova_model, test = "Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## Industry 7 0.049306 1.71515 14 950 0.0476 *
## Exit.Status 2 0.001877 0.22307 4 950 0.9256
## Industry:Exit.Status 14 0.061028 1.06788 28 950 0.3709
## Residuals 475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Menganalisis pengaruh simultan dari faktor-faktor kategorikal terhadap dua DV (Funding & Valuation).
MANCOVA (dengan Kovariat)
# Model MANCOVA dengan interaksi dua arah
mancova_model <- manova(cbind(FundingAmount, Valuation) ~ Industry + Exit.Status + Industry:Exit.Status + Employees + Startup.Age+ MarketShare, data = df_clean)
# Ringkasan hasil menggunakan Pillai's Trace
summary(mancova_model, test = "Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## Industry 7 0.049959 1.72749 14 944 0.04542 *
## Exit.Status 2 0.001901 0.22454 4 944 0.92475
## Employees 1 0.002970 0.70149 2 471 0.49636
## Startup.Age 1 0.002051 0.48407 2 471 0.61658
## MarketShare 1 0.013210 3.15252 2 471 0.04365 *
## Industry:Exit.Status 14 0.066430 1.15829 28 944 0.26152
## Residuals 472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Menambahkan variabel numerik (kovariat) untuk mengontrol pengaruh mereka terhadap DV.
Uji Homogenitas Regresi: Interaksi antara faktor dan kovariat
factors <- c("Industry", "Region", "Exit.Status")
covariates <- c("Employees", "YearFounded", "MarketShare")
dependent_vars <- c("FundingAmount", "Valuation")
# Homogenitas Kemiringan Regresi (uji interaksi kovariat dan faktor)
lm_slope_check1 <- lm(FundingAmount ~ Industry * Employees, data = df_clean)
anova(lm_slope_check1)## Analysis of Variance Table
##
## Response: FundingAmount
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 95148 13592.6 1.8355 0.07854 .
## Employees 1 6409 6409.2 0.8655 0.35267
## Industry:Employees 7 51375 7339.3 0.9911 0.43686
## Residuals 483 3576780 7405.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lm_slope_check2 <- lm(Valuation ~ Industry * Employees, data = df_clean)
anova(lm_slope_check2)## Analysis of Variance Table
##
## Response: Valuation
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 14619889 2088556 2.2398 0.0300 *
## Employees 1 68964 68964 0.0740 0.7858
## Industry:Employees 7 3501455 500208 0.5364 0.8070
## Residuals 483 450384230 932473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lm_slope_check3 <- lm(FundingAmount ~ Industry * Startup.Age, data = df_clean)
anova(lm_slope_check3)## Analysis of Variance Table
##
## Response: FundingAmount
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 95148 13592.6 1.8330 0.0790 .
## Startup.Age 1 7238 7237.9 0.9760 0.3237
## Industry:Startup.Age 7 45632 6518.8 0.8791 0.5228
## Residuals 483 3581695 7415.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lm_slope_check4 <- lm(Valuation ~ Industry * Startup.Age, data = df_clean)
anova(lm_slope_check4)## Analysis of Variance Table
##
## Response: Valuation
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 14619889 2088556 2.2455 0.02959 *
## Startup.Age 1 531580 531580 0.5715 0.45003
## Industry:Startup.Age 7 4173761 596252 0.6410 0.72196
## Residuals 483 449249308 930123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lm_slope_check5 <- lm(FundingAmount ~ Industry * MarketShare, data = df_clean)
anova(lm_slope_check5)## Analysis of Variance Table
##
## Response: FundingAmount
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 95148 13592.6 1.8616 0.07395 .
## MarketShare 1 16613 16612.8 2.2753 0.13210
## Industry:MarketShare 7 91375 13053.5 1.7878 0.08759 .
## Residuals 483 3526577 7301.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lm_slope_check6 <- lm(Valuation ~ Industry * MarketShare, data = df_clean)
anova(lm_slope_check6)## Analysis of Variance Table
##
## Response: Valuation
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 14619889 2088556 2.3372 0.023587 *
## MarketShare 1 5464522 5464522 6.1151 0.013746 *
## Industry:MarketShare 7 16877587 2411084 2.6981 0.009456 **
## Residuals 483 431612540 893608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Memastikan tidak ada interaksi signifikan antara faktor dan kovariat. Jika ada, asumsi kemiringan homogen (slope homogenity) dilanggar.
Analisis per Industri
industries <- unique(df_clean$Industry)
for (ind in industries) {
cat("MANCOVA untuk industri:", ind, "\n")
df_subset <- df_clean %>% filter(Industry == ind)
mancova_ind <- manova(cbind(FundingAmount, Valuation) ~ Region + Exit.Status + Employees + YearFounded + MarketShare,
data = df_subset)
print(summary(mancova_ind, test = "Wilks"))
}## MANCOVA untuk industri: IoT
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.87069 0.89606 8 100 0.52288
## Exit.Status 2 0.90909 1.22026 4 100 0.30704
## Employees 1 0.97016 0.76882 2 50 0.46896
## YearFounded 1 0.90884 2.50750 2 50 0.09167 .
## MarketShare 1 0.88891 3.12429 2 50 0.05266 .
## Residuals 51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## MANCOVA untuk industri: EdTech
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.87283 1.10833 8 126 0.3621
## Exit.Status 2 0.96986 0.48579 4 126 0.7461
## Employees 1 0.99565 0.13772 2 63 0.8716
## YearFounded 1 0.98711 0.41140 2 63 0.6645
## MarketShare 1 0.93586 2.15903 2 63 0.1239
## Residuals 64
## MANCOVA untuk industri: Gaming
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.85399 1.02642 8 100 0.42137
## Exit.Status 2 0.86736 1.84357 4 100 0.12641
## Employees 1 0.99834 0.04145 2 50 0.95943
## YearFounded 1 0.99758 0.06072 2 50 0.94116
## MarketShare 1 0.90895 2.50433 2 50 0.09193 .
## Residuals 51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## MANCOVA untuk industri: AI
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.91807 0.55674 8 102 0.8108
## Exit.Status 2 0.90346 1.32777 4 102 0.2647
## Employees 1 0.94395 1.51424 2 51 0.2297
## YearFounded 1 0.95777 1.12437 2 51 0.3328
## MarketShare 1 0.99195 0.20701 2 51 0.8137
## Residuals 52
## MANCOVA untuk industri: HealthTech
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.75041 1.46667 8 76 0.18358
## Exit.Status 2 0.94362 0.55938 4 76 0.69282
## Employees 1 0.94864 1.02869 2 38 0.36722
## YearFounded 1 0.96227 0.74497 2 38 0.48156
## MarketShare 1 0.87016 2.83501 2 38 0.07119 .
## Residuals 39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## MANCOVA untuk industri: FinTech
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.85484 1.22368 8 120 0.2910
## Exit.Status 2 0.95955 0.62584 4 120 0.6450
## Employees 1 0.95954 1.26488 2 60 0.2897
## YearFounded 1 0.95751 1.33134 2 60 0.2718
## MarketShare 1 0.99365 0.19162 2 60 0.8261
## Residuals 61
## MANCOVA untuk industri: Cybersecurity
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.80722 1.13021 8 80 0.3524
## Exit.Status 2 0.92552 0.78918 4 80 0.5356
## Employees 1 0.98485 0.30766 2 40 0.7369
## YearFounded 1 0.97475 0.51806 2 40 0.5996
## MarketShare 1 0.98866 0.22948 2 40 0.7960
## Residuals 41
## MANCOVA untuk industri: E-Commerce
## Df Wilks approx F num Df den Df Pr(>F)
## Region 4 0.79955 1.7456 8 118 0.0949 .
## Exit.Status 2 0.90951 1.4327 4 118 0.2275
## Employees 1 0.99644 0.1054 2 59 0.9001
## YearFounded 1 0.97436 0.7763 2 59 0.4648
## MarketShare 1 0.69263 13.0914 2 59 1.971e-05 ***
## Residuals 60
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Melakukan MANCOVA secara spesifik per industri untuk melihat efek Region dan Exit.Status di dalam satu sektor.
Exploratory Data Analysis (EDA)
library(corrplot)## Warning: package 'corrplot' was built under R version 4.4.3## corrplot 0.95 loadedlibrary(ggplot2)
ggplot(df_clean, aes(x = FundingAmount)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df_clean, aes(x = FundingAmount, y = Valuation)) +
geom_point(aes(color = Industry), alpha = 0.7) +
labs(title = "Hubungan Funding dan Valuation", x = "Funding (M USD)", y = "Valuation (M USD)")
# Ubah data dari wide ke long format untuk plotting
dv_long <- dv %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Value")
# QQ Plot untuk setiap variabel dependen
ggplot(dv_long, aes(sample = Value)) +
stat_qq() +
stat_qq_line(color = "red") +
facet_wrap(~Variable, scales = "free") +
ggtitle("QQ Plot untuk Setiap Variabel Dependen") +
theme_minimal()
# Histogram untuk setiap variabel dependen
ggplot(dv_long, aes(x = Value)) +
geom_histogram(bins = 30, fill = "skyblue", color = "black") +
facet_wrap(~Variable, scales = "free") +
ggtitle("Histogram untuk Setiap Variabel Dependen") +
theme_minimal()
# Boxplot Valuation dan FundingAmount berdasarkan Industry, Exit.Status, Region
# Fungsi bantu untuk plot boxplot per kategori variabel
plot_boxplot <- function(data, yvar, xvar, title) {
ggplot(data, aes_string(x = xvar, y = yvar)) +
geom_boxplot(fill = "skyblue", outlier.color = "red") +
labs(title = title, x = xvar, y = yvar) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}
# Boxplot Valuation
plot_boxplot(df_clean, "Valuation", "Industry", "Boxplot Valuation by Industry")## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
plot_boxplot(df_clean, "FundingAmount", "Industry", "Boxplot Funding by Industry")
plot_boxplot(df_clean, "Valuation", "Exit.Status", "Boxplot Valuation by Exit.Status")
plot_boxplot(df_clean, "FundingAmount", "Exit.Status", "Boxplot Funding by Exit.Status")
plot_boxplot(df_clean, "Valuation", "Region", "Boxplot Valuation by Region")
plot_boxplot(df_clean, "FundingAmount", "Region", "Boxplot Funding by Region")
# Heatmap Korelasi antar variabel numerik
# Variabel numerik yang ingin dikorelasi
num_vars <- c("Valuation", "FundingAmount", "Employees", "MarketShare", "YearFounded")
# Loop per Industry, hitung korelasi, tampilkan heatmap korelasi
industries <- unique(df_clean$Industry)
for (ind in industries) {
cat("\n==== Korelasi untuk Industry:", ind, "====\n")
df_sub <- df_clean %>%
filter(Industry == ind) %>%
select(all_of(num_vars))
# Hitung korelasi jika data cukup (minimal 5 data)
if (nrow(df_sub) >= 5) {
cor_mat <- cor(df_sub, use = "pairwise.complete.obs")
print(cor_mat)
# Plot heatmap korelasi
corrplot(cor_mat, method = "color", addCoef.col = "black", number.cex = 0.7,
tl.cex = 0.8, tl.col = "black", diag = FALSE,
main = paste("Heatmap Korelasi - Industry:", ind))
} else {
cat("Data terlalu sedikit untuk korelasi (", nrow(df_sub), " baris).\n")
}
}##
## ==== Korelasi untuk Industry: IoT ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.0000000 0.814738981 -0.10920120 -0.3020530 0.128559331
## FundingAmount 0.8147390 1.000000000 -0.04850536 -0.2744601 -0.002694575
## Employees -0.1092012 -0.048505359 1.00000000 0.1814739 0.131011843
## MarketShare -0.3020530 -0.274460147 0.18147391 1.0000000 -0.134995963
## YearFounded 0.1285593 -0.002694575 0.13101184 -0.1349960 1.000000000
##
## ==== Korelasi untuk Industry: EdTech ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.000000000 0.75243437 -0.003792397 0.21142609 -0.04809204
## FundingAmount 0.752434367 1.00000000 -0.051162712 0.26952018 0.07415474
## Employees -0.003792397 -0.05116271 1.000000000 0.06829533 -0.08369190
## MarketShare 0.211426087 0.26952018 0.068295329 1.00000000 0.05537131
## YearFounded -0.048092038 0.07415474 -0.083691898 0.05537131 1.00000000
##
## ==== Korelasi untuk Industry: Gaming ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.000000000 0.76818386 -0.07985884 0.27522415 -0.005135801
## FundingAmount 0.768183863 1.00000000 -0.11121468 0.17588445 -0.038828614
## Employees -0.079858841 -0.11121468 1.00000000 0.01786377 0.053892964
## MarketShare 0.275224151 0.17588445 0.01786377 1.00000000 -0.022104920
## YearFounded -0.005135801 -0.03882861 0.05389296 -0.02210492 1.000000000
##
## ==== Korelasi untuk Industry: AI ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.00000000 0.79292460 0.17931977 0.09682286 -0.01146661
## FundingAmount 0.79292460 1.00000000 0.24143061 0.15045233 -0.09185258
## Employees 0.17931977 0.24143061 1.00000000 0.14559039 0.02327304
## MarketShare 0.09682286 0.15045233 0.14559039 1.00000000 -0.03916184
## YearFounded -0.01146661 -0.09185258 0.02327304 -0.03916184 1.00000000
##
## ==== Korelasi untuk Industry: HealthTech ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.0000000 0.83530945 0.12112027 0.29806466 -0.18425982
## FundingAmount 0.8353094 1.00000000 0.17958301 0.09540439 -0.24661228
## Employees 0.1211203 0.17958301 1.00000000 -0.09958066 -0.08317693
## MarketShare 0.2980647 0.09540439 -0.09958066 1.00000000 -0.02707538
## YearFounded -0.1842598 -0.24661228 -0.08317693 -0.02707538 1.00000000
##
## ==== Korelasi untuk Industry: FinTech ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.00000000 0.73497080 0.0254031733 -0.03258144 -0.1843740335
## FundingAmount 0.73497080 1.00000000 0.1393198089 -0.04659909 -0.1900154651
## Employees 0.02540317 0.13931981 1.0000000000 0.03641118 0.0006129848
## MarketShare -0.03258144 -0.04659909 0.0364111764 1.00000000 0.1366373349
## YearFounded -0.18437403 -0.19001547 0.0006129848 0.13663733 1.0000000000
##
## ==== Korelasi untuk Industry: Cybersecurity ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.00000000 0.83339095 0.095154461 0.04620644 -0.072252410
## FundingAmount 0.83339095 1.00000000 0.048770492 0.14068126 -0.054180109
## Employees 0.09515446 0.04877049 1.000000000 -0.06556492 -0.009830092
## MarketShare 0.04620644 0.14068126 -0.065564921 1.00000000 -0.166612312
## YearFounded -0.07225241 -0.05418011 -0.009830092 -0.16661231 1.000000000
##
## ==== Korelasi untuk Industry: E-Commerce ====
## Valuation FundingAmount Employees MarketShare YearFounded
## Valuation 1.00000000 0.84536824 -0.04217710 0.24257401 0.04372064
## FundingAmount 0.84536824 1.00000000 -0.01428470 0.01264458 0.11010735
## Employees -0.04217710 -0.01428470 1.00000000 -0.08625305 0.06677905
## MarketShare 0.24257401 0.01264458 -0.08625305 1.00000000 0.05908967
## YearFounded 0.04372064 0.11010735 0.06677905 0.05908967 1.00000000
# Pilih variabel numerik yang diinginkan
num_vars <- df_clean %>% select(Valuation, FundingAmount, Employees, MarketShare, YearFounded)
# Korelasi matrix
cor_mat <- cor(num_vars, use = "pairwise.complete.obs")
# Heatmap korelasi dengan corrplot
corrplot(cor_mat, method = "color", addCoef.col = "black", number.cex = 0.8,
tl.cex = 0.8, tl.col = "black", diag = FALSE,
title = "Heatmap Korelasi Variabel Numerik", mar=c(0,0,1,0))
# Scatterplot Matrix (Pairplot) dengan warna kategori
# Pairplot untuk Valuation, FundingAmount, Employees, dengan warna Industry (ubah sesuai kebutuhan)
GGally::ggpairs(df_clean, columns = c("Valuation", "FundingAmount", "Employees"),
aes(color = Industry, alpha = 0.6),
title = "Scatterplot Matrix dengan warna Industry")## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
# Alternatif warna berdasarkan Region
GGally::ggpairs(df_clean, columns = c("Valuation", "FundingAmount", "Employees"),
aes(color = Region, alpha = 0.6),
title = "Scatterplot Matrix dengan warna Region")
# Alternatif warna berdasarkan Exit.Status
GGally::ggpairs(df_clean, columns = c("Valuation", "FundingAmount", "Employees"),
aes(color = Exit.Status, alpha = 0.6),
title = "Scatterplot Matrix dengan warna Exit.Status")
# Assumption Checks tambahan: Levene's Test visual (distribusi residual)
# Fungsi plot residual boxplot per kelompok (sebagai visualisasi Levene's Test)
plot_levene_residual <- function(model, factor_var, dv_name) {
res <- residuals(model)
df_res <- data.frame(residuals = res, group = df_clean[[factor_var]])
ggplot(df_res, aes(x = group, y = residuals)) +
geom_boxplot(fill = "lightgreen") +
labs(title = paste("Distribusi Residual", dv_name, "berdasarkan", factor_var),
x = factor_var, y = "Residuals") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}Menampilkan visualisasi distribusi dan hubungan antar variabel: -Histogram Funding dan Valuation -Scatter plot antara Funding dan Valuation -Boxplot Funding berdasarkan Exit.Status
Uji Normalitas & Multikolinearitas
mvn_result <- mvn(data = dv)
print(mvn_result$multivariateNormality)## NULLTujuan: Mengecek normalitas kovariat secara univariat.
Mengecek multikolinearitas antar kovariat dengan VIF (sebaiknya < 5).
Uji Multikolinearitas Covariates
vif_lm <- lm(FundingAmount ~ Employees + Startup.Age+ MarketShare, data = df_clean)
vif(vif_lm)## Employees Startup.Age MarketShare
## 1.001125 1.000099 1.001099# Dapatkan semua kategori industri
industries <- unique(df_clean$Industry)
# Loop untuk analisis MANCOVA per industri
for (ind in industries) {
cat("\n============================\n")
cat("Industry:", ind, "\n")
cat("============================\n")
df_subset <- df_clean %>% filter(Industry == ind)
# Pastikan ada cukup data per kelompok (minimal 20-30 baris idealnya)
cat("Jumlah data:", nrow(df_subset), "\n")
if (nrow(df_subset) > 30) {
mancova_ind <- manova(cbind(FundingAmount, Valuation) ~ Startup.Age + Exit.Status + Employees + YearFounded + MarketShare, data = df_subset)
print(summary(mancova_ind, test = "Wilks"))
} else {
cat("Data terlalu sedikit untuk analisis MANCOVA yang stabil.\n")
}
}##
## ============================
## Industry: IoT
## ============================
## Jumlah data: 61
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.94595 1.54283 2 54 0.2230
## Exit.Status 2 0.90548 1.37420 4 108 0.2477
## Employees 1 0.97431 0.71193 2 54 0.4952
## MarketShare 1 0.92950 2.04775 2 54 0.1389
## Residuals 55
##
## ============================
## Industry: EdTech
## ============================
## Jumlah data: 74
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.96908 1.06888 2 67 0.34917
## Exit.Status 2 0.98925 0.18157 4 134 0.94762
## Employees 1 0.99400 0.20225 2 67 0.81739
## MarketShare 1 0.93001 2.52097 2 67 0.08798 .
## Residuals 68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ============================
## Industry: Gaming
## ============================
## Jumlah data: 61
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.99674 0.08827 2 54 0.91565
## Exit.Status 2 0.90213 1.42688 4 108 0.22998
## Employees 1 0.98701 0.35525 2 54 0.70263
## MarketShare 1 0.89763 3.07916 2 54 0.05416 .
## Residuals 55
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ============================
## Industry: AI
## ============================
## Jumlah data: 62
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.97942 0.57794 2 55 0.5644
## Exit.Status 2 0.92174 1.14372 4 110 0.3399
## Employees 1 0.94563 1.58124 2 55 0.2149
## MarketShare 1 0.98098 0.53318 2 55 0.5897
## Residuals 56
##
## ============================
## Industry: HealthTech
## ============================
## Jumlah data: 49
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.93557 1.4462 2 42 0.2470
## Exit.Status 2 0.97789 0.2360 4 84 0.9173
## Employees 1 0.97098 0.6277 2 42 0.5387
## MarketShare 1 0.82944 4.3182 2 42 0.0197 *
## Residuals 43
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ============================
## Industry: FinTech
## ============================
## Jumlah data: 71
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.95822 1.39512 2 64 0.2552
## Exit.Status 2 0.93611 1.07396 4 128 0.3722
## Employees 1 0.96776 1.06612 2 64 0.3504
## MarketShare 1 0.99591 0.13157 2 64 0.8770
## Residuals 65
##
## ============================
## Industry: Cybersecurity
## ============================
## Jumlah data: 51
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.99440 0.12387 2 44 0.8838
## Exit.Status 2 0.91445 1.00613 4 88 0.4088
## Employees 1 0.98916 0.24108 2 44 0.7868
## MarketShare 1 0.96180 0.87388 2 44 0.4244
## Residuals 45
##
## ============================
## Industry: E-Commerce
## ============================
## Jumlah data: 70
## Df Wilks approx F num Df den Df Pr(>F)
## Startup.Age 1 0.97630 0.7646 2 63 0.4698079
## Exit.Status 2 0.92106 1.3221 4 126 0.2653477
## Employees 1 0.99115 0.2812 2 63 0.7558362
## MarketShare 1 0.76115 9.8847 2 63 0.0001846 ***
## Residuals 64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ANOVA & ANCOVA
anova_funding <- aov(FundingAmount ~ Industry + Region + Exit.Status + Employees + YearFounded + MarketShare, data = df_clean)
anova_valuation <- aov(Valuation ~ Industry + Region + Exit.Status + Employees + YearFounded + MarketShare, data = df_clean)
cat("==== ANOVA FundingAmount ====\n")## ==== ANOVA FundingAmount ====summary(anova_funding)## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 95148 13593 1.830 0.0795 .
## Region 4 18119 4530 0.610 0.6557
## Exit.Status 2 5719 2860 0.385 0.6806
## Employees 1 7030 7030 0.947 0.3311
## YearFounded 1 8343 8343 1.123 0.2897
## MarketShare 1 15721 15721 2.117 0.1463
## Residuals 482 3579633 7427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("\n==== ANOVA Valuation ====\n")##
## ==== ANOVA Valuation ====summary(anova_valuation)## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 14619889 2088556 2.269 0.0279 *
## Region 4 3726348 931587 1.012 0.4006
## Exit.Status 2 599400 299700 0.326 0.7222
## Employees 1 105273 105273 0.114 0.7353
## YearFounded 1 623716 623716 0.678 0.4108
## MarketShare 1 5308872 5308872 5.769 0.0167 *
## Residuals 482 443591040 920313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Uji Asumsi Sephiro
cat("\n==== Shapiro-Wilk Normality Test ====\n")##
## ==== Shapiro-Wilk Normality Test ====cat("FundingAmount:\n")## FundingAmount:print(shapiro.test(residuals(anova_funding)))##
## Shapiro-Wilk normality test
##
## data: residuals(anova_funding)
## W = 0.97287, p-value = 5.465e-08cat("Valuation:\n")## Valuation:print(shapiro.test(residuals(anova_valuation)))##
## Shapiro-Wilk normality test
##
## data: residuals(anova_valuation)
## W = 0.96669, p-value = 3.248e-09library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.4.3# QQ Plot
ggqqplot(residuals(anova_funding), title = "QQ Plot Residuals - FundingAmount")
ggqqplot(residuals(anova_valuation), title = "QQ Plot Residuals - Valuation")
Levine Test
cat("\n==== Levene's Test (Homoskedastisitas) ====\n")##
## ==== Levene's Test (Homoskedastisitas) ====cat("FundingAmount by Industry:\n")## FundingAmount by Industry:print(leveneTest(FundingAmount ~ Industry, data = df_clean))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 0.5122 0.8256
## 491cat("Valuation by Industry:\n")## Valuation by Industry:print(leveneTest(Valuation ~ Industry, data = df_clean))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 7 1.9835 0.0556 .
## 491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Uji Linearitas Kovariat
par(mfrow = c(2,2))
plot(anova_funding, which = 1, main = "FundingAmount: Residual vs Fitted")
plot(anova_valuation, which = 1, main = "Valuation: Residual vs Fitted")
Uji Interaksi
cat("\n==== Homogenitas Slope (Interaksi dengan MarketShare) ====\n")##
## ==== Homogenitas Slope (Interaksi dengan MarketShare) ====model_interact_funding <- lm(FundingAmount ~ Industry * MarketShare, data = df_clean)
model_interact_valuation <- lm(Valuation ~ Industry * MarketShare, data = df_clean)
cat("FundingAmount:\n")## FundingAmount:print(anova(model_interact_funding))## Analysis of Variance Table
##
## Response: FundingAmount
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 95148 13592.6 1.8616 0.07395 .
## MarketShare 1 16613 16612.8 2.2753 0.13210
## Industry:MarketShare 7 91375 13053.5 1.7878 0.08759 .
## Residuals 483 3526577 7301.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Valuation:\n")## Valuation:print(anova(model_interact_valuation))## Analysis of Variance Table
##
## Response: Valuation
## Df Sum Sq Mean Sq F value Pr(>F)
## Industry 7 14619889 2088556 2.3372 0.023587 *
## MarketShare 1 5464522 5464522 6.1151 0.013746 *
## Industry:MarketShare 7 16877587 2411084 2.6981 0.009456 **
## Residuals 483 431612540 893608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hasil Koreksi Bonferroni
p_funding <- summary(anova_funding)[[1]][["Pr(>F)"]]
p_valuation <- summary(anova_valuation)[[1]][["Pr(>F)"]]
p_all <- c(p_funding, p_valuation)
p_bonf <- p.adjust(p_all, method = "bonferroni")
effect_names <- rep(rownames(summary(anova_funding)[[1]]), 2)
dv_names <- c(rep("FundingAmount", length(p_funding)), rep("Valuation", length(p_valuation)))
result_bonf <- data.frame(
DV = dv_names,
Effect = effect_names,
p_value = round(p_all, 4),
p_bonferroni = round(p_bonf, 4),
Significant = ifelse(p_bonf < 0.05, "Yes", "No")
)
cat("\n==== Hasil ANOVA dengan Koreksi Bonferroni ====\n")##
## ==== Hasil ANOVA dengan Koreksi Bonferroni ====print(result_bonf)## DV Effect p_value p_bonferroni Significant
## 1 FundingAmount Industry 0.0795 0.9540 No
## 2 FundingAmount Region 0.6557 1.0000 No
## 3 FundingAmount Exit.Status 0.6806 1.0000 No
## 4 FundingAmount Employees 0.3311 1.0000 No
## 5 FundingAmount YearFounded 0.2897 1.0000 No
## 6 FundingAmount MarketShare 0.1463 1.0000 No
## 7 FundingAmount Residuals NA NA <NA>
## 8 Valuation Industry 0.0279 0.3348 No
## 9 Valuation Region 0.4006 1.0000 No
## 10 Valuation Exit.Status 0.7222 1.0000 No
## 11 Valuation Employees 0.7353 1.0000 No
## 12 Valuation YearFounded 0.4108 1.0000 No
## 13 Valuation MarketShare 0.0167 0.2003 No
## 14 Valuation Residuals NA NA <NA>