Project UAS Anmul_Kelompok 10
Berlianti Debby Maharani, Shellomitha Sulvana Dewi, Nisrina Afaf
2025-05-27
# Baca data
library(readr)## Warning: package 'readr' was built under R version 4.4.3data <- read.csv("C:\\Users\\Shellomitha Sulvana\\Downloads\\Extended_Employee_Performance_and_Productivity_Data.csv", stringsAsFactors = FALSE)
print(data)## Employee_ID Department Gender Age Job_Title
## 1 1 IT Male 55 Specialist
## 2 2 Finance Male 29 Developer
## 3 3 Finance Male 55 Specialist
## 4 4 Customer Support Female 48 Analyst
## 5 5 Engineering Female 36 Analyst
## 6 6 IT Male 43 Manager
## 7 7 IT Male 37 Technician
## 8 8 Engineering Female 55 Engineer
## 9 9 Marketing Female 55 Technician
## 10 10 Engineering Female 45 Consultant
## 11 11 Customer Support Male 52 Engineer
## 12 12 Customer Support Male 27 Technician
## 13 13 HR Male 51 Technician
## 14 14 Engineering Male 27 Analyst
## 15 15 Finance Male 46 Analyst
## 16 16 Customer Support Male 26 Developer
## 17 17 Operations Male 29 Engineer
## 18 18 Sales Other 28 Developer
## 19 19 Customer Support Other 56 Developer
## 20 20 Finance Male 23 Technician
## 21 21 Operations Female 33 Manager
## 22 22 Sales Male 59 Manager
## 23 23 Finance Male 26 Specialist
## 24 24 Sales Male 58 Consultant
## 25 25 HR Female 38 Analyst
## 26 26 Customer Support Male 38 Specialist
## 27 27 Finance Male 45 Engineer
## 28 28 Legal Male 43 Manager
## 29 29 Engineering Male 53 Specialist
## 30 30 Finance Male 52 Analyst
## 31 31 Customer Support Male 58 Consultant
## 32 32 Legal Female 24 Manager
## 33 33 Customer Support Male 29 Analyst
## 34 34 Sales Female 25 Engineer
## 35 35 Marketing Male 41 Analyst
## 36 36 HR Female 42 Engineer
## 37 37 Sales Male 43 Consultant
## 38 38 Sales Other 46 Consultant
## 39 39 IT Male 48 Specialist
## 40 40 HR Female 58 Engineer
## 41 41 HR Female 44 Analyst
## 42 42 Marketing Male 29 Specialist
## 43 43 Customer Support Male 60 Engineer
## 44 44 Marketing Female 43 Manager
## 45 45 Customer Support Female 46 Developer
## 46 46 HR Female 29 Specialist
## 47 47 HR Male 32 Technician
## 48 48 Operations Male 31 Specialist
## 49 49 Legal Male 40 Specialist
## 50 50 Operations Other 57 Developer
## 51 51 Legal Female 35 Consultant
## 52 52 HR Male 40 Developer
## 53 53 Engineering Male 28 Consultant
## 54 54 Engineering Female 37 Technician
## 55 55 Operations Female 34 Technician
## 56 56 Marketing Female 34 Developer
## 57 57 Customer Support Female 33 Specialist
## 58 58 Finance Male 23 Developer
## 59 59 IT Other 52 Technician
## 60 60 Customer Support Female 54 Manager
## 61 61 Operations Female 34 Consultant
## 62 62 Engineering Male 53 Specialist
## 63 63 Sales Male 27 Manager
## 64 64 Finance Female 58 Manager
## 65 65 Sales Female 41 Specialist
## 66 66 Legal Male 37 Technician
## 67 67 Sales Male 49 Manager
## 68 68 HR Male 39 Consultant
## 69 69 Operations Female 38 Specialist
## 70 70 Sales Male 52 Manager
## 71 71 IT Female 42 Developer
## 72 72 Engineering Female 42 Analyst
## 73 73 Sales Female 33 Developer
## 74 74 Legal Female 47 Developer
## 75 75 HR Female 33 Consultant
## 76 76 Engineering Male 47 Consultant
## 77 77 Customer Support Male 31 Technician
## 78 78 Sales Male 28 Developer
## 79 79 Legal Male 46 Consultant
## 80 80 Customer Support Female 54 Specialist
## 81 81 Legal Female 55 Developer
## 82 82 Customer Support Female 23 Specialist
## 83 83 Engineering Male 46 Analyst
## 84 84 HR Female 28 Analyst
## 85 85 Marketing Female 46 Specialist
## 86 86 Legal Female 59 Engineer
## 87 87 Marketing Male 55 Technician
## 88 88 Marketing Male 52 Technician
## 89 89 Finance Female 27 Engineer
## 90 90 Customer Support Female 39 Manager
## 91 91 Customer Support Male 55 Consultant
## 92 92 Legal Male 48 Developer
## 93 93 Finance Male 50 Manager
## 94 94 Engineering Male 52 Specialist
## 95 95 Operations Male 52 Developer
## 96 96 Marketing Female 30 Manager
## 97 97 Engineering Male 58 Specialist
## 98 98 Finance Female 38 Manager
## 99 99 Sales Male 24 Developer
## 100 100 Customer Support Other 33 Manager
## 101 101 Finance Female 45 Engineer
## 102 102 IT Female 33 Consultant
## 103 103 IT Male 33 Consultant
## 104 104 Engineering Male 58 Consultant
## 105 105 Customer Support Female 55 Technician
## 106 106 Customer Support Male 46 Manager
## 107 107 HR Female 22 Developer
## 108 108 Operations Female 37 Engineer
## 109 109 Sales Male 56 Consultant
## 110 110 Sales Male 36 Consultant
## 111 111 IT Male 43 Technician
## 112 112 Finance Male 39 Analyst
## 113 113 Legal Female 30 Technician
## 114 114 Customer Support Male 35 Consultant
## 115 115 Legal Female 48 Specialist
## 116 116 Customer Support Female 56 Analyst
## 117 117 Legal Male 26 Manager
## 118 118 IT Male 38 Consultant
## 119 119 IT Female 27 Manager
## 120 120 Finance Male 29 Specialist
## 121 121 Customer Support Male 26 Specialist
## 122 122 Legal Female 25 Technician
## 123 123 Operations Female 44 Manager
## 124 124 Finance Male 46 Consultant
## 125 125 IT Male 28 Engineer
## 126 126 Marketing Male 34 Developer
## 127 127 Engineering Other 59 Consultant
## 128 128 Sales Male 31 Consultant
## 129 129 Sales Male 51 Developer
## 130 130 Sales Other 29 Engineer
## 131 131 Engineering Male 47 Specialist
## 132 132 HR Male 32 Technician
## 133 133 Marketing Female 55 Manager
## 134 134 Operations Female 29 Consultant
## 135 135 Operations Male 24 Consultant
## 136 136 Sales Male 23 Analyst
## 137 137 Legal Female 35 Analyst
## 138 138 Customer Support Male 28 Manager
## 139 139 Finance Male 38 Technician
## 140 140 Legal Female 39 Specialist
## 141 141 Engineering Female 23 Analyst
## 142 142 Customer Support Male 22 Specialist
## 143 143 Legal Female 60 Specialist
## 144 144 Customer Support Female 54 Analyst
## 145 145 Operations Female 26 Consultant
## 146 146 Customer Support Male 43 Consultant
## 147 147 Legal Female 57 Engineer
## 148 148 Sales Male 58 Analyst
## 149 149 IT Female 31 Technician
## 150 150 Finance Female 55 Analyst
## 151 151 Operations Male 59 Consultant
## 152 152 Sales Male 25 Manager
## 153 153 Marketing Female 54 Technician
## 154 154 Customer Support Male 60 Specialist
## 155 155 Legal Male 51 Engineer
## 156 156 Customer Support Female 35 Specialist
## 157 157 Legal Male 46 Manager
## 158 158 Operations Female 39 Analyst
## 159 159 Legal Female 41 Technician
## 160 160 Operations Female 30 Manager
## 161 161 Engineering Male 36 Manager
## 162 162 HR Male 32 Technician
## 163 163 Finance Male 44 Technician
## 164 164 Legal Male 37 Analyst
## 165 165 Engineering Male 26 Analyst
## 166 166 Sales Female 30 Specialist
## 167 167 Sales Male 36 Manager
## 168 168 Legal Male 31 Analyst
## 169 169 Legal Female 39 Specialist
## 170 170 Legal Female 34 Technician
## 171 171 Engineering Male 23 Manager
## 172 172 Finance Male 22 Developer
## 173 173 Operations Male 43 Developer
## 174 174 Marketing Female 26 Specialist
## 175 175 Customer Support Female 30 Analyst
## 176 176 Engineering Female 48 Developer
## 177 177 HR Female 54 Consultant
## 178 178 Customer Support Male 25 Analyst
## 179 179 Customer Support Other 48 Technician
## 180 180 Legal Female 53 Manager
## 181 181 Legal Male 28 Technician
## 182 182 Legal Male 57 Manager
## 183 183 Sales Male 48 Technician
## 184 184 Customer Support Female 32 Manager
## 185 185 Finance Female 33 Technician
## 186 186 HR Female 57 Manager
## 187 187 Legal Male 38 Engineer
## 188 188 IT Female 43 Specialist
## 189 189 Customer Support Female 26 Specialist
## 190 190 Operations Female 41 Consultant
## 191 191 HR Female 37 Analyst
## 192 192 Engineering Female 36 Analyst
## 193 193 Finance Male 54 Specialist
## 194 194 IT Female 45 Analyst
## 195 195 IT Female 56 Specialist
## 196 196 Legal Female 39 Engineer
## 197 197 HR Male 44 Technician
## 198 198 HR Female 55 Analyst
## 199 199 IT Female 52 Developer
## 200 200 Operations Male 27 Consultant
## 201 201 Finance Male 45 Analyst
## 202 202 Operations Female 54 Specialist
## 203 203 Engineering Male 32 Consultant
## 204 204 Customer Support Male 52 Analyst
## 205 205 HR Female 38 Technician
## 206 206 Marketing Male 50 Engineer
## 207 207 Customer Support Male 41 Manager
## 208 208 Sales Female 40 Developer
## 209 209 Engineering Male 43 Developer
## 210 210 Legal Male 42 Consultant
## 211 211 Finance Female 26 Specialist
## 212 212 Marketing Male 36 Manager
## 213 213 Operations Male 28 Developer
## 214 214 Legal Female 32 Specialist
## 215 215 Operations Female 25 Developer
## 216 216 Sales Female 30 Manager
## 217 217 Operations Female 42 Engineer
## 218 218 Legal Female 22 Analyst
## 219 219 Engineering Male 23 Engineer
## 220 220 Customer Support Male 52 Engineer
## 221 221 Operations Male 25 Consultant
## 222 222 Customer Support Male 50 Developer
## 223 223 Legal Male 32 Analyst
## 224 224 Engineering Female 55 Engineer
## 225 225 Sales Female 53 Specialist
## 226 226 Sales Female 44 Manager
## 227 227 HR Female 25 Engineer
## 228 228 Operations Female 57 Developer
## 229 229 IT Male 42 Consultant
## 230 230 Legal Female 39 Engineer
## 231 231 Operations Male 25 Manager
## 232 232 Customer Support Female 27 Analyst
## 233 233 Sales Female 32 Technician
## 234 234 Finance Female 57 Analyst
## 235 235 Marketing Male 32 Analyst
## 236 236 HR Female 39 Technician
## 237 237 Marketing Other 44 Technician
## 238 238 Sales Male 28 Engineer
## 239 239 Operations Female 42 Consultant
## 240 240 Finance Female 27 Manager
## 241 241 HR Male 60 Developer
## 242 242 Engineering Male 42 Manager
## 243 243 Finance Male 33 Developer
## 244 244 IT Female 47 Analyst
## 245 245 HR Female 45 Manager
## 246 246 IT Female 32 Specialist
## 247 247 Marketing Female 24 Technician
## 248 248 HR Female 43 Analyst
## 249 249 Legal Male 32 Consultant
## 250 250 Customer Support Male 35 Technician
## 251 251 Engineering Male 56 Technician
## 252 252 IT Male 35 Consultant
## 253 253 Operations Male 34 Specialist
## 254 254 Legal Female 30 Consultant
## 255 255 Marketing Other 46 Developer
## 256 256 Operations Male 34 Consultant
## 257 257 Engineering Female 45 Specialist
## 258 258 HR Female 31 Specialist
## 259 259 Sales Male 26 Technician
## 260 260 Marketing Male 40 Manager
## 261 261 Marketing Male 46 Developer
## 262 262 Operations Female 32 Technician
## 263 263 HR Female 26 Developer
## 264 264 IT Male 52 Manager
## 265 265 Marketing Other 26 Engineer
## 266 266 IT Female 34 Technician
## 267 267 Finance Male 31 Developer
## 268 268 IT Female 54 Manager
## 269 269 Operations Male 39 Technician
## 270 270 IT Female 52 Manager
## 271 271 Sales Male 39 Specialist
## 272 272 Operations Male 31 Consultant
## 273 273 Marketing Female 44 Manager
## 274 274 Operations Other 25 Engineer
## 275 275 IT Male 26 Technician
## 276 276 Marketing Male 56 Consultant
## 277 277 Engineering Male 27 Engineer
## 278 278 Legal Female 59 Consultant
## 279 279 Operations Male 34 Developer
## 280 280 Customer Support Male 54 Consultant
## 281 281 Sales Female 57 Analyst
## 282 282 Engineering Female 43 Consultant
## 283 283 Customer Support Male 36 Engineer
## 284 284 IT Female 41 Technician
## 285 285 Marketing Female 38 Manager
## 286 286 HR Female 40 Manager
## 287 287 HR Female 22 Consultant
## 288 288 Finance Male 34 Analyst
## 289 289 Operations Female 58 Manager
## 290 290 Legal Male 42 Analyst
## 291 291 Engineering Male 40 Consultant
## 292 292 Sales Male 56 Analyst
## 293 293 Sales Male 51 Developer
## 294 294 Operations Female 22 Consultant
## 295 295 Sales Male 48 Specialist
## 296 296 HR Female 40 Consultant
## 297 297 Operations Female 36 Consultant
## 298 298 Customer Support Female 30 Analyst
## 299 299 Engineering Other 22 Engineer
## 300 300 Finance Female 53 Technician
## 301 301 Marketing Male 34 Developer
## 302 302 Customer Support Female 23 Developer
## 303 303 Sales Female 57 Analyst
## 304 304 Operations Male 24 Consultant
## 305 305 Engineering Male 46 Analyst
## 306 306 Legal Female 51 Developer
## 307 307 Engineering Female 46 Technician
## 308 308 Sales Female 58 Analyst
## 309 309 Marketing Female 24 Manager
## 310 310 Marketing Female 35 Consultant
## 311 311 Finance Female 46 Developer
## 312 312 Customer Support Male 34 Engineer
## 313 313 Customer Support Male 47 Consultant
## 314 314 Engineering Male 54 Analyst
## 315 315 Sales Female 43 Manager
## 316 316 Sales Male 42 Analyst
## 317 317 Operations Male 23 Consultant
## 318 318 Finance Female 27 Engineer
## 319 319 Sales Female 22 Engineer
## 320 320 Sales Female 45 Engineer
## 321 321 Finance Female 36 Specialist
## 322 322 Engineering Female 23 Consultant
## 323 323 IT Male 26 Manager
## 324 324 Marketing Female 34 Consultant
## 325 325 Finance Male 24 Analyst
## 326 326 HR Male 57 Developer
## 327 327 Finance Female 46 Consultant
## 328 328 Finance Female 22 Developer
## 329 329 Legal Other 53 Consultant
## 330 330 Operations Female 27 Specialist
## 331 331 Marketing Other 34 Consultant
## 332 332 HR Female 50 Technician
## 333 333 Operations Female 31 Engineer
## 334 334 Operations Male 38 Technician
## 335 335 Finance Female 33 Consultant
## 336 336 IT Female 28 Consultant
## 337 337 Customer Support Male 35 Engineer
## 338 338 Operations Female 55 Developer
## 339 339 Finance Male 60 Specialist
## 340 340 Legal Female 45 Specialist
## 341 341 Marketing Male 43 Engineer
## 342 342 Finance Male 31 Manager
## 343 343 Finance Female 29 Manager
## 344 344 Customer Support Female 27 Analyst
## 345 345 Operations Female 56 Developer
## 346 346 Operations Female 58 Analyst
## 347 347 HR Female 54 Analyst
## 348 348 HR Male 48 Consultant
## 349 349 Sales Male 31 Technician
## 350 350 Engineering Female 43 Manager
## 351 351 IT Female 26 Developer
## 352 352 HR Male 43 Engineer
## 353 353 Legal Female 25 Specialist
## 354 354 Legal Female 44 Analyst
## 355 355 Customer Support Male 46 Technician
## 356 356 HR Female 27 Developer
## 357 357 Operations Female 53 Specialist
## 358 358 Sales Female 32 Technician
## 359 359 Operations Male 36 Technician
## 360 360 Finance Male 52 Consultant
## 361 361 HR Male 50 Engineer
## 362 362 Sales Female 38 Specialist
## 363 363 IT Female 30 Specialist
## 364 364 Customer Support Female 37 Engineer
## 365 365 Legal Male 40 Technician
## 366 366 Marketing Male 45 Manager
## 367 367 Marketing Female 42 Technician
## 368 368 Marketing Male 39 Manager
## 369 369 Legal Female 28 Engineer
## 370 370 HR Other 30 Manager
## 371 371 Engineering Male 32 Consultant
## 372 372 Customer Support Male 50 Specialist
## 373 373 Customer Support Female 27 Specialist
## 374 374 IT Female 32 Manager
## 375 375 Operations Female 37 Manager
## 376 376 HR Female 43 Analyst
## 377 377 Finance Male 28 Manager
## 378 378 Engineering Female 33 Developer
## 379 379 Sales Female 53 Engineer
## 380 380 Customer Support Female 44 Specialist
## 381 381 Operations Female 26 Technician
## 382 382 Customer Support Male 42 Specialist
## 383 383 Marketing Male 36 Specialist
## 384 384 Engineering Female 34 Developer
## 385 385 Sales Female 52 Manager
## 386 386 Operations Male 31 Manager
## 387 387 Sales Male 26 Manager
## 388 388 Operations Female 43 Manager
## 389 389 Finance Female 60 Technician
## 390 390 IT Female 23 Developer
## 391 391 Engineering Male 33 Manager
## 392 392 Finance Male 25 Manager
## 393 393 Finance Male 35 Technician
## 394 394 Marketing Female 31 Engineer
## 395 395 IT Female 34 Engineer
## 396 396 Customer Support Female 39 Developer
## 397 397 Finance Male 35 Developer
## 398 398 Legal Male 41 Technician
## 399 399 Finance Male 25 Technician
## 400 400 HR Other 31 Technician
## 401 401 Marketing Female 54 Specialist
## 402 402 Finance Female 58 Engineer
## 403 403 HR Male 39 Consultant
## 404 404 IT Male 25 Specialist
## 405 405 Customer Support Female 27 Consultant
## 406 406 Sales Male 41 Engineer
## 407 407 HR Male 40 Analyst
## 408 408 IT Male 30 Developer
## 409 409 IT Male 52 Analyst
## 410 410 Engineering Female 38 Specialist
## 411 411 Engineering Male 50 Engineer
## 412 412 Marketing Male 29 Technician
## 413 413 Customer Support Male 22 Developer
## 414 414 Engineering Male 29 Analyst
## 415 415 Customer Support Other 33 Technician
## 416 416 IT Female 30 Developer
## 417 417 Operations Male 42 Manager
## 418 418 IT Male 42 Specialist
## 419 419 Customer Support Female 42 Engineer
## 420 420 Marketing Male 38 Engineer
## 421 421 Legal Female 37 Technician
## 422 422 Marketing Male 52 Technician
## 423 423 Marketing Male 51 Engineer
## 424 424 Finance Female 31 Manager
## 425 425 IT Female 30 Developer
## 426 426 Sales Female 45 Engineer
## 427 427 Finance Female 22 Developer
## 428 428 Customer Support Female 48 Engineer
## 429 429 HR Male 25 Engineer
## 430 430 IT Male 50 Manager
## 431 431 Engineering Female 57 Developer
## 432 432 Engineering Female 23 Developer
## 433 433 Customer Support Female 40 Consultant
## 434 434 Marketing Female 50 Technician
## 435 435 Operations Male 45 Specialist
## 436 436 Sales Male 45 Manager
## 437 437 Legal Male 51 Consultant
## 438 438 Operations Female 23 Manager
## 439 439 Marketing Male 35 Analyst
## 440 440 Legal Male 48 Engineer
## 441 441 HR Male 39 Consultant
## 442 442 Engineering Male 49 Analyst
## 443 443 Customer Support Male 28 Engineer
## 444 444 HR Male 30 Analyst
## 445 445 HR Male 39 Manager
## 446 446 Sales Other 56 Specialist
## 447 447 Marketing Female 23 Consultant
## 448 448 Sales Female 30 Developer
## 449 449 Marketing Female 47 Consultant
## 450 450 IT Male 53 Technician
## 451 451 Customer Support Female 49 Engineer
## 452 452 Customer Support Female 47 Specialist
## 453 453 Customer Support Male 36 Specialist
## 454 454 Operations Female 31 Developer
## 455 455 IT Male 33 Developer
## 456 456 Operations Male 23 Manager
## 457 457 Marketing Female 35 Technician
## 458 458 Engineering Female 52 Consultant
## 459 459 Sales Male 39 Consultant
## 460 460 Engineering Female 59 Engineer
## 461 461 Operations Male 28 Analyst
## 462 462 Sales Male 60 Developer
## 463 463 IT Male 26 Manager
## 464 464 Operations Female 37 Consultant
## 465 465 IT Male 40 Specialist
## 466 466 HR Male 47 Manager
## 467 467 Sales Male 50 Specialist
## 468 468 Marketing Male 56 Consultant
## 469 469 Finance Male 39 Consultant
## 470 470 Operations Male 36 Analyst
## 471 471 IT Female 47 Developer
## 472 472 Customer Support Female 47 Specialist
## 473 473 IT Female 27 Engineer
## 474 474 Marketing Male 32 Consultant
## 475 475 Engineering Female 29 Consultant
## 476 476 Sales Female 39 Technician
## 477 477 Engineering Male 26 Engineer
## 478 478 Legal Male 39 Specialist
## 479 479 HR Male 32 Analyst
## 480 480 IT Male 37 Consultant
## 481 481 IT Male 57 Engineer
## 482 482 Engineering Female 52 Specialist
## 483 483 Operations Male 55 Manager
## 484 484 Operations Female 58 Consultant
## 485 485 Operations Male 37 Specialist
## 486 486 HR Male 59 Consultant
## 487 487 Customer Support Male 43 Technician
## 488 488 Engineering Male 41 Consultant
## 489 489 IT Female 48 Specialist
## 490 490 Finance Female 42 Analyst
## 491 491 Legal Female 51 Consultant
## 492 492 Customer Support Male 36 Manager
## 493 493 Legal Male 22 Engineer
## 494 494 Marketing Other 48 Specialist
## 495 495 Sales Male 41 Specialist
## 496 496 Finance Female 50 Developer
## 497 497 Engineering Male 27 Consultant
## 498 498 Engineering Male 35 Analyst
## 499 499 Operations Female 27 Specialist
## 500 500 Marketing Male 27 Analyst
## 501 501 IT Female 31 Specialist
## 502 502 Customer Support Male 57 Technician
## 503 503 Finance Male 40 Consultant
## 504 504 Operations Female 36 Specialist
## 505 505 Finance Female 58 Analyst
## 506 506 HR Other 56 Technician
## 507 507 Customer Support Other 36 Manager
## 508 508 Engineering Male 49 Manager
## 509 509 Operations Male 35 Consultant
## 510 510 Legal Male 57 Specialist
## 511 511 HR Female 50 Consultant
## 512 512 IT Female 46 Specialist
## 513 513 Legal Male 35 Engineer
## 514 514 Finance Male 36 Consultant
## 515 515 IT Male 50 Consultant
## 516 516 Engineering Female 46 Consultant
## 517 517 Legal Male 55 Manager
## 518 518 Engineering Female 27 Developer
## 519 519 Finance Male 39 Developer
## 520 520 Finance Female 43 Developer
## 521 521 Sales Male 24 Consultant
## 522 522 Customer Support Female 52 Analyst
## 523 523 Engineering Female 49 Consultant
## 524 524 Legal Female 27 Specialist
## 525 525 Marketing Male 59 Consultant
## 526 526 Operations Male 31 Technician
## 527 527 Operations Male 52 Manager
## 528 528 HR Female 28 Engineer
## 529 529 HR Male 32 Developer
## 530 530 HR Male 27 Developer
## 531 531 Marketing Male 37 Engineer
## 532 532 Customer Support Female 30 Engineer
## 533 533 Legal Male 34 Manager
## 534 534 Customer Support Female 45 Developer
## 535 535 HR Male 29 Consultant
## 536 536 Finance Female 45 Specialist
## 537 537 Marketing Female 46 Technician
## 538 538 IT Male 35 Manager
## 539 539 HR Female 46 Technician
## 540 540 Sales Female 55 Technician
## 541 541 Legal Female 52 Engineer
## 542 542 Marketing Female 35 Specialist
## 543 543 Marketing Other 38 Specialist
## 544 544 Legal Female 35 Developer
## 545 545 Sales Male 39 Developer
## 546 546 Operations Female 33 Manager
## 547 547 Sales Male 52 Specialist
## 548 548 Sales Other 58 Analyst
## 549 549 Finance Female 48 Specialist
## 550 550 Operations Male 31 Engineer
## 551 551 Engineering Female 31 Specialist
## 552 552 Customer Support Female 45 Analyst
## 553 553 Engineering Female 24 Developer
## 554 554 Customer Support Male 60 Engineer
## 555 555 Sales Male 24 Technician
## 556 556 Finance Male 60 Analyst
## 557 557 Customer Support Female 51 Specialist
## 558 558 Engineering Female 48 Specialist
## 559 559 Legal Female 54 Technician
## 560 560 Finance Female 29 Developer
## 561 561 Customer Support Female 46 Manager
## 562 562 Marketing Female 56 Specialist
## 563 563 Engineering Male 56 Analyst
## 564 564 Sales Female 57 Technician
## 565 565 Sales Female 35 Analyst
## 566 566 Operations Male 33 Technician
## 567 567 Customer Support Female 26 Technician
## 568 568 Operations Female 28 Engineer
## 569 569 Marketing Female 55 Specialist
## 570 570 Legal Male 46 Developer
## 571 571 HR Male 35 Developer
## 572 572 Operations Female 32 Manager
## 573 573 HR Female 26 Analyst
## 574 574 Finance Female 35 Analyst
## 575 575 Engineering Female 41 Manager
## 576 576 Marketing Male 50 Analyst
## 577 577 Marketing Male 25 Technician
## 578 578 IT Male 40 Specialist
## 579 579 Customer Support Female 37 Developer
## 580 580 IT Female 52 Specialist
## 581 581 Engineering Male 54 Analyst
## 582 582 Finance Male 54 Specialist
## 583 583 HR Male 34 Manager
## 584 584 Legal Male 39 Developer
## 585 585 Sales Female 59 Specialist
## 586 586 Engineering Female 53 Engineer
## 587 587 IT Male 49 Developer
## 588 588 Marketing Male 59 Analyst
## 589 589 Engineering Female 36 Consultant
## 590 590 Marketing Male 43 Analyst
## 591 591 Engineering Male 27 Consultant
## 592 592 Finance Male 54 Consultant
## 593 593 Marketing Female 29 Developer
## 594 594 HR Male 33 Analyst
## 595 595 HR Male 35 Developer
## 596 596 Finance Female 43 Manager
## 597 597 Sales Male 40 Technician
## 598 598 Operations Female 27 Technician
## 599 599 IT Female 39 Developer
## 600 600 Operations Male 34 Technician
## 601 601 HR Other 27 Developer
## 602 602 IT Male 27 Developer
## 603 603 Customer Support Female 46 Analyst
## 604 604 Sales Male 28 Consultant
## 605 605 Marketing Male 41 Engineer
## 606 606 IT Female 35 Engineer
## 607 607 Engineering Female 43 Manager
## 608 608 Sales Male 42 Technician
## 609 609 Sales Female 43 Technician
## 610 610 Sales Male 30 Technician
## 611 611 Marketing Female 42 Manager
## 612 612 HR Male 42 Technician
## 613 613 Operations Male 56 Developer
## 614 614 Sales Female 41 Analyst
## 615 615 Operations Male 24 Specialist
## 616 616 Operations Male 26 Technician
## 617 617 Sales Female 36 Engineer
## 618 618 Operations Male 56 Consultant
## 619 619 IT Female 40 Consultant
## 620 620 Operations Female 56 Technician
## 621 621 HR Male 40 Specialist
## 622 622 Engineering Female 35 Consultant
## 623 623 Legal Male 33 Developer
## 624 624 Engineering Male 42 Technician
## 625 625 Legal Female 51 Engineer
## 626 626 Legal Male 49 Consultant
## 627 627 IT Female 26 Manager
## 628 628 IT Female 33 Engineer
## 629 629 Finance Female 35 Manager
## 630 630 Sales Female 35 Engineer
## 631 631 HR Female 48 Analyst
## 632 632 Finance Female 35 Manager
## 633 633 IT Female 35 Technician
## 634 634 HR Male 51 Consultant
## 635 635 Sales Male 33 Specialist
## 636 636 IT Female 37 Developer
## 637 637 Finance Other 47 Analyst
## 638 638 Sales Male 42 Specialist
## 639 639 Sales Female 29 Consultant
## 640 640 Sales Male 53 Analyst
## 641 641 Engineering Male 43 Engineer
## 642 642 IT Male 35 Engineer
## 643 643 IT Male 37 Manager
## 644 644 Legal Male 30 Engineer
## 645 645 Legal Female 59 Manager
## 646 646 Finance Female 39 Technician
## 647 647 Customer Support Male 22 Technician
## 648 648 Customer Support Male 30 Consultant
## 649 649 Marketing Female 31 Developer
## 650 650 Legal Male 44 Developer
## 651 651 Legal Female 29 Developer
## 652 652 Operations Male 33 Specialist
## 653 653 Marketing Male 39 Specialist
## 654 654 Operations Male 39 Analyst
## 655 655 HR Male 27 Analyst
## 656 656 Customer Support Female 59 Analyst
## 657 657 HR Female 39 Technician
## 658 658 Finance Male 35 Analyst
## 659 659 Operations Female 33 Manager
## 660 660 Customer Support Female 55 Engineer
## 661 661 Customer Support Male 43 Consultant
## 662 662 Sales Male 46 Analyst
## 663 663 Engineering Female 47 Analyst
## 664 664 Legal Male 47 Engineer
## 665 665 Customer Support Female 37 Technician
## 666 666 IT Female 37 Technician
## 667 667 Sales Male 59 Analyst
## 668 668 Finance Male 45 Technician
## 669 669 Sales Female 43 Consultant
## 670 670 Legal Male 47 Developer
## 671 671 Customer Support Male 46 Specialist
## 672 672 Legal Female 56 Consultant
## 673 673 Finance Female 53 Analyst
## 674 674 Legal Female 50 Consultant
## 675 675 IT Female 53 Developer
## 676 676 Finance Male 52 Manager
## 677 677 IT Male 46 Manager
## 678 678 Legal Female 25 Consultant
## 679 679 Marketing Other 60 Consultant
## 680 680 Engineering Male 44 Analyst
## 681 681 Engineering Male 39 Consultant
## 682 682 Operations Male 51 Technician
## 683 683 Operations Male 46 Technician
## 684 684 Engineering Female 45 Developer
## 685 685 Sales Female 37 Technician
## 686 686 Finance Male 51 Engineer
## 687 687 HR Female 52 Technician
## 688 688 IT Male 32 Consultant
## 689 689 Legal Male 58 Manager
## 690 690 Sales Male 48 Technician
## 691 691 Finance Male 54 Engineer
## 692 692 Marketing Male 23 Engineer
## 693 693 Sales Male 50 Developer
## 694 694 Engineering Male 27 Analyst
## 695 695 Sales Male 24 Manager
## 696 696 Finance Male 41 Manager
## 697 697 Engineering Male 33 Consultant
## 698 698 Legal Male 57 Manager
## 699 699 Customer Support Female 42 Technician
## 700 700 Legal Male 57 Manager
## 701 701 Sales Female 44 Analyst
## 702 702 IT Male 26 Developer
## 703 703 Marketing Male 51 Specialist
## 704 704 Sales Male 38 Consultant
## 705 705 Marketing Male 26 Developer
## 706 706 Engineering Male 22 Developer
## 707 707 Customer Support Male 40 Analyst
## 708 708 Customer Support Male 55 Engineer
## 709 709 Engineering Female 60 Consultant
## 710 710 Finance Female 40 Specialist
## 711 711 Finance Female 46 Manager
## 712 712 Finance Female 57 Manager
## 713 713 Sales Male 39 Specialist
## 714 714 Sales Male 44 Consultant
## 715 715 Operations Female 33 Developer
## 716 716 Operations Male 49 Consultant
## 717 717 Marketing Female 42 Consultant
## 718 718 Engineering Other 48 Engineer
## 719 719 Legal Male 35 Developer
## 720 720 Legal Male 27 Manager
## 721 721 Finance Male 46 Engineer
## 722 722 Engineering Male 37 Engineer
## 723 723 Operations Male 52 Technician
## 724 724 IT Male 29 Consultant
## 725 725 Finance Female 60 Consultant
## 726 726 Marketing Female 60 Engineer
## 727 727 Marketing Male 42 Analyst
## 728 728 Customer Support Female 43 Analyst
## 729 729 Operations Female 24 Technician
## 730 730 HR Female 26 Technician
## 731 731 Customer Support Male 29 Specialist
## 732 732 Operations Male 52 Specialist
## 733 733 Legal Other 51 Technician
## 734 734 IT Male 47 Developer
## 735 735 HR Male 42 Manager
## 736 736 Marketing Female 42 Specialist
## 737 737 Engineering Female 33 Developer
## 738 738 IT Female 32 Specialist
## 739 739 Sales Female 45 Analyst
## 740 740 Marketing Male 25 Manager
## 741 741 Sales Female 54 Engineer
## 742 742 Customer Support Male 55 Engineer
## 743 743 Customer Support Male 31 Engineer
## 744 744 IT Female 51 Analyst
## 745 745 Operations Female 30 Manager
## 746 746 Legal Male 39 Technician
## 747 747 HR Female 60 Manager
## 748 748 Finance Male 53 Manager
## 749 749 HR Male 48 Technician
## 750 750 Customer Support Female 37 Consultant
## 751 751 Engineering Female 39 Engineer
## 752 752 IT Female 45 Manager
## 753 753 IT Male 60 Engineer
## 754 754 HR Female 59 Specialist
## 755 755 Finance Female 23 Consultant
## 756 756 HR Male 59 Manager
## 757 757 HR Female 49 Analyst
## 758 758 HR Male 58 Engineer
## 759 759 Customer Support Male 27 Consultant
## 760 760 Legal Male 42 Specialist
## 761 761 Finance Female 44 Engineer
## 762 762 Customer Support Male 50 Consultant
## 763 763 Engineering Female 57 Specialist
## 764 764 Customer Support Male 44 Technician
## 765 765 Operations Male 29 Specialist
## 766 766 IT Female 33 Specialist
## 767 767 HR Male 34 Analyst
## 768 768 Marketing Female 26 Technician
## 769 769 Finance Female 43 Consultant
## 770 770 Engineering Female 60 Analyst
## 771 771 Customer Support Male 45 Technician
## 772 772 Operations Male 44 Specialist
## 773 773 Customer Support Other 39 Engineer
## 774 774 Finance Male 49 Technician
## 775 775 Marketing Female 52 Developer
## 776 776 Customer Support Female 27 Technician
## 777 777 Engineering Female 42 Specialist
## 778 778 IT Female 30 Developer
## 779 779 Engineering Female 55 Specialist
## 780 780 HR Male 29 Developer
## 781 781 Engineering Other 28 Specialist
## 782 782 Engineering Female 43 Engineer
## 783 783 HR Female 29 Developer
## 784 784 Sales Female 33 Analyst
## 785 785 Engineering Female 48 Specialist
## 786 786 Legal Female 56 Analyst
## 787 787 Legal Male 32 Developer
## 788 788 Legal Female 44 Specialist
## 789 789 Operations Female 39 Engineer
## 790 790 HR Female 51 Technician
## 791 791 Operations Male 48 Analyst
## 792 792 Sales Male 36 Specialist
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## 4324 4324 IT Other 40 Manager
## 4325 4325 Marketing Male 24 Engineer
## 4326 4326 Engineering Female 32 Manager
## 4327 4327 Sales Female 33 Developer
## 4328 4328 Sales Male 39 Developer
## 4329 4329 Marketing Female 49 Specialist
## 4330 4330 Engineering Male 25 Developer
## 4331 4331 Finance Female 37 Consultant
## 4332 4332 HR Male 30 Developer
## 4333 4333 IT Male 29 Manager
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## 4336 4336 Sales Female 48 Engineer
## 4337 4337 Finance Male 31 Consultant
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## 4339 4339 IT Female 57 Analyst
## 4340 4340 Finance Male 24 Developer
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## 4344 4344 Operations Female 32 Engineer
## 4345 4345 Marketing Male 27 Engineer
## 4346 4346 Engineering Female 32 Consultant
## 4347 4347 Customer Support Female 34 Consultant
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## 4354 4354 Marketing Male 52 Manager
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## 4358 4358 Marketing Male 52 Technician
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## 4364 4364 Legal Male 42 Specialist
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## 4366 4366 Engineering Female 38 Manager
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## 4403 4403 Operations Female 37 Manager
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## 4406 4406 Legal Female 44 Analyst
## 4407 4407 Engineering Female 52 Consultant
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## 4412 4412 HR Male 32 Technician
## 4413 4413 Operations Female 28 Consultant
## 4414 4414 Customer Support Female 58 Specialist
## 4415 4415 IT Male 51 Developer
## 4416 4416 Finance Male 30 Technician
## 4417 4417 HR Female 34 Analyst
## 4418 4418 Legal Other 39 Developer
## 4419 4419 HR Male 48 Developer
## 4420 4420 Marketing Female 33 Specialist
## 4421 4421 Operations Male 26 Technician
## 4422 4422 Customer Support Female 52 Engineer
## 4423 4423 Sales Male 27 Specialist
## 4424 4424 Operations Female 52 Developer
## 4425 4425 Marketing Male 52 Technician
## 4426 4426 Finance Male 46 Engineer
## 4427 4427 Engineering Female 32 Analyst
## 4428 4428 IT Male 59 Developer
## 4429 4429 Marketing Female 41 Developer
## 4430 4430 Finance Male 51 Consultant
## 4431 4431 Engineering Male 33 Analyst
## 4432 4432 Sales Female 24 Technician
## 4433 4433 IT Female 39 Consultant
## 4434 4434 Customer Support Female 32 Analyst
## 4435 4435 Customer Support Female 44 Manager
## 4436 4436 HR Female 42 Technician
## 4437 4437 Customer Support Female 44 Specialist
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## 4439 4439 Customer Support Female 50 Developer
## 4440 4440 Finance Male 59 Manager
## 4441 4441 Marketing Female 39 Manager
## 4442 4442 Customer Support Male 36 Specialist
## 4443 4443 Marketing Male 23 Technician
## 4444 4444 IT Female 40 Consultant
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## 4446 4446 Finance Male 38 Engineer
## 4447 4447 HR Male 36 Consultant
## 4448 4448 Engineering Male 32 Specialist
## 4449 4449 IT Male 55 Developer
## 4450 4450 Customer Support Male 49 Consultant
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## 4452 4452 Engineering Male 44 Engineer
## 4453 4453 Customer Support Female 35 Analyst
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## 4456 4456 HR Male 42 Developer
## 4457 4457 Sales Female 52 Consultant
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## 4459 4459 Finance Male 32 Technician
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## 4461 4461 Marketing Female 56 Analyst
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## 4464 4464 Customer Support Male 38 Engineer
## 4465 4465 Marketing Male 30 Specialist
## 4466 4466 Marketing Other 55 Manager
## 4467 4467 Legal Female 57 Analyst
## 4468 4468 Sales Female 25 Developer
## 4469 4469 Engineering Female 24 Analyst
## 4470 4470 Finance Male 43 Engineer
## 4471 4471 IT Female 53 Consultant
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## 4473 4473 Customer Support Male 48 Developer
## 4474 4474 IT Male 39 Analyst
## 4475 4475 Finance Male 33 Specialist
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## 4477 4477 Marketing Female 39 Analyst
## 4478 4478 HR Female 48 Consultant
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## 4480 4480 Sales Male 34 Specialist
## 4481 4481 Customer Support Female 42 Developer
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## 4484 4484 Customer Support Male 28 Technician
## 4485 4485 IT Male 48 Specialist
## 4486 4486 Legal Male 27 Consultant
## 4487 4487 Legal Female 28 Analyst
## 4488 4488 HR Female 40 Engineer
## 4489 4489 IT Male 50 Technician
## 4490 4490 Marketing Male 56 Consultant
## 4491 4491 Legal Male 48 Analyst
## 4492 4492 HR Male 22 Consultant
## 4493 4493 Customer Support Male 51 Manager
## 4494 4494 Customer Support Female 30 Manager
## 4495 4495 Customer Support Female 26 Developer
## 4496 4496 IT Female 45 Analyst
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## 4498 4498 Operations Male 22 Analyst
## 4499 4499 IT Female 41 Developer
## 4500 4500 Operations Male 24 Manager
## 4501 4501 Sales Male 27 Analyst
## 4502 4502 Engineering Female 46 Engineer
## 4503 4503 Marketing Male 25 Engineer
## 4504 4504 Operations Male 39 Consultant
## 4505 4505 Finance Male 53 Manager
## 4506 4506 Sales Female 24 Manager
## 4507 4507 Customer Support Male 22 Developer
## 4508 4508 Customer Support Female 29 Engineer
## 4509 4509 Legal Male 30 Analyst
## 4510 4510 Sales Female 27 Technician
## 4511 4511 IT Female 45 Engineer
## 4512 4512 Engineering Female 32 Analyst
## 4513 4513 Engineering Male 43 Specialist
## 4514 4514 Operations Male 45 Analyst
## 4515 4515 Customer Support Male 34 Manager
## 4516 4516 Engineering Female 29 Developer
## 4517 4517 Marketing Male 47 Engineer
## 4518 4518 Operations Female 58 Manager
## 4519 4519 Operations Male 26 Technician
## 4520 4520 Engineering Female 28 Manager
## 4521 4521 Customer Support Male 25 Specialist
## 4522 4522 Customer Support Female 39 Specialist
## 4523 4523 Engineering Male 43 Developer
## 4524 4524 Marketing Male 47 Specialist
## 4525 4525 Finance Female 33 Specialist
## 4526 4526 HR Female 58 Developer
## 4527 4527 Customer Support Male 52 Manager
## 4528 4528 Legal Female 49 Specialist
## 4529 4529 Operations Female 45 Analyst
## 4530 4530 Sales Other 26 Technician
## 4531 4531 IT Female 49 Engineer
## 4532 4532 IT Other 29 Manager
## 4533 4533 Operations Male 58 Specialist
## 4534 4534 IT Male 51 Developer
## 4535 4535 Customer Support Male 22 Analyst
## 4536 4536 Marketing Male 36 Consultant
## 4537 4537 HR Male 30 Analyst
## 4538 4538 Marketing Male 44 Consultant
## 4539 4539 Legal Female 30 Consultant
## 4540 4540 Marketing Male 26 Consultant
## 4541 4541 IT Female 42 Developer
## 4542 4542 Marketing Other 28 Engineer
## 4543 4543 Sales Female 38 Analyst
## 4544 4544 Finance Male 23 Technician
## 4545 4545 Marketing Female 48 Manager
## 4546 4546 Customer Support Male 46 Consultant
## 4547 4547 HR Male 60 Developer
## 4548 4548 Legal Female 47 Analyst
## 4549 4549 Sales Other 37 Technician
## 4550 4550 Sales Female 51 Specialist
## 4551 4551 IT Female 56 Consultant
## 4552 4552 HR Male 52 Developer
## 4553 4553 HR Male 48 Specialist
## 4554 4554 Operations Male 29 Analyst
## 4555 4555 Legal Male 38 Developer
## 4556 4556 Engineering Female 38 Specialist
## 4557 4557 Marketing Female 27 Specialist
## 4558 4558 Legal Male 26 Technician
## 4559 4559 Legal Female 43 Developer
## 4560 4560 Operations Female 35 Technician
## 4561 4561 Operations Female 25 Developer
## 4562 4562 Customer Support Female 52 Analyst
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## 4564 4564 Engineering Female 24 Consultant
## 4565 4565 Customer Support Female 55 Technician
## 4566 4566 Customer Support Male 48 Developer
## 4567 4567 Sales Male 38 Analyst
## 4568 4568 Marketing Female 43 Analyst
## 4569 4569 Engineering Female 24 Technician
## 4570 4570 Legal Male 46 Analyst
## 4571 4571 Legal Female 44 Consultant
## 4572 4572 HR Male 53 Technician
## 4573 4573 Operations Other 55 Analyst
## 4574 4574 Finance Male 57 Technician
## 4575 4575 Operations Male 37 Engineer
## 4576 4576 Marketing Male 60 Developer
## 4577 4577 Marketing Male 26 Developer
## 4578 4578 Legal Female 37 Manager
## 4579 4579 Operations Male 35 Analyst
## 4580 4580 Finance Male 42 Consultant
## 4581 4581 Sales Male 47 Developer
## 4582 4582 Sales Female 54 Engineer
## 4583 4583 Marketing Male 33 Engineer
## 4584 4584 IT Male 33 Consultant
## 4585 4585 Customer Support Male 41 Specialist
## 4586 4586 HR Female 41 Manager
## 4587 4587 Customer Support Male 37 Engineer
## 4588 4588 Engineering Female 38 Engineer
## 4589 4589 Finance Male 45 Engineer
## 4590 4590 Customer Support Female 59 Consultant
## 4591 4591 Legal Female 48 Consultant
## 4592 4592 Engineering Female 27 Analyst
## 4593 4593 Legal Female 41 Specialist
## 4594 4594 HR Female 44 Analyst
## 4595 4595 Legal Male 24 Consultant
## 4596 4596 Operations Female 27 Specialist
## 4597 4597 IT Male 29 Manager
## 4598 4598 Customer Support Female 58 Manager
## 4599 4599 Legal Female 53 Specialist
## 4600 4600 Marketing Female 32 Engineer
## 4601 4601 Customer Support Male 44 Specialist
## 4602 4602 Legal Male 52 Specialist
## 4603 4603 Finance Female 53 Manager
## 4604 4604 Legal Male 33 Technician
## 4605 4605 IT Female 52 Specialist
## 4606 4606 Engineering Female 43 Manager
## 4607 4607 HR Male 38 Developer
## 4608 4608 Marketing Female 34 Technician
## 4609 4609 Marketing Male 54 Specialist
## 4610 4610 Marketing Male 51 Consultant
## 4611 4611 Finance Female 46 Developer
## 4612 4612 Finance Male 56 Manager
## 4613 4613 Operations Female 22 Specialist
## 4614 4614 Finance Male 38 Analyst
## 4615 4615 Engineering Female 44 Technician
## 4616 4616 Customer Support Male 50 Analyst
## 4617 4617 Finance Male 35 Analyst
## 4618 4618 HR Female 51 Manager
## 4619 4619 Sales Male 24 Analyst
## 4620 4620 HR Male 54 Specialist
## 4621 4621 Legal Female 39 Engineer
## 4622 4622 Finance Female 57 Manager
## 4623 4623 Engineering Male 54 Specialist
## 4624 4624 Customer Support Female 25 Consultant
## 4625 4625 IT Male 31 Analyst
## 4626 4626 Finance Male 24 Engineer
## 4627 4627 Legal Female 27 Engineer
## 4628 4628 Marketing Female 25 Specialist
## 4629 4629 Marketing Female 26 Analyst
## 4630 4630 Customer Support Male 26 Analyst
## 4631 4631 Legal Male 51 Manager
## 4632 4632 HR Female 57 Manager
## 4633 4633 Finance Female 25 Technician
## 4634 4634 Customer Support Male 53 Manager
## 4635 4635 Sales Male 40 Technician
## 4636 4636 Engineering Male 35 Engineer
## 4637 4637 Sales Male 23 Specialist
## 4638 4638 Finance Male 50 Consultant
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## 4640 4640 HR Female 32 Analyst
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## 4642 4642 IT Female 52 Engineer
## 4643 4643 Finance Female 33 Analyst
## 4644 4644 Sales Female 25 Specialist
## 4645 4645 Engineering Female 25 Developer
## 4646 4646 Operations Female 23 Analyst
## 4647 4647 IT Female 22 Manager
## 4648 4648 Engineering Male 52 Technician
## 4649 4649 Customer Support Female 27 Manager
## 4650 4650 Operations Female 60 Analyst
## 4651 4651 Legal Female 58 Specialist
## 4652 4652 Legal Male 44 Analyst
## 4653 4653 Sales Male 49 Manager
## 4654 4654 Sales Male 25 Manager
## 4655 4655 Engineering Male 51 Analyst
## 4656 4656 HR Female 46 Developer
## 4657 4657 Engineering Female 44 Specialist
## 4658 4658 IT Male 58 Manager
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## 4660 4660 Marketing Male 45 Consultant
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## 4664 4664 Finance Male 35 Technician
## 4665 4665 IT Female 42 Engineer
## 4666 4666 IT Male 30 Technician
## 4667 4667 Engineering Male 47 Consultant
## 4668 4668 Customer Support Female 27 Engineer
## 4669 4669 Engineering Female 22 Engineer
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## 4683 4683 Customer Support Male 29 Manager
## 4684 4684 Customer Support Male 34 Engineer
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## 4698 4698 HR Female 48 Specialist
## 4699 4699 Customer Support Male 32 Analyst
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## 4701 4701 Engineering Male 35 Manager
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## 4703 4703 Customer Support Male 24 Analyst
## 4704 4704 Engineering Female 39 Engineer
## 4705 4705 IT Male 28 Technician
## 4706 4706 Sales Male 24 Analyst
## 4707 4707 IT Male 37 Consultant
## 4708 4708 Customer Support Male 50 Analyst
## 4709 4709 HR Female 38 Technician
## 4710 4710 Engineering Male 54 Developer
## 4711 4711 Sales Male 40 Manager
## 4712 4712 IT Female 48 Specialist
## 4713 4713 Marketing Female 49 Technician
## 4714 4714 IT Male 39 Engineer
## 4715 4715 Customer Support Male 56 Manager
## 4716 4716 Marketing Female 44 Technician
## 4717 4717 Operations Female 52 Engineer
## 4718 4718 Operations Female 40 Engineer
## 4719 4719 Marketing Male 22 Engineer
## 4720 4720 Customer Support Male 28 Specialist
## 4721 4721 Engineering Male 38 Specialist
## 4722 4722 Engineering Male 26 Specialist
## 4723 4723 HR Female 39 Analyst
## 4724 4724 Finance Male 33 Specialist
## 4725 4725 HR Female 55 Consultant
## 4726 4726 IT Male 33 Analyst
## 4727 4727 Operations Female 56 Analyst
## 4728 4728 Operations Female 50 Manager
## 4729 4729 IT Female 49 Analyst
## 4730 4730 Operations Female 59 Technician
## 4731 4731 Legal Male 41 Engineer
## 4732 4732 IT Female 37 Developer
## 4733 4733 IT Female 52 Engineer
## 4734 4734 Legal Male 40 Specialist
## 4735 4735 IT Female 42 Consultant
## 4736 4736 Finance Female 41 Developer
## 4737 4737 Engineering Male 37 Technician
## 4738 4738 Sales Male 37 Manager
## 4739 4739 Finance Female 35 Manager
## 4740 4740 IT Female 43 Analyst
## 4741 4741 Finance Male 55 Engineer
## 4742 4742 Marketing Male 52 Manager
## 4743 4743 IT Male 53 Consultant
## 4744 4744 Operations Male 22 Manager
## 4745 4745 Customer Support Female 60 Technician
## 4746 4746 Customer Support Female 45 Technician
## 4747 4747 Marketing Female 51 Engineer
## 4748 4748 Engineering Male 25 Engineer
## 4749 4749 Marketing Female 31 Analyst
## 4750 4750 Sales Female 23 Specialist
## 4751 4751 HR Male 54 Manager
## 4752 4752 HR Male 59 Developer
## 4753 4753 Finance Male 52 Specialist
## 4754 4754 Operations Male 28 Analyst
## 4755 4755 Marketing Male 40 Developer
## 4756 4756 Sales Female 58 Engineer
## 4757 4757 Operations Male 40 Developer
## 4758 4758 Sales Male 22 Technician
## 4759 4759 Engineering Male 42 Analyst
## 4760 4760 IT Male 44 Analyst
## 4761 4761 Marketing Female 24 Manager
## 4762 4762 HR Male 40 Developer
## 4763 4763 HR Male 30 Specialist
## 4764 4764 Customer Support Male 34 Developer
## 4765 4765 Finance Male 28 Specialist
## 4766 4766 Operations Male 22 Consultant
## 4767 4767 Customer Support Female 27 Technician
## 4768 4768 Engineering Male 42 Analyst
## 4769 4769 Finance Male 22 Consultant
## 4770 4770 Marketing Male 23 Specialist
## 4771 4771 Sales Female 48 Consultant
## 4772 4772 Customer Support Male 54 Technician
## 4773 4773 Legal Female 39 Consultant
## 4774 4774 Customer Support Female 27 Technician
## 4775 4775 Finance Female 39 Specialist
## 4776 4776 IT Female 35 Engineer
## 4777 4777 Customer Support Male 26 Analyst
## 4778 4778 Finance Other 27 Specialist
## 4779 4779 Marketing Female 42 Analyst
## 4780 4780 Operations Other 29 Developer
## 4781 4781 IT Male 29 Specialist
## 4782 4782 Sales Female 43 Developer
## 4783 4783 Legal Male 55 Engineer
## 4784 4784 Finance Female 58 Specialist
## 4785 4785 HR Female 23 Developer
## 4786 4786 Operations Female 30 Analyst
## 4787 4787 Marketing Male 34 Technician
## 4788 4788 Sales Male 60 Analyst
## 4789 4789 HR Female 24 Engineer
## 4790 4790 Engineering Male 38 Technician
## 4791 4791 Engineering Male 32 Manager
## 4792 4792 Operations Female 52 Consultant
## 4793 4793 Operations Male 31 Consultant
## 4794 4794 Legal Female 56 Developer
## 4795 4795 Finance Male 44 Manager
## 4796 4796 Legal Male 57 Developer
## 4797 4797 HR Male 49 Consultant
## 4798 4798 Operations Male 39 Engineer
## 4799 4799 Marketing Male 46 Developer
## 4800 4800 HR Female 43 Engineer
## 4801 4801 Customer Support Female 30 Specialist
## 4802 4802 Sales Male 31 Developer
## 4803 4803 Engineering Female 22 Analyst
## 4804 4804 Operations Male 40 Consultant
## 4805 4805 Marketing Male 58 Specialist
## 4806 4806 Customer Support Male 51 Manager
## 4807 4807 Legal Female 38 Analyst
## 4808 4808 HR Male 24 Specialist
## 4809 4809 Operations Male 25 Consultant
## 4810 4810 IT Female 60 Analyst
## 4811 4811 Marketing Male 23 Manager
## 4812 4812 Legal Female 51 Manager
## 4813 4813 Finance Male 46 Engineer
## 4814 4814 Customer Support Male 22 Manager
## 4815 4815 Legal Female 53 Analyst
## 4816 4816 Legal Female 38 Specialist
## 4817 4817 Customer Support Female 49 Manager
## 4818 4818 Operations Female 53 Technician
## 4819 4819 Engineering Female 44 Developer
## 4820 4820 Sales Male 47 Engineer
## 4821 4821 Marketing Female 30 Technician
## 4822 4822 Customer Support Female 55 Analyst
## 4823 4823 Legal Female 41 Consultant
## 4824 4824 Sales Female 34 Technician
## 4825 4825 Finance Female 57 Engineer
## 4826 4826 Finance Female 32 Specialist
## 4827 4827 Operations Male 24 Developer
## 4828 4828 HR Male 37 Consultant
## 4829 4829 Operations Male 55 Engineer
## 4830 4830 Customer Support Female 44 Analyst
## 4831 4831 HR Female 53 Engineer
## 4832 4832 HR Male 44 Consultant
## 4833 4833 Sales Male 27 Developer
## 4834 4834 Engineering Male 36 Manager
## 4835 4835 Legal Female 37 Developer
## 4836 4836 Marketing Male 47 Developer
## 4837 4837 Customer Support Female 59 Specialist
## 4838 4838 Customer Support Male 46 Consultant
## 4839 4839 Finance Male 44 Manager
## 4840 4840 Finance Female 37 Analyst
## 4841 4841 Marketing Female 56 Specialist
## 4842 4842 Legal Female 47 Engineer
## 4843 4843 Customer Support Male 24 Consultant
## 4844 4844 Operations Female 39 Manager
## 4845 4845 Legal Male 35 Developer
## 4846 4846 Customer Support Male 55 Specialist
## 4847 4847 Finance Male 31 Engineer
## 4848 4848 Marketing Female 27 Developer
## 4849 4849 Marketing Female 48 Developer
## 4850 4850 Sales Male 43 Analyst
## 4851 4851 Engineering Female 42 Analyst
## 4852 4852 Legal Female 56 Specialist
## 4853 4853 Finance Male 32 Manager
## 4854 4854 Operations Female 43 Specialist
## 4855 4855 Marketing Female 46 Technician
## 4856 4856 Finance Male 49 Technician
## 4857 4857 Customer Support Female 57 Technician
## 4858 4858 Legal Male 55 Manager
## 4859 4859 Finance Male 55 Manager
## 4860 4860 IT Male 33 Manager
## 4861 4861 Legal Male 42 Developer
## 4862 4862 Engineering Male 28 Specialist
## 4863 4863 Legal Male 41 Manager
## 4864 4864 Customer Support Female 29 Analyst
## 4865 4865 Legal Female 43 Engineer
## 4866 4866 Engineering Male 26 Manager
## 4867 4867 Legal Female 53 Specialist
## 4868 4868 IT Male 60 Consultant
## 4869 4869 Customer Support Female 57 Analyst
## 4870 4870 Engineering Female 26 Consultant
## 4871 4871 Finance Other 33 Manager
## 4872 4872 Legal Female 49 Technician
## 4873 4873 Engineering Female 43 Developer
## 4874 4874 Finance Male 31 Developer
## 4875 4875 Legal Male 38 Analyst
## 4876 4876 Engineering Female 46 Manager
## 4877 4877 Engineering Male 23 Engineer
## 4878 4878 HR Female 42 Developer
## 4879 4879 IT Male 41 Manager
## 4880 4880 Sales Female 53 Engineer
## 4881 4881 Finance Female 59 Consultant
## 4882 4882 Engineering Male 42 Manager
## 4883 4883 Legal Male 34 Engineer
## 4884 4884 Marketing Female 57 Analyst
## 4885 4885 Finance Male 47 Analyst
## 4886 4886 Operations Male 37 Technician
## 4887 4887 IT Male 33 Analyst
## 4888 4888 Legal Male 23 Manager
## 4889 4889 Marketing Female 39 Engineer
## 4890 4890 Customer Support Male 38 Analyst
## 4891 4891 Customer Support Female 33 Manager
## 4892 4892 Finance Male 59 Consultant
## 4893 4893 Engineering Male 50 Analyst
## 4894 4894 Operations Male 29 Manager
## 4895 4895 Sales Female 27 Developer
## 4896 4896 Finance Female 50 Specialist
## 4897 4897 Sales Male 53 Developer
## 4898 4898 Finance Female 42 Manager
## 4899 4899 IT Female 43 Engineer
## 4900 4900 Legal Male 49 Analyst
## 4901 4901 Marketing Male 57 Specialist
## 4902 4902 Sales Male 60 Specialist
## 4903 4903 Engineering Male 42 Manager
## 4904 4904 Customer Support Female 51 Specialist
## 4905 4905 Legal Male 42 Technician
## 4906 4906 Operations Female 57 Analyst
## 4907 4907 Marketing Male 31 Developer
## 4908 4908 Legal Male 31 Analyst
## 4909 4909 Finance Male 44 Engineer
## 4910 4910 HR Other 31 Consultant
## 4911 4911 Sales Female 24 Manager
## 4912 4912 Marketing Male 57 Consultant
## 4913 4913 Engineering Female 53 Manager
## 4914 4914 Engineering Male 55 Technician
## 4915 4915 Operations Female 44 Analyst
## 4916 4916 Marketing Male 29 Specialist
## 4917 4917 Sales Male 27 Analyst
## 4918 4918 Legal Male 38 Specialist
## 4919 4919 Legal Male 35 Manager
## 4920 4920 Finance Male 59 Specialist
## 4921 4921 HR Other 50 Consultant
## 4922 4922 IT Female 50 Engineer
## 4923 4923 Customer Support Male 42 Specialist
## 4924 4924 Legal Female 25 Specialist
## 4925 4925 HR Other 42 Developer
## 4926 4926 Finance Female 57 Manager
## 4927 4927 Engineering Male 42 Consultant
## 4928 4928 Engineering Female 42 Analyst
## 4929 4929 Engineering Female 41 Developer
## 4930 4930 Marketing Male 53 Technician
## 4931 4931 Marketing Female 39 Manager
## 4932 4932 HR Female 25 Developer
## 4933 4933 Sales Female 46 Technician
## 4934 4934 Legal Female 32 Consultant
## 4935 4935 Legal Female 28 Analyst
## 4936 4936 Engineering Female 50 Manager
## 4937 4937 Finance Female 59 Technician
## 4938 4938 Marketing Female 42 Consultant
## 4939 4939 Engineering Male 54 Consultant
## 4940 4940 Operations Female 28 Specialist
## 4941 4941 IT Female 60 Technician
## 4942 4942 Marketing Other 51 Specialist
## 4943 4943 Engineering Male 45 Analyst
## 4944 4944 Customer Support Female 42 Developer
## 4945 4945 Sales Male 23 Engineer
## 4946 4946 Legal Female 32 Analyst
## 4947 4947 Sales Female 60 Engineer
## 4948 4948 Finance Male 40 Consultant
## 4949 4949 Customer Support Female 49 Technician
## 4950 4950 Legal Other 34 Developer
## 4951 4951 Engineering Female 24 Developer
## 4952 4952 Legal Male 23 Analyst
## 4953 4953 Sales Male 57 Developer
## 4954 4954 HR Male 39 Engineer
## 4955 4955 Legal Male 46 Technician
## 4956 4956 Engineering Female 45 Specialist
## 4957 4957 Marketing Female 45 Specialist
## 4958 4958 Marketing Male 55 Manager
## 4959 4959 Finance Female 58 Engineer
## 4960 4960 Customer Support Female 22 Manager
## 4961 4961 Engineering Female 59 Analyst
## 4962 4962 Finance Female 58 Specialist
## 4963 4963 Finance Female 59 Manager
## 4964 4964 Finance Female 60 Specialist
## 4965 4965 Customer Support Female 38 Analyst
## 4966 4966 Operations Male 32 Analyst
## 4967 4967 Sales Male 26 Technician
## 4968 4968 HR Female 48 Technician
## 4969 4969 Operations Male 30 Engineer
## 4970 4970 IT Male 44 Analyst
## 4971 4971 Sales Male 56 Engineer
## 4972 4972 IT Male 39 Specialist
## 4973 4973 Sales Male 54 Technician
## 4974 4974 Operations Female 56 Consultant
## 4975 4975 HR Male 27 Consultant
## 4976 4976 Finance Male 24 Engineer
## 4977 4977 Engineering Female 51 Engineer
## 4978 4978 Marketing Female 34 Engineer
## 4979 4979 Finance Female 47 Technician
## 4980 4980 Legal Male 50 Analyst
## 4981 4981 Operations Male 51 Developer
## 4982 4982 Customer Support Other 42 Consultant
## 4983 4983 Engineering Female 34 Specialist
## 4984 4984 IT Female 34 Technician
## 4985 4985 Engineering Male 37 Manager
## 4986 4986 Sales Male 38 Technician
## 4987 4987 IT Male 52 Technician
## 4988 4988 Customer Support Male 56 Manager
## 4989 4989 Operations Female 42 Consultant
## 4990 4990 HR Female 58 Consultant
## 4991 4991 IT Female 51 Consultant
## 4992 4992 Finance Male 22 Consultant
## 4993 4993 Customer Support Female 40 Manager
## 4994 4994 Marketing Female 46 Consultant
## 4995 4995 Sales Male 53 Manager
## 4996 4996 Finance Male 24 Specialist
## 4997 4997 Sales Male 60 Specialist
## 4998 4998 HR Male 30 Manager
## 4999 4999 Sales Female 37 Manager
## Hire_Date Years_At_Company Education_Level
## 1 2022-01-19 08:03:05.556036 2 High School
## 2 2024-04-18 08:03:05.556036 0 High School
## 3 2015-10-26 08:03:05.556036 8 High School
## 4 2016-10-22 08:03:05.556036 7 Bachelor
## 5 2021-07-23 08:03:05.556036 3 Bachelor
## 6 2016-08-14 08:03:05.556036 8 High School
## 7 2023-08-28 08:03:05.556036 1 Bachelor
## 8 2014-10-27 08:03:05.556036 9 Bachelor
## 9 2023-06-29 08:03:05.556036 1 High School
## 10 2016-12-23 08:03:05.556036 7 Bachelor
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## 4953 2016-03-17 08:03:05.556036 8 Bachelor
## 4954 2021-09-22 08:03:05.556036 2 Master
## 4955 2018-12-04 08:03:05.556036 5 High School
## 4956 2014-12-16 08:03:05.556036 9 Bachelor
## 4957 2020-01-29 08:03:05.556036 4 High School
## 4958 2014-10-22 08:03:05.556036 9 Bachelor
## 4959 2015-08-05 08:03:05.556036 9 High School
## 4960 2017-07-19 08:03:05.556036 7 Bachelor
## 4961 2020-05-02 08:03:05.556036 4 High School
## 4962 2022-09-21 08:03:05.556036 1 High School
## 4963 2015-05-04 08:03:05.556036 9 Bachelor
## 4964 2018-10-07 08:03:05.556036 5 Master
## 4965 2020-09-09 08:03:05.556036 3 Master
## 4966 2019-07-05 08:03:05.556036 5 Master
## 4967 2015-03-14 08:03:05.556036 9 Master
## 4968 2017-03-12 08:03:05.556036 7 Bachelor
## 4969 2023-03-07 08:03:05.556036 1 High School
## 4970 2020-08-28 08:03:05.556036 4 Master
## 4971 2024-05-04 08:03:05.556036 0 Bachelor
## 4972 2022-10-02 08:03:05.556036 1 Bachelor
## 4973 2022-01-23 08:03:05.556036 2 High School
## 4974 2022-10-28 08:03:05.556036 1 High School
## 4975 2024-05-12 08:03:05.556036 0 High School
## 4976 2015-10-08 08:03:05.556036 8 High School
## 4977 2024-01-15 08:03:05.556036 0 Bachelor
## 4978 2020-04-10 08:03:05.556036 4 Bachelor
## 4979 2021-04-17 08:03:05.556036 3 High School
## 4980 2020-09-21 08:03:05.556036 3 High School
## 4981 2019-02-11 08:03:05.556036 5 Bachelor
## 4982 2021-02-09 08:03:05.556036 3 High School
## 4983 2021-04-16 08:03:05.556036 3 High School
## 4984 2019-05-18 08:03:05.556036 5 High School
## 4985 2019-10-30 08:03:05.556036 4 Bachelor
## 4986 2018-12-09 08:03:05.556036 5 Bachelor
## 4987 2019-02-10 08:03:05.556036 5 Bachelor
## 4988 2018-11-06 08:03:05.556036 5 High School
## 4989 2017-06-23 08:03:05.556036 7 High School
## 4990 2017-05-19 08:03:05.556036 7 Bachelor
## 4991 2018-02-04 08:03:05.556036 6 Bachelor
## 4992 2017-04-06 08:03:05.556036 7 High School
## 4993 2017-05-10 08:03:05.556036 7 Bachelor
## 4994 2018-06-09 08:03:05.556036 6 High School
## 4995 2024-07-08 08:03:05.556036 0 Bachelor
## 4996 2018-10-21 08:03:05.556036 5 Bachelor
## 4997 2023-12-06 08:03:05.556036 0 Bachelor
## 4998 2020-03-14 08:03:05.556036 4 Bachelor
## 4999 2016-10-19 08:03:05.556036 7 Bachelor
## Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled
## 1 5 6750 33 32
## 2 5 7500 34 34
## 3 3 5850 37 27
## 4 2 4800 52 10
## 5 2 4800 38 11
## 6 3 7800 46 31
## 7 5 5250 55 20
## 8 2 7200 42 46
## 9 2 4200 51 23
## 10 1 6050 41 33
## 11 3 7800 38 1
## 12 5 5250 39 13
## 13 4 4900 31 11
## 14 4 5600 33 30
## 15 1 4400 33 49
## 16 2 6000 33 41
## 17 2 7200 43 26
## 18 3 6500 47 36
## 19 1 5500 57 9
## 20 2 4200 52 0
## 21 1 6600 33 28
## 22 5 9000 30 22
## 23 4 6300 48 40
## 24 2 6600 55 2
## 25 2 4800 50 21
## 26 5 6750 59 24
## 27 2 7200 59 24
## 28 3 7800 44 42
## 29 2 5400 31 34
## 30 3 5200 45 25
## 31 1 6050 38 15
## 32 4 8400 39 25
## 33 2 4800 48 49
## 34 4 8400 44 26
## 35 4 5600 34 13
## 36 5 9000 31 9
## 37 2 6600 53 42
## 38 2 6600 31 6
## 39 1 4950 46 7
## 40 5 9000 44 2
## 41 2 4800 59 45
## 42 4 6300 33 10
## 43 3 7800 46 48
## 44 3 7800 43 25
## 45 2 6000 58 7
## 46 2 5400 31 19
## 47 4 4900 41 19
## 48 1 4950 45 31
## 49 5 6750 59 49
## 50 2 6000 53 10
## 51 1 6050 55 26
## 52 2 6000 46 9
## 53 3 7150 38 34
## 54 2 4200 50 22
## 55 5 5250 32 26
## 56 3 6500 41 18
## 57 1 4950 33 46
## 58 4 7000 32 42
## 59 3 4550 53 7
## 60 1 6600 51 17
## 61 2 6600 40 17
## 62 5 6750 43 44
## 63 3 7800 44 29
## 64 4 8400 50 10
## 65 1 4950 41 37
## 66 4 4900 54 32
## 67 2 7200 55 30
## 68 2 6600 53 10
## 69 4 6300 52 6
## 70 1 6600 32 2
## 71 1 5500 53 20
## 72 4 5600 53 1
## 73 2 6000 36 42
## 74 2 6000 37 6
## 75 2 6600 31 6
## 76 4 7700 55 36
## 77 3 4550 32 46
## 78 5 7500 42 28
## 79 3 7150 60 36
## 80 1 4950 31 27
## 81 3 6500 38 12
## 82 3 5850 40 19
## 83 2 4800 35 24
## 84 1 4400 53 38
## 85 2 5400 44 16
## 86 4 8400 33 11
## 87 3 4550 59 1
## 88 2 4200 33 45
## 89 2 7200 31 1
## 90 4 8400 33 11
## 91 2 6600 44 42
## 92 1 5500 42 11
## 93 4 8400 54 9
## 94 4 6300 44 19
## 95 1 5500 47 4
## 96 3 7800 49 24
## 97 3 5850 45 24
## 98 3 7800 41 31
## 99 2 6000 50 33
## 100 3 7800 36 9
## 101 4 8400 35 0
## 102 1 6050 54 31
## 103 3 7150 49 45
## 104 4 7700 59 44
## 105 1 3850 58 1
## 106 4 8400 36 1
## 107 4 7000 34 8
## 108 4 8400 56 26
## 109 1 6050 51 24
## 110 5 8250 50 32
## 111 2 4200 48 33
## 112 1 4400 31 5
## 113 4 4900 34 14
## 114 4 7700 43 14
## 115 2 5400 32 24
## 116 5 6000 43 5
## 117 5 9000 33 4
## 118 4 7700 54 44
## 119 5 9000 49 12
## 120 3 5850 47 15
## 121 1 4950 49 2
## 122 1 3850 30 42
## 123 4 8400 53 11
## 124 4 7700 35 31
## 125 3 7800 46 4
## 126 1 5500 56 6
## 127 2 6600 50 44
## 128 2 6600 59 27
## 129 5 7500 45 8
## 130 1 6600 39 10
## 131 5 6750 34 14
## 132 5 5250 52 14
## 133 3 7800 49 9
## 134 2 6600 55 41
## 135 2 6600 39 24
## 136 5 6000 56 28
## 137 2 4800 46 21
## 138 5 9000 40 9
## 139 2 4200 34 24
## 140 1 4950 44 4
## 141 2 4800 35 20
## 142 3 5850 52 6
## 143 2 5400 35 27
## 144 3 5200 48 49
## 145 1 6050 53 18
## 146 2 6600 41 30
## 147 1 6600 39 41
## 148 3 5200 56 26
## 149 1 3850 59 3
## 150 1 4400 57 17
## 151 4 7700 52 11
## 152 4 8400 35 10
## 153 5 5250 37 49
## 154 3 5850 49 19
## 155 3 7800 53 49
## 156 1 4950 57 30
## 157 1 6600 34 5
## 158 1 4400 55 45
## 159 1 3850 31 36
## 160 4 8400 52 28
## 161 1 6600 39 43
## 162 5 5250 56 35
## 163 5 5250 32 46
## 164 1 4400 43 33
## 165 2 4800 54 26
## 166 3 5850 50 29
## 167 4 8400 51 44
## 168 5 6000 49 48
## 169 4 6300 53 17
## 170 5 5250 50 22
## 171 3 7800 57 49
## 172 2 6000 52 23
## 173 2 6000 39 17
## 174 5 6750 35 30
## 175 2 4800 56 48
## 176 3 6500 57 14
## 177 3 7150 40 2
## 178 4 5600 40 42
## 179 2 4200 52 21
## 180 2 7200 31 36
## 181 1 3850 36 13
## 182 2 7200 48 38
## 183 4 4900 33 26
## 184 5 9000 41 27
## 185 3 4550 38 30
## 186 4 8400 35 37
## 187 4 8400 41 28
## 188 2 5400 54 14
## 189 2 5400 33 47
## 190 1 6050 39 24
## 191 4 5600 46 18
## 192 2 4800 39 49
## 193 1 4950 59 2
## 194 2 4800 57 28
## 195 5 6750 53 19
## 196 5 9000 59 16
## 197 1 3850 37 9
## 198 5 6000 43 14
## 199 1 5500 44 40
## 200 1 6050 36 4
## 201 5 6000 30 12
## 202 3 5850 52 28
## 203 2 6600 47 5
## 204 2 4800 34 19
## 205 1 3850 32 39
## 206 2 7200 44 10
## 207 4 8400 55 28
## 208 1 5500 30 20
## 209 3 6500 55 49
## 210 2 6600 58 39
## 211 4 6300 41 47
## 212 2 7200 44 27
## 213 4 7000 32 16
## 214 5 6750 49 5
## 215 3 6500 34 25
## 216 3 7800 40 0
## 217 2 7200 54 12
## 218 4 5600 42 7
## 219 5 9000 55 12
## 220 4 8400 35 0
## 221 2 6600 34 41
## 222 5 7500 32 7
## 223 1 4400 45 35
## 224 4 8400 36 22
## 225 1 4950 30 21
## 226 5 9000 53 0
## 227 5 9000 45 47
## 228 2 6000 40 37
## 229 4 7700 47 29
## 230 5 9000 41 8
## 231 3 7800 58 9
## 232 3 5200 56 34
## 233 2 4200 46 28
## 234 4 5600 58 38
## 235 4 5600 36 12
## 236 5 5250 57 3
## 237 1 3850 53 30
## 238 4 8400 38 9
## 239 4 7700 53 17
## 240 4 8400 56 27
## 241 3 6500 54 12
## 242 1 6600 41 10
## 243 2 6000 39 38
## 244 2 4800 38 44
## 245 5 9000 48 9
## 246 4 6300 55 46
## 247 4 4900 30 33
## 248 2 4800 57 36
## 249 3 7150 35 43
## 250 3 4550 51 28
## 251 2 4200 38 27
## 252 1 6050 59 44
## 253 3 5850 50 13
## 254 4 7700 43 31
## 255 1 5500 47 12
## 256 4 7700 52 13
## 257 2 5400 31 19
## 258 3 5850 59 19
## 259 5 5250 46 39
## 260 2 7200 60 25
## 261 2 6000 60 3
## 262 1 3850 40 25
## 263 2 6000 59 39
## 264 4 8400 39 30
## 265 4 8400 35 46
## 266 5 5250 40 24
## 267 1 5500 38 29
## 268 2 7200 59 6
## 269 2 4200 50 23
## 270 3 7800 39 4
## 271 3 5850 39 40
## 272 1 6050 57 25
## 273 5 9000 35 18
## 274 1 6600 58 30
## 275 4 4900 33 21
## 276 4 7700 59 19
## 277 4 8400 42 2
## 278 3 7150 55 19
## 279 3 6500 44 0
## 280 2 6600 56 6
## 281 1 4400 51 15
## 282 5 8250 33 20
## 283 3 7800 55 10
## 284 4 4900 53 45
## 285 3 7800 40 49
## 286 1 6600 59 24
## 287 4 7700 58 19
## 288 5 6000 50 37
## 289 5 9000 32 30
## 290 4 5600 43 24
## 291 4 7700 51 44
## 292 3 5200 31 25
## 293 3 6500 47 39
## 294 4 7700 55 14
## 295 4 6300 36 0
## 296 3 7150 56 10
## 297 4 7700 34 39
## 298 1 4400 34 36
## 299 2 7200 51 40
## 300 2 4200 45 6
## 301 2 6000 33 49
## 302 5 7500 55 36
## 303 5 6000 57 40
## 304 1 6050 51 43
## 305 1 4400 31 0
## 306 5 7500 51 11
## 307 1 3850 40 23
## 308 1 4400 35 29
## 309 2 7200 45 35
## 310 2 6600 55 19
## 311 3 6500 49 15
## 312 3 7800 54 26
## 313 1 6050 35 0
## 314 4 5600 41 11
## 315 2 7200 51 5
## 316 4 5600 50 9
## 317 2 6600 44 29
## 318 1 6600 60 36
## 319 3 7800 36 20
## 320 3 7800 60 13
## 321 1 4950 34 5
## 322 1 6050 41 7
## 323 5 9000 36 30
## 324 1 6050 52 6
## 325 4 5600 39 15
## 326 4 7000 30 24
## 327 4 7700 57 21
## 328 5 7500 52 8
## 329 2 6600 59 15
## 330 4 6300 38 21
## 331 5 8250 58 25
## 332 4 4900 39 46
## 333 4 8400 34 46
## 334 2 4200 51 9
## 335 5 8250 51 2
## 336 2 6600 50 26
## 337 1 6600 48 4
## 338 1 5500 53 7
## 339 3 5850 38 47
## 340 4 6300 45 26
## 341 1 6600 36 31
## 342 2 7200 55 17
## 343 5 9000 50 39
## 344 3 5200 52 20
## 345 4 7000 36 15
## 346 3 5200 46 15
## 347 3 5200 50 15
## 348 3 7150 39 7
## 349 4 4900 56 19
## 350 2 7200 59 41
## 351 4 7000 55 44
## 352 1 6600 54 10
## 353 3 5850 34 3
## 354 2 4800 55 15
## 355 5 5250 50 18
## 356 1 5500 54 10
## 357 4 6300 34 6
## 358 5 5250 43 28
## 359 3 4550 44 39
## 360 2 6600 38 12
## 361 2 7200 54 42
## 362 4 6300 40 21
## 363 5 6750 45 6
## 364 5 9000 47 2
## 365 3 4550 32 3
## 366 3 7800 48 39
## 367 1 3850 42 10
## 368 4 8400 43 17
## 369 3 7800 58 11
## 370 4 8400 44 21
## 371 3 7150 34 14
## 372 5 6750 47 49
## 373 1 4950 57 19
## 374 3 7800 49 17
## 375 4 8400 43 18
## 376 1 4400 55 9
## 377 2 7200 35 12
## 378 4 7000 57 49
## 379 1 6600 33 25
## 380 1 4950 41 14
## 381 3 4550 33 40
## 382 1 4950 43 23
## 383 3 5850 55 12
## 384 3 6500 33 8
## 385 3 7800 39 28
## 386 3 7800 42 11
## 387 5 9000 32 37
## 388 2 7200 53 3
## 389 4 4900 50 6
## 390 2 6000 44 36
## 391 3 7800 32 11
## 392 3 7800 43 33
## 393 3 4550 58 11
## 394 4 8400 54 8
## 395 4 8400 35 2
## 396 1 5500 58 4
## 397 4 7000 40 33
## 398 4 4900 59 8
## 399 1 3850 52 41
## 400 2 4200 48 27
## 401 3 5850 42 5
## 402 2 7200 43 23
## 403 4 7700 56 34
## 404 3 5850 45 1
## 405 5 8250 44 2
## 406 3 7800 42 2
## 407 2 4800 42 30
## 408 3 6500 41 22
## 409 2 4800 55 47
## 410 2 5400 52 15
## 411 5 9000 44 16
## 412 5 5250 59 29
## 413 1 5500 54 12
## 414 4 5600 41 5
## 415 5 5250 44 36
## 416 2 6000 34 14
## 417 2 7200 54 13
## 418 3 5850 32 7
## 419 5 9000 56 13
## 420 4 8400 33 45
## 421 5 5250 57 1
## 422 3 4550 55 26
## 423 1 6600 30 25
## 424 4 8400 37 49
## 425 2 6000 37 0
## 426 2 7200 40 38
## 427 5 7500 49 46
## 428 4 8400 41 12
## 429 2 7200 60 30
## 430 2 7200 31 23
## 431 2 6000 58 9
## 432 4 7000 36 22
## 433 1 6050 51 32
## 434 4 4900 59 26
## 435 3 5850 54 35
## 436 4 8400 46 10
## 437 3 7150 36 36
## 438 5 9000 45 2
## 439 1 4400 43 18
## 440 5 9000 32 16
## 441 5 8250 37 26
## 442 2 4800 49 33
## 443 4 8400 59 37
## 444 4 5600 58 36
## 445 4 8400 51 27
## 446 4 6300 36 47
## 447 4 7700 43 40
## 448 2 6000 38 49
## 449 4 7700 60 41
## 450 4 4900 45 36
## 451 1 6600 48 4
## 452 2 5400 32 24
## 453 1 4950 58 4
## 454 5 7500 42 19
## 455 4 7000 45 21
## 456 5 9000 33 43
## 457 2 4200 52 27
## 458 4 7700 55 46
## 459 4 7700 31 23
## 460 2 7200 39 26
## 461 3 5200 56 37
## 462 1 5500 49 39
## 463 4 8400 48 31
## 464 3 7150 56 7
## 465 3 5850 30 15
## 466 3 7800 53 0
## 467 5 6750 32 48
## 468 5 8250 52 4
## 469 2 6600 42 19
## 470 5 6000 43 26
## 471 4 7000 40 7
## 472 4 6300 48 12
## 473 4 8400 51 2
## 474 4 7700 57 8
## 475 4 7700 44 23
## 476 3 4550 57 44
## 477 3 7800 40 29
## 478 1 4950 35 34
## 479 2 4800 43 37
## 480 5 8250 31 35
## 481 4 8400 49 30
## 482 3 5850 55 10
## 483 3 7800 52 4
## 484 1 6050 55 37
## 485 5 6750 39 37
## 486 3 7150 33 35
## 487 5 5250 59 12
## 488 1 6050 43 12
## 489 2 5400 34 16
## 490 4 5600 37 1
## 491 1 6050 38 21
## 492 3 7800 42 21
## 493 1 6600 56 1
## 494 4 6300 40 17
## 495 2 5400 34 20
## 496 4 7000 31 40
## 497 2 6600 55 9
## 498 4 5600 51 25
## 499 3 5850 46 47
## 500 5 6000 46 48
## 501 5 6750 31 13
## 502 2 4200 51 8
## 503 2 6600 47 16
## 504 4 6300 45 35
## 505 1 4400 52 24
## 506 5 5250 51 27
## 507 5 9000 39 5
## 508 1 6600 35 41
## 509 1 6050 32 33
## 510 1 4950 57 0
## 511 5 8250 38 37
## 512 2 5400 60 6
## 513 2 7200 42 29
## 514 2 6600 46 48
## 515 3 7150 44 25
## 516 5 8250 48 17
## 517 4 8400 38 41
## 518 1 5500 32 28
## 519 5 7500 34 12
## 520 1 5500 53 8
## 521 2 6600 54 49
## 522 1 4400 53 38
## 523 5 8250 49 4
## 524 4 6300 30 11
## 525 5 8250 56 28
## 526 1 3850 36 19
## 527 4 8400 52 30
## 528 1 6600 30 3
## 529 3 6500 35 36
## 530 2 6000 57 12
## 531 2 7200 48 23
## 532 3 7800 44 7
## 533 2 7200 38 45
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## 535 2 6600 48 46
## 536 1 4950 52 14
## 537 2 4200 45 37
## 538 3 7800 49 17
## 539 5 5250 59 4
## 540 4 4900 30 12
## 541 2 7200 36 6
## 542 3 5850 43 31
## 543 1 4950 40 32
## 544 2 6000 40 8
## 545 1 5500 57 41
## 546 1 6600 41 25
## 547 1 4950 49 19
## 548 3 5200 52 12
## 549 1 4950 59 15
## 550 2 7200 31 16
## 551 2 5400 54 13
## 552 3 5200 54 16
## 553 2 6000 38 39
## 554 5 9000 40 1
## 555 1 3850 52 7
## 556 5 6000 42 33
## 557 2 5400 40 5
## 558 5 6750 40 2
## 559 2 4200 36 45
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## 561 1 6600 47 12
## 562 1 4950 40 18
## 563 1 4400 40 15
## 564 3 4550 49 27
## 565 3 5200 60 36
## 566 5 5250 41 26
## 567 5 5250 50 5
## 568 4 8400 60 35
## 569 5 6750 32 2
## 570 4 7000 46 14
## 571 4 7000 39 35
## 572 1 6600 57 40
## 573 2 4800 34 12
## 574 4 5600 40 0
## 575 5 9000 53 35
## 576 5 6000 43 12
## 577 4 4900 30 39
## 578 3 5850 33 34
## 579 1 5500 41 11
## 580 2 5400 40 1
## 581 3 5200 47 20
## 582 2 5400 32 8
## 583 5 9000 57 23
## 584 1 5500 45 19
## 585 3 5850 40 47
## 586 4 8400 60 8
## 587 2 6000 44 46
## 588 1 4400 59 10
## 589 1 6050 45 2
## 590 5 6000 45 49
## 591 1 6050 51 28
## 592 5 8250 34 22
## 593 3 6500 45 36
## 594 4 5600 33 16
## 595 1 5500 39 44
## 596 4 8400 47 35
## 597 3 4550 40 17
## 598 5 5250 35 11
## 599 3 6500 40 48
## 600 5 5250 52 4
## 601 3 6500 59 42
## 602 5 7500 30 16
## 603 1 4400 41 49
## 604 2 6600 42 19
## 605 5 9000 34 9
## 606 4 8400 49 1
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## 608 1 3850 59 30
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## 610 1 3850 33 15
## 611 1 6600 55 7
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## 629 5 9000 43 41
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## 635 1 4950 47 35
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## 638 2 5400 53 38
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## 641 2 7200 49 16
## 642 4 8400 43 39
## 643 4 8400 35 2
## 644 2 7200 44 26
## 645 2 7200 31 17
## 646 4 4900 60 14
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## 653 1 4950 47 38
## 654 1 4400 40 1
## 655 5 6000 31 27
## 656 2 4800 46 34
## 657 4 4900 59 30
## 658 1 4400 41 2
## 659 5 9000 49 3
## 660 4 8400 46 41
## 661 3 7150 34 23
## 662 1 4400 57 48
## 663 1 4400 40 8
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## 665 5 5250 36 25
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## 669 1 6050 53 22
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## 709 5 8250 40 26
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## 742 5 9000 48 21
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## 767 5 6000 34 26
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## 770 3 5200 42 32
## 771 1 3850 35 5
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## 4876 1 6600 44 3
## 4877 1 6600 40 2
## 4878 4 7000 38 9
## 4879 4 8400 45 27
## 4880 2 7200 35 38
## 4881 5 8250 52 13
## 4882 1 6600 59 16
## 4883 3 7800 40 18
## 4884 4 5600 31 35
## 4885 1 4400 57 8
## 4886 4 4900 51 22
## 4887 3 5200 51 13
## 4888 4 8400 33 39
## 4889 4 8400 54 26
## 4890 3 5200 40 48
## 4891 5 9000 52 15
## 4892 3 7150 38 8
## 4893 1 4400 55 31
## 4894 3 7800 46 0
## 4895 1 5500 36 31
## 4896 5 6750 44 48
## 4897 3 6500 39 1
## 4898 4 8400 53 20
## 4899 2 7200 51 16
## 4900 5 6000 38 29
## 4901 4 6300 34 27
## 4902 1 4950 52 2
## 4903 2 7200 34 34
## 4904 5 6750 35 37
## 4905 5 5250 31 42
## 4906 4 5600 32 46
## 4907 4 7000 30 33
## 4908 3 5200 49 22
## 4909 5 9000 43 15
## 4910 3 7150 49 32
## 4911 4 8400 43 47
## 4912 5 8250 41 17
## 4913 5 9000 42 14
## 4914 1 3850 48 18
## 4915 1 4400 43 22
## 4916 5 6750 49 5
## 4917 5 6000 44 33
## 4918 3 5850 58 28
## 4919 2 7200 37 16
## 4920 4 6300 55 25
## 4921 5 8250 58 25
## 4922 3 7800 39 15
## 4923 1 4950 33 2
## 4924 4 6300 58 24
## 4925 5 7500 41 23
## 4926 1 6600 44 13
## 4927 3 7150 40 15
## 4928 2 4800 39 0
## 4929 5 7500 60 42
## 4930 5 5250 39 14
## 4931 2 7200 30 10
## 4932 5 7500 54 13
## 4933 4 4900 50 21
## 4934 1 6050 49 30
## 4935 5 6000 35 20
## 4936 2 7200 44 37
## 4937 3 4550 33 18
## 4938 1 6050 56 35
## 4939 4 7700 36 10
## 4940 5 6750 32 6
## 4941 1 3850 47 45
## 4942 5 6750 59 33
## 4943 2 4800 41 24
## 4944 5 7500 37 26
## 4945 1 6600 44 33
## 4946 3 5200 57 41
## 4947 2 7200 33 44
## 4948 1 6050 48 10
## 4949 4 4900 36 2
## 4950 5 7500 34 15
## 4951 2 6000 55 28
## 4952 1 4400 42 8
## 4953 5 7500 51 42
## 4954 5 9000 52 19
## 4955 4 4900 39 18
## 4956 5 6750 34 6
## 4957 1 4950 31 31
## 4958 1 6600 44 36
## 4959 2 7200 37 39
## 4960 3 7800 52 36
## 4961 3 5200 46 25
## 4962 3 5850 44 17
## 4963 3 7800 59 37
## 4964 3 5850 44 0
## 4965 5 6000 54 32
## 4966 3 5200 59 34
## 4967 3 4550 36 18
## 4968 1 3850 44 6
## 4969 5 9000 32 5
## 4970 1 4400 42 26
## 4971 5 9000 59 39
## 4972 5 6750 52 37
## 4973 5 5250 40 3
## 4974 3 7150 48 2
## 4975 2 6600 40 17
## 4976 2 7200 53 41
## 4977 5 9000 41 25
## 4978 5 9000 57 40
## 4979 2 4200 35 35
## 4980 4 5600 38 30
## 4981 2 6000 45 44
## 4982 4 7700 32 37
## 4983 4 6300 36 26
## 4984 4 4900 50 4
## 4985 2 7200 49 39
## 4986 1 3850 30 17
## 4987 4 4900 49 10
## 4988 3 7800 36 48
## 4989 1 6050 36 0
## 4990 5 8250 46 21
## 4991 4 7700 46 48
## 4992 1 6050 38 31
## 4993 2 7200 38 49
## 4994 3 7150 52 41
## 4995 1 6600 59 7
## 4996 4 6300 42 43
## 4997 2 5400 31 33
## 4998 5 9000 38 46
## 4999 5 9000 36 9
## Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours
## 1 22 2 0 14 66
## 2 13 14 100 12 61
## 3 6 3 50 10 1
## 4 28 12 100 10 0
## 5 29 13 100 15 9
## 6 8 0 100 15 95
## 7 29 2 0 16 27
## 8 7 8 100 7 64
## 9 21 14 0 1 0
## 10 2 6 75 4 53
## 11 5 0 25 4 90
## 12 2 13 25 5 88
## 13 16 1 75 7 17
## 14 5 13 25 4 26
## 15 13 1 100 13 37
## 16 9 13 50 3 46
## 17 29 12 0 6 76
## 18 6 12 75 12 54
## 19 24 1 75 1 36
## 20 9 1 0 5 66
## 21 13 13 50 15 53
## 22 9 2 25 4 20
## 23 9 13 0 13 71
## 24 28 8 25 6 78
## 25 24 11 25 7 49
## 26 0 13 25 3 9
## 27 23 6 0 7 26
## 28 3 10 75 4 45
## 29 14 6 50 6 42
## 30 29 9 25 2 37
## 31 25 2 50 12 16
## 32 7 7 50 18 22
## 33 16 14 50 17 37
## 34 24 8 75 18 37
## 35 0 10 75 14 36
## 36 18 11 100 8 21
## 37 10 2 0 16 18
## 38 15 7 0 2 48
## 39 1 1 0 11 6
## 40 2 8 100 14 85
## 41 9 13 0 16 28
## 42 6 2 25 8 28
## 43 4 6 75 13 21
## 44 2 3 0 7 85
## 45 29 3 25 12 4
## 46 11 6 75 7 90
## 47 26 1 75 5 21
## 48 20 2 75 15 82
## 49 0 2 100 17 13
## 50 19 13 50 3 15
## 51 29 4 100 2 28
## 52 12 8 25 10 18
## 53 4 3 0 5 12
## 54 0 8 100 1 92
## 55 6 6 25 3 68
## 56 21 3 75 8 7
## 57 28 8 75 1 36
## 58 8 12 0 3 27
## 59 5 2 25 12 74
## 60 19 8 50 5 99
## 61 14 4 50 6 20
## 62 0 13 75 16 75
## 63 12 0 75 18 73
## 64 14 9 25 17 21
## 65 13 9 75 5 3
## 66 4 3 25 2 12
## 67 19 0 75 19 93
## 68 17 14 50 3 64
## 69 9 6 0 5 9
## 70 13 11 25 16 8
## 71 13 8 100 8 75
## 72 16 11 50 11 15
## 73 15 1 0 5 41
## 74 15 1 0 12 8
## 75 24 10 75 10 1
## 76 22 3 100 10 84
## 77 19 0 50 19 82
## 78 6 6 25 1 38
## 79 27 13 25 5 94
## 80 5 6 0 12 53
## 81 25 6 100 13 94
## 82 14 14 100 14 0
## 83 10 13 0 4 85
## 84 22 14 100 17 67
## 85 13 13 100 11 0
## 86 7 4 100 5 40
## 87 9 1 25 17 59
## 88 2 4 50 18 12
## 89 25 5 0 15 22
## 90 10 2 100 9 53
## 91 5 6 0 9 26
## 92 26 12 100 15 84
## 93 29 7 25 15 3
## 94 10 7 50 1 81
## 95 19 10 0 19 33
## 96 20 2 50 19 43
## 97 16 12 50 7 0
## 98 12 10 75 9 64
## 99 0 9 0 13 37
## 100 10 14 25 13 30
## 101 18 14 75 17 48
## 102 11 3 0 2 48
## 103 29 2 75 6 57
## 104 18 13 100 12 53
## 105 1 0 0 4 29
## 106 6 9 50 17 10
## 107 17 12 100 16 1
## 108 7 9 100 15 12
## 109 2 11 75 3 82
## 110 28 5 50 9 56
## 111 4 7 25 12 75
## 112 25 12 75 3 34
## 113 12 8 25 2 59
## 114 0 14 75 5 31
## 115 1 0 25 11 88
## 116 29 8 100 11 96
## 117 15 3 75 17 44
## 118 17 3 0 1 81
## 119 27 10 25 19 4
## 120 6 1 25 11 65
## 121 12 6 75 6 23
## 122 4 11 75 6 13
## 123 28 5 0 12 60
## 124 19 7 75 14 19
## 125 12 7 0 1 36
## 126 3 12 0 6 60
## 127 17 0 100 16 35
## 128 22 6 100 1 44
## 129 7 6 50 12 66
## 130 25 1 100 1 66
## 131 27 6 50 15 97
## 132 28 5 25 17 34
## 133 15 9 75 12 92
## 134 13 2 25 13 44
## 135 0 12 25 11 63
## 136 29 1 0 12 94
## 137 17 2 25 6 9
## 138 12 2 100 17 34
## 139 23 0 100 12 22
## 140 5 5 50 4 11
## 141 11 1 25 13 16
## 142 8 14 50 6 45
## 143 19 2 50 15 93
## 144 27 3 75 11 64
## 145 15 11 50 5 46
## 146 12 4 25 18 80
## 147 7 13 0 7 1
## 148 25 6 100 14 34
## 149 13 0 50 13 78
## 150 21 12 100 6 92
## 151 15 13 50 12 19
## 152 10 0 50 1 51
## 153 22 14 100 6 21
## 154 1 7 100 12 15
## 155 17 1 75 3 30
## 156 24 8 100 13 70
## 157 7 1 25 17 71
## 158 14 5 50 7 11
## 159 21 12 100 6 98
## 160 17 1 100 12 27
## 161 27 10 100 3 70
## 162 20 10 0 16 41
## 163 15 14 50 19 29
## 164 2 2 0 4 53
## 165 23 0 100 16 43
## 166 23 9 75 12 47
## 167 1 6 50 12 78
## 168 25 0 25 19 93
## 169 23 5 75 14 84
## 170 26 2 25 5 49
## 171 9 11 50 13 52
## 172 17 3 25 6 50
## 173 1 8 25 8 63
## 174 18 11 0 5 16
## 175 21 1 100 2 42
## 176 12 12 75 14 16
## 177 7 8 100 18 99
## 178 14 9 100 16 47
## 179 8 13 50 19 79
## 180 25 3 0 9 36
## 181 13 5 0 13 67
## 182 9 8 50 17 56
## 183 29 4 75 5 21
## 184 17 7 25 12 62
## 185 1 11 75 9 56
## 186 7 10 100 5 59
## 187 22 6 100 8 38
## 188 8 9 25 11 97
## 189 15 5 50 10 11
## 190 24 6 100 12 4
## 191 0 9 0 10 12
## 192 24 14 25 15 64
## 193 22 3 0 4 99
## 194 26 2 50 15 85
## 195 22 4 0 2 61
## 196 24 2 50 1 26
## 197 8 11 75 5 15
## 198 22 6 75 17 42
## 199 28 14 50 12 56
## 200 26 8 100 9 38
## 201 14 9 100 8 26
## 202 21 3 0 16 14
## 203 20 10 75 19 87
## 204 18 6 75 14 37
## 205 2 2 50 14 40
## 206 21 0 0 5 88
## 207 1 14 50 5 66
## 208 7 10 0 6 64
## 209 24 8 50 15 75
## 210 13 3 0 14 23
## 211 26 2 50 7 98
## 212 9 10 100 13 52
## 213 10 9 75 6 21
## 214 11 6 75 17 99
## 215 1 5 0 1 69
## 216 22 7 100 16 74
## 217 17 8 75 7 5
## 218 11 5 25 2 7
## 219 5 11 0 1 95
## 220 27 6 50 7 82
## 221 7 11 25 12 9
## 222 2 9 100 10 14
## 223 25 12 100 12 52
## 224 23 2 50 1 43
## 225 20 4 75 2 29
## 226 11 7 25 13 65
## 227 18 7 25 15 69
## 228 8 5 75 12 53
## 229 3 9 75 14 7
## 230 27 3 75 9 70
## 231 6 9 0 5 56
## 232 28 3 25 2 80
## 233 20 2 75 2 29
## 234 10 6 25 14 58
## 235 9 11 75 12 91
## 236 28 5 0 17 29
## 237 3 11 25 15 33
## 238 23 7 75 12 37
## 239 18 1 50 16 55
## 240 19 4 75 8 90
## 241 9 11 0 11 32
## 242 24 4 50 2 8
## 243 28 9 25 1 33
## 244 4 3 50 11 31
## 245 7 1 100 3 65
## 246 26 11 0 9 87
## 247 20 3 25 15 18
## 248 5 4 100 19 37
## 249 15 10 25 5 75
## 250 4 9 50 3 84
## 251 14 6 100 6 20
## 252 23 4 0 17 25
## 253 27 10 0 9 57
## 254 26 13 50 3 37
## 255 24 9 25 17 3
## 256 8 9 100 13 81
## 257 16 12 25 8 12
## 258 22 6 75 3 78
## 259 25 2 100 16 57
## 260 6 13 75 11 94
## 261 8 13 0 17 93
## 262 10 10 50 16 89
## 263 7 1 0 10 9
## 264 29 7 0 17 18
## 265 2 11 100 3 81
## 266 17 6 0 5 4
## 267 24 10 25 15 76
## 268 11 1 100 17 32
## 269 29 9 75 9 69
## 270 20 2 0 9 57
## 271 22 6 0 8 19
## 272 19 5 0 14 25
## 273 28 3 75 8 5
## 274 23 10 50 1 33
## 275 8 7 75 8 12
## 276 25 5 25 14 53
## 277 15 2 100 8 74
## 278 20 11 50 15 66
## 279 19 0 25 7 48
## 280 13 7 75 1 49
## 281 15 13 25 19 36
## 282 5 2 75 14 42
## 283 23 0 100 7 45
## 284 24 1 50 19 75
## 285 29 2 25 14 66
## 286 1 14 50 8 2
## 287 25 9 100 15 58
## 288 21 6 0 14 66
## 289 27 8 25 5 94
## 290 23 7 0 1 5
## 291 23 5 25 19 88
## 292 29 1 100 3 46
## 293 27 12 0 3 33
## 294 9 3 25 2 2
## 295 25 1 50 19 39
## 296 29 13 50 15 21
## 297 8 4 0 11 35
## 298 1 0 75 9 76
## 299 6 14 50 16 92
## 300 23 12 100 9 23
## 301 7 11 25 6 68
## 302 17 9 75 10 62
## 303 17 12 100 13 19
## 304 5 11 25 18 94
## 305 5 1 100 7 20
## 306 21 14 50 7 77
## 307 17 14 25 18 26
## 308 28 13 25 2 30
## 309 4 1 0 19 53
## 310 1 9 100 14 76
## 311 5 9 100 16 84
## 312 29 9 100 6 57
## 313 26 12 50 9 90
## 314 1 0 25 1 84
## 315 25 2 0 2 57
## 316 14 1 0 15 71
## 317 6 12 100 5 28
## 318 13 9 75 19 66
## 319 1 9 50 16 52
## 320 4 6 100 19 34
## 321 26 2 100 16 41
## 322 0 12 75 18 37
## 323 9 10 0 7 3
## 324 21 14 75 18 58
## 325 15 10 75 16 87
## 326 9 9 100 5 74
## 327 11 8 25 18 65
## 328 19 2 25 7 42
## 329 21 10 100 5 42
## 330 4 9 25 11 26
## 331 11 7 25 12 4
## 332 0 7 75 6 3
## 333 1 9 100 11 68
## 334 28 1 0 15 45
## 335 4 1 75 15 86
## 336 6 8 75 12 83
## 337 0 3 100 16 26
## 338 26 7 50 19 51
## 339 9 14 25 3 67
## 340 14 3 100 3 22
## 341 3 1 75 8 59
## 342 1 7 100 6 20
## 343 11 1 25 16 6
## 344 1 6 0 17 86
## 345 16 13 25 19 36
## 346 19 10 75 1 73
## 347 9 3 100 4 97
## 348 28 0 75 2 57
## 349 15 0 0 15 81
## 350 17 13 75 3 9
## 351 23 3 50 6 89
## 352 27 4 75 5 32
## 353 18 5 0 4 68
## 354 18 13 0 13 41
## 355 15 9 0 5 11
## 356 14 0 0 8 94
## 357 23 6 100 8 63
## 358 10 11 100 3 90
## 359 24 12 100 15 35
## 360 7 13 50 15 10
## 361 26 12 25 6 42
## 362 25 10 50 16 91
## 363 13 0 75 12 92
## 364 8 8 25 9 37
## 365 24 2 75 7 98
## 366 18 9 0 18 0
## 367 23 5 25 4 28
## 368 8 11 100 1 74
## 369 15 6 100 7 54
## 370 10 12 50 8 88
## 371 2 2 0 9 80
## 372 12 1 0 3 27
## 373 3 7 25 3 0
## 374 25 13 100 16 7
## 375 16 10 50 5 11
## 376 16 13 75 18 80
## 377 1 14 75 13 7
## 378 28 2 75 14 29
## 379 14 13 0 4 40
## 380 10 5 75 9 87
## 381 25 1 0 14 43
## 382 23 13 0 11 32
## 383 12 3 25 3 58
## 384 20 10 100 8 88
## 385 17 14 50 19 61
## 386 23 4 25 11 31
## 387 11 14 50 9 64
## 388 7 5 75 17 25
## 389 10 7 75 2 95
## 390 14 9 50 7 89
## 391 20 4 50 12 76
## 392 11 3 25 2 85
## 393 10 3 25 13 67
## 394 23 4 75 9 25
## 395 5 0 0 17 19
## 396 20 11 25 3 97
## 397 25 12 0 9 96
## 398 27 7 25 6 29
## 399 3 2 25 16 12
## 400 6 3 50 17 7
## 401 21 12 100 13 74
## 402 7 8 25 9 70
## 403 9 7 100 4 60
## 404 1 3 25 18 40
## 405 7 1 25 2 66
## 406 17 14 0 11 73
## 407 10 5 50 16 24
## 408 1 2 0 16 41
## 409 28 12 25 15 51
## 410 29 6 50 14 13
## 411 19 6 0 6 88
## 412 16 8 75 5 18
## 413 29 2 50 3 55
## 414 23 2 25 5 47
## 415 19 10 0 1 97
## 416 16 11 25 4 9
## 417 24 2 75 4 93
## 418 24 8 50 8 94
## 419 10 7 100 19 86
## 420 25 7 50 8 58
## 421 13 13 25 13 63
## 422 16 3 0 6 43
## 423 5 14 75 15 2
## 424 8 14 25 11 62
## 425 23 7 75 15 48
## 426 10 8 50 13 28
## 427 21 4 100 15 41
## 428 6 2 50 8 19
## 429 23 13 50 4 12
## 430 27 9 25 18 4
## 431 26 6 0 16 57
## 432 24 9 50 6 27
## 433 9 10 25 6 45
## 434 5 1 75 18 5
## 435 2 5 75 17 75
## 436 2 7 50 3 85
## 437 2 0 0 19 5
## 438 3 13 25 19 50
## 439 10 11 25 12 26
## 440 12 10 50 17 5
## 441 18 9 100 16 12
## 442 4 3 75 3 54
## 443 11 5 25 5 40
## 444 17 0 0 9 7
## 445 5 13 25 14 10
## 446 11 4 25 18 88
## 447 12 5 25 3 37
## 448 9 6 50 13 62
## 449 27 0 0 16 93
## 450 14 3 0 5 7
## 451 6 13 100 10 78
## 452 7 11 0 11 53
## 453 18 7 75 3 59
## 454 27 5 75 4 28
## 455 28 1 75 5 68
## 456 13 4 75 9 94
## 457 3 6 25 11 4
## 458 9 4 75 17 40
## 459 27 6 50 11 60
## 460 18 0 75 12 67
## 461 28 11 50 11 98
## 462 21 8 100 10 74
## 463 3 7 75 5 62
## 464 12 7 100 5 64
## 465 13 2 75 10 35
## 466 1 7 25 10 5
## 467 28 13 25 4 99
## 468 28 6 0 8 48
## 469 0 6 50 10 54
## 470 23 5 50 13 48
## 471 15 5 75 10 22
## 472 28 1 50 4 27
## 473 2 1 50 19 64
## 474 2 8 75 8 70
## 475 22 0 0 16 6
## 476 8 2 0 10 58
## 477 7 7 75 14 3
## 478 11 13 25 18 26
## 479 25 14 0 2 10
## 480 0 0 75 3 42
## 481 26 2 25 11 39
## 482 24 2 75 3 48
## 483 17 9 75 15 34
## 484 21 12 0 12 26
## 485 22 9 50 14 8
## 486 15 6 0 8 80
## 487 1 5 50 9 20
## 488 14 0 0 19 86
## 489 6 0 0 4 97
## 490 2 0 50 18 11
## 491 25 0 100 4 99
## 492 20 14 0 11 7
## 493 21 12 75 17 64
## 494 3 10 25 10 88
## 495 7 5 100 10 37
## 496 23 10 50 3 69
## 497 10 5 100 1 26
## 498 18 6 50 17 48
## 499 0 8 0 11 62
## 500 15 5 0 19 97
## 501 9 8 75 18 90
## 502 18 8 0 16 98
## 503 22 10 100 15 19
## 504 28 10 25 11 36
## 505 0 3 0 11 39
## 506 2 9 75 3 87
## 507 22 5 25 17 45
## 508 20 13 50 12 51
## 509 5 9 100 17 57
## 510 0 3 75 4 7
## 511 2 0 0 14 15
## 512 18 11 25 10 74
## 513 14 5 0 11 72
## 514 3 13 0 10 51
## 515 5 2 75 11 30
## 516 11 1 25 10 44
## 517 25 12 50 9 3
## 518 16 14 100 1 93
## 519 0 10 0 15 47
## 520 16 4 100 10 56
## 521 9 9 75 19 57
## 522 23 10 25 6 28
## 523 4 3 75 1 81
## 524 26 10 50 2 12
## 525 14 12 75 18 48
## 526 18 9 100 9 67
## 527 20 6 25 11 51
## 528 11 9 75 4 6
## 529 0 5 100 18 68
## 530 4 8 0 16 22
## 531 20 14 75 14 88
## 532 3 3 75 15 87
## 533 21 9 100 9 39
## 534 25 11 25 5 97
## 535 21 0 75 9 47
## 536 10 12 50 4 26
## 537 29 5 75 3 89
## 538 25 6 0 5 57
## 539 13 7 100 4 29
## 540 29 1 25 9 89
## 541 20 4 75 2 2
## 542 10 10 100 3 26
## 543 26 12 75 16 42
## 544 9 8 50 4 3
## 545 12 7 100 10 45
## 546 11 13 75 11 5
## 547 28 9 0 13 79
## 548 5 4 25 18 31
## 549 23 12 0 6 81
## 550 11 4 0 9 62
## 551 24 14 50 18 59
## 552 22 0 100 17 17
## 553 2 4 25 16 46
## 554 27 5 100 19 7
## 555 23 2 50 18 96
## 556 6 12 0 19 19
## 557 14 9 75 13 90
## 558 6 8 50 15 98
## 559 1 1 25 7 34
## 560 24 14 100 13 40
## 561 23 14 0 18 37
## 562 13 12 100 13 61
## 563 0 6 0 17 12
## 564 25 13 0 3 20
## 565 25 14 25 15 3
## 566 11 2 75 17 60
## 567 12 10 100 18 75
## 568 1 7 0 11 52
## 569 3 9 75 9 2
## 570 29 14 50 18 87
## 571 18 3 100 11 5
## 572 24 13 75 19 23
## 573 6 14 50 15 56
## 574 20 5 100 13 92
## 575 19 14 75 4 31
## 576 7 6 75 7 5
## 577 6 14 100 17 47
## 578 20 11 100 8 0
## 579 23 4 25 11 84
## 580 8 12 75 9 77
## 581 19 14 25 5 35
## 582 27 14 75 7 86
## 583 25 8 75 14 13
## 584 17 9 100 13 32
## 585 4 4 100 15 71
## 586 9 7 50 4 37
## 587 13 3 0 5 14
## 588 6 3 0 11 20
## 589 9 12 50 14 15
## 590 11 11 50 18 62
## 591 7 3 50 8 90
## 592 12 8 75 2 15
## 593 11 4 75 7 39
## 594 28 1 50 11 25
## 595 24 0 0 9 8
## 596 4 13 100 18 28
## 597 18 7 75 18 18
## 598 16 1 75 6 56
## 599 20 9 50 2 64
## 600 25 10 75 8 38
## 601 7 10 75 1 69
## 602 5 1 0 3 95
## 603 24 5 25 15 8
## 604 11 2 25 13 19
## 605 20 6 100 17 41
## 606 3 12 75 10 75
## 607 21 12 25 9 53
## 608 9 5 25 2 2
## 609 17 3 25 16 33
## 610 20 8 100 18 16
## 611 19 7 75 15 15
## 612 14 9 100 1 46
## 613 17 11 25 9 95
## 614 1 7 0 15 77
## 615 0 6 25 7 69
## 616 13 9 0 6 29
## 617 28 12 75 17 27
## 618 9 2 50 4 50
## 619 7 7 25 8 23
## 620 5 1 0 19 40
## 621 10 3 0 16 49
## 622 19 0 0 11 39
## 623 28 12 50 6 34
## 624 23 9 50 11 37
## 625 9 6 50 12 75
## 626 21 1 25 2 17
## 627 14 14 50 5 82
## 628 17 0 0 11 16
## 629 22 10 100 15 87
## 630 21 3 50 15 97
## 631 16 8 0 2 37
## 632 1 1 0 19 24
## 633 3 6 50 15 73
## 634 1 4 75 19 39
## 635 2 3 0 8 28
## 636 25 8 25 16 64
## 637 8 0 50 6 24
## 638 25 8 50 14 70
## 639 26 4 0 13 88
## 640 20 12 100 11 96
## 641 10 5 0 5 63
## 642 6 11 25 4 11
## 643 20 3 50 14 55
## 644 3 5 0 3 97
## 645 22 6 100 15 59
## 646 16 10 100 18 19
## 647 19 0 0 2 19
## 648 1 5 75 5 54
## 649 23 14 75 5 15
## 650 23 12 100 11 90
## 651 23 0 100 2 99
## 652 11 9 0 18 30
## 653 27 3 25 3 75
## 654 11 5 25 18 89
## 655 7 8 100 7 81
## 656 25 6 100 17 23
## 657 10 8 0 12 59
## 658 15 3 100 16 94
## 659 26 4 50 12 86
## 660 4 7 100 11 23
## 661 3 2 0 9 63
## 662 16 3 75 12 18
## 663 9 5 75 2 29
## 664 8 14 100 2 89
## 665 20 13 75 14 87
## 666 19 2 75 9 10
## 667 7 14 0 7 30
## 668 16 4 0 11 99
## 669 26 5 50 6 98
## 670 16 7 100 4 58
## 671 22 14 100 10 22
## 672 17 6 100 15 98
## 673 26 3 100 14 83
## 674 23 1 75 4 79
## 675 27 10 0 11 67
## 676 27 6 50 17 67
## 677 6 0 0 11 25
## 678 10 5 50 10 74
## 679 28 12 25 15 21
## 680 26 14 0 14 33
## 681 23 7 75 14 16
## 682 7 6 75 8 31
## 683 23 2 25 4 63
## 684 23 8 25 10 72
## 685 10 13 50 3 48
## 686 15 6 75 10 93
## 687 26 1 25 12 66
## 688 23 0 50 9 50
## 689 18 6 100 12 15
## 690 26 5 50 17 3
## 691 2 6 0 10 6
## 692 28 7 50 16 92
## 693 6 12 50 4 94
## 694 11 5 100 15 47
## 695 10 10 0 14 81
## 696 6 9 25 10 69
## 697 16 4 25 13 53
## 698 13 4 100 5 32
## 699 13 10 0 2 95
## 700 8 6 25 19 90
## 701 5 9 100 7 96
## 702 16 8 75 16 0
## 703 6 1 50 6 60
## 704 14 14 100 16 55
## 705 2 4 75 5 48
## 706 19 13 75 8 25
## 707 28 2 50 9 56
## 708 17 12 75 19 41
## 709 2 11 0 17 53
## 710 17 10 75 11 73
## 711 0 10 50 14 96
## 712 6 1 25 15 74
## 713 27 0 100 11 46
## 714 8 4 75 10 88
## 715 13 2 50 11 53
## 716 3 2 0 15 39
## 717 14 6 50 5 36
## 718 0 0 50 3 19
## 719 17 0 75 2 14
## 720 2 11 25 10 37
## 721 17 3 25 17 51
## 722 15 11 25 8 56
## 723 7 3 75 14 84
## 724 12 1 75 6 19
## 725 28 9 0 3 2
## 726 29 1 25 10 65
## 727 21 14 75 6 63
## 728 25 5 25 7 4
## 729 16 8 0 16 44
## 730 9 9 75 19 31
## 731 27 2 100 11 10
## 732 10 2 0 1 92
## 733 28 11 0 6 9
## 734 8 3 50 13 71
## 735 25 13 0 10 81
## 736 17 4 75 2 90
## 737 20 13 0 19 43
## 738 17 10 100 1 65
## 739 25 9 50 18 89
## 740 2 7 25 17 8
## 741 24 11 100 13 71
## 742 27 6 75 1 63
## 743 11 13 100 5 75
## 744 5 3 25 10 89
## 745 19 12 25 19 68
## 746 15 12 75 1 54
## 747 4 6 75 1 74
## 748 15 2 25 6 86
## 749 28 7 25 17 32
## 750 11 6 25 17 71
## 751 9 0 25 16 63
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## 4856 27 12 100 8 38
## 4857 9 3 100 16 42
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## 4859 11 7 50 5 90
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## 4867 2 12 0 15 69
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## 4869 11 10 50 7 25
## 4870 18 4 25 14 27
## 4871 18 5 75 7 11
## 4872 20 2 0 13 42
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## 4881 18 6 0 16 42
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## 4886 7 10 50 10 6
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## 4896 14 7 75 16 57
## 4897 24 7 50 8 40
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## 4908 6 1 50 18 4
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## 4936 12 4 0 15 30
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## 4942 22 9 50 11 27
## 4943 2 4 50 13 97
## 4944 12 5 50 3 24
## 4945 5 7 50 16 21
## 4946 8 12 50 8 76
## 4947 6 9 50 8 11
## 4948 10 1 50 19 56
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## 4959 4 0 0 18 63
## 4960 27 2 25 14 48
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## 4966 9 9 0 10 48
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## 4996 24 5 100 9 34
## 4997 12 8 50 8 81
## 4998 9 5 75 4 36
## 4999 9 13 0 13 32
## Promotions Employee_Satisfaction_Score Resigned
## 1 0 2.63 False
## 2 2 1.72 False
## 3 0 3.17 False
## 4 1 1.86 False
## 5 1 1.25 False
## 6 0 2.77 False
## 7 0 4.46 False
## 8 0 2.09 False
## 9 1 1.44 False
## 10 2 2.93 False
## 11 1 2.34 False
## 12 0 1.96 False
## 13 2 2.13 False
## 14 1 1.46 False
## 15 1 2.22 False
## 16 1 2.42 False
## 17 2 3.15 False
## 18 0 1.19 False
## 19 0 1.12 False
## 20 0 3.91 False
## 21 2 3.70 False
## 22 0 4.66 False
## 23 2 3.68 False
## 24 2 4.26 False
## 25 1 4.43 False
## 26 2 2.12 False
## 27 1 3.16 False
## 28 2 1.35 True
## 29 1 4.52 False
## 30 2 4.95 False
## 31 2 4.65 False
## 32 0 1.95 False
## 33 0 4.43 False
## 34 2 2.10 False
## 35 1 1.94 False
## 36 1 4.21 False
## 37 1 4.97 False
## 38 0 2.47 False
## 39 1 2.66 False
## 40 1 4.22 False
## 41 2 2.90 True
## 42 2 4.84 False
## 43 0 3.59 False
## 44 0 3.32 False
## 45 1 2.53 False
## 46 1 1.59 False
## 47 2 1.27 False
## 48 2 4.84 False
## 49 1 2.90 False
## 50 0 1.47 False
## 51 1 3.02 False
## 52 1 1.43 False
## 53 0 2.56 False
## 54 1 1.10 False
## 55 1 1.87 True
## 56 0 1.70 False
## 57 2 2.64 True
## 58 2 3.97 False
## 59 2 4.23 True
## 60 1 3.02 False
## 61 1 2.32 False
## 62 2 2.15 False
## 63 1 2.26 False
## 64 0 1.68 True
## 65 0 4.33 False
## 66 1 2.13 False
## 67 0 4.26 False
## 68 2 2.45 False
## 69 0 2.42 False
## 70 0 4.45 False
## 71 1 4.49 False
## 72 2 3.55 False
## 73 0 1.31 False
## 74 1 4.98 False
## 75 1 1.39 False
## 76 1 1.75 False
## 77 0 3.20 False
## 78 1 3.55 False
## 79 2 3.50 True
## 80 1 2.09 False
## 81 0 3.75 False
## 82 0 3.40 True
## 83 0 1.33 False
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## 85 1 4.27 False
## 86 2 2.37 False
## 87 2 1.77 False
## 88 0 1.11 False
## 89 1 2.08 False
## 90 0 3.22 False
## 91 2 3.23 True
## 92 1 3.69 True
## 93 2 3.66 False
## 94 1 4.10 False
## 95 0 4.36 False
## 96 2 1.03 True
## 97 0 2.34 False
## 98 0 4.20 False
## 99 1 4.39 False
## 100 1 3.19 True
## 101 0 3.42 False
## 102 1 3.63 True
## 103 2 5.00 True
## 104 1 2.13 False
## 105 1 2.30 False
## 106 0 2.94 False
## 107 0 2.80 False
## 108 2 4.68 False
## 109 1 2.70 False
## 110 0 1.64 False
## 111 2 4.80 False
## 112 0 3.51 False
## 113 0 4.50 False
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## 115 1 1.58 False
## 116 0 2.47 False
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## 118 1 4.20 False
## 119 1 2.87 False
## 120 0 4.37 False
## 121 1 4.17 False
## 122 1 2.94 True
## 123 1 4.47 False
## 124 0 1.14 False
## 125 2 1.39 False
## 126 0 4.65 False
## 127 0 4.40 False
## 128 2 1.74 False
## 129 2 1.24 True
## 130 2 1.61 False
## 131 2 2.84 True
## 132 0 2.87 False
## 133 0 1.96 False
## 134 1 3.83 False
## 135 0 3.79 False
## 136 0 3.03 False
## 137 1 1.25 False
## 138 2 4.83 False
## 139 0 1.12 False
## 140 0 4.38 False
## 141 1 3.61 False
## 142 1 4.61 False
## 143 0 2.62 False
## 144 2 1.82 False
## 145 0 2.23 False
## 146 2 4.36 False
## 147 0 3.30 False
## 148 2 4.55 False
## 149 2 4.75 False
## 150 0 3.00 False
## 151 0 4.76 False
## 152 1 1.02 False
## 153 2 4.44 False
## 154 2 4.88 False
## 155 2 3.78 False
## 156 1 1.05 False
## 157 0 1.79 False
## 158 2 3.34 False
## 159 0 4.04 False
## 160 0 4.15 True
## 161 1 2.85 False
## 162 2 2.04 False
## 163 0 1.78 False
## 164 1 1.97 True
## 165 0 1.82 True
## 166 2 3.49 False
## 167 1 1.14 False
## 168 0 1.97 False
## 169 0 4.25 False
## 170 2 2.59 False
## 171 2 4.72 False
## 172 2 3.63 False
## 173 1 2.24 False
## 174 0 3.36 False
## 175 2 3.85 False
## 176 1 3.59 True
## 177 1 3.79 False
## 178 0 1.14 False
## 179 2 1.30 False
## 180 2 2.89 False
## 181 1 2.39 True
## 182 2 4.28 False
## 183 1 2.47 False
## 184 2 1.46 False
## 185 2 2.69 False
## 186 2 4.38 False
## 187 0 3.08 False
## 188 2 4.01 False
## 189 0 2.80 True
## 190 0 3.85 False
## 191 0 2.32 False
## 192 1 3.84 True
## 193 0 3.94 False
## 194 0 1.48 False
## 195 0 2.51 True
## 196 1 1.59 True
## 197 2 1.49 False
## 198 2 3.45 False
## 199 0 3.23 False
## 200 2 3.73 True
## 201 1 2.41 False
## 202 2 3.47 False
## 203 1 4.06 False
## 204 0 2.63 False
## 205 1 1.25 False
## 206 2 4.90 True
## 207 2 1.02 False
## 208 1 4.61 True
## 209 2 2.96 False
## 210 0 4.32 False
## 211 0 2.86 True
## 212 2 2.76 False
## 213 0 4.27 False
## 214 1 2.42 False
## 215 0 1.75 False
## 216 2 4.21 True
## 217 0 4.58 False
## 218 0 4.77 False
## 219 1 4.14 False
## 220 1 4.97 False
## 221 1 3.23 False
## 222 1 4.74 False
## 223 0 4.98 False
## 224 1 2.76 False
## 225 1 1.49 False
## 226 2 4.90 False
## 227 0 1.66 False
## 228 2 2.83 False
## 229 2 3.62 False
## 230 2 1.39 False
## 231 1 3.10 False
## 232 1 2.06 False
## 233 1 1.45 False
## 234 0 4.13 False
## 235 2 2.65 False
## 236 2 4.33 False
## 237 2 3.51 False
## 238 0 1.96 False
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## 240 2 1.53 False
## 241 0 2.18 True
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## 282 0 3.06 True
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## 286 1 1.65 True
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## 307 1 2.97 True
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## 4837 1 1.55 False
## 4838 0 1.83 False
## 4839 2 4.26 True
## 4840 0 4.29 False
## 4841 0 4.11 False
## 4842 1 2.35 False
## 4843 2 2.82 False
## 4844 0 2.19 False
## 4845 1 4.40 True
## 4846 2 4.04 True
## 4847 1 1.87 False
## 4848 1 1.84 False
## 4849 0 2.44 False
## 4850 2 1.68 False
## 4851 2 3.41 False
## 4852 2 1.72 False
## 4853 1 4.17 False
## 4854 0 1.12 False
## 4855 0 2.95 False
## 4856 2 2.60 True
## 4857 0 2.98 False
## 4858 0 2.76 False
## 4859 1 1.75 False
## 4860 1 1.59 False
## 4861 2 1.15 False
## 4862 2 1.32 False
## 4863 2 1.61 False
## 4864 1 1.36 False
## 4865 2 2.09 False
## 4866 1 4.91 False
## 4867 2 3.01 True
## 4868 2 4.62 False
## 4869 0 3.48 False
## 4870 0 4.26 False
## 4871 1 1.82 False
## 4872 2 2.37 False
## 4873 1 3.94 False
## 4874 0 3.47 False
## 4875 0 4.06 False
## 4876 1 3.40 False
## 4877 1 4.65 False
## 4878 1 2.57 False
## 4879 2 2.98 False
## 4880 1 4.93 False
## 4881 1 2.60 False
## 4882 0 3.29 False
## 4883 1 1.10 False
## 4884 2 4.72 True
## 4885 1 3.93 False
## 4886 0 4.35 False
## 4887 2 4.21 False
## 4888 1 2.76 False
## 4889 2 1.43 False
## 4890 1 1.62 True
## 4891 1 1.37 False
## 4892 1 3.29 False
## 4893 2 2.00 False
## 4894 1 3.49 False
## 4895 1 1.01 False
## 4896 0 4.30 False
## 4897 0 1.08 False
## 4898 2 1.85 False
## 4899 0 1.97 False
## 4900 0 2.45 False
## 4901 0 1.32 False
## 4902 1 2.61 False
## 4903 2 4.84 False
## 4904 0 2.65 False
## 4905 2 2.00 False
## 4906 0 2.68 False
## 4907 0 3.62 False
## 4908 0 4.44 False
## 4909 0 3.89 False
## 4910 0 1.01 False
## 4911 0 4.33 False
## 4912 1 3.73 False
## 4913 0 1.44 False
## 4914 0 1.97 False
## 4915 0 4.03 True
## 4916 1 1.03 False
## 4917 1 1.98 False
## 4918 0 3.72 True
## 4919 2 3.60 False
## 4920 1 3.41 False
## 4921 2 4.43 False
## 4922 2 3.35 False
## 4923 1 1.03 False
## 4924 1 4.15 False
## 4925 2 1.78 False
## 4926 0 2.80 False
## 4927 0 3.56 False
## 4928 2 2.43 False
## 4929 0 4.35 False
## 4930 0 2.42 False
## 4931 0 1.11 False
## 4932 2 4.76 False
## 4933 2 4.83 True
## 4934 1 2.85 False
## 4935 2 4.99 True
## 4936 2 4.68 False
## 4937 1 1.97 False
## 4938 1 3.38 False
## 4939 0 3.35 True
## 4940 2 1.39 False
## 4941 2 4.99 False
## 4942 1 1.97 False
## 4943 0 1.68 False
## 4944 2 4.70 True
## 4945 0 1.75 False
## 4946 2 3.48 False
## 4947 0 2.28 False
## 4948 1 4.36 False
## 4949 2 3.41 False
## 4950 1 1.32 False
## 4951 0 4.13 False
## 4952 0 2.95 False
## 4953 2 3.58 False
## 4954 0 4.45 False
## 4955 2 3.00 False
## 4956 2 3.07 True
## 4957 1 2.37 False
## 4958 1 1.81 False
## 4959 0 2.90 False
## 4960 2 2.32 False
## 4961 2 1.96 False
## 4962 1 2.98 False
## 4963 0 3.21 False
## 4964 2 4.77 False
## 4965 1 2.91 False
## 4966 0 1.00 False
## 4967 1 4.40 True
## 4968 0 3.95 False
## 4969 0 2.03 False
## 4970 2 2.70 False
## 4971 1 2.73 False
## 4972 0 3.53 False
## 4973 0 2.44 True
## 4974 2 3.51 False
## 4975 0 1.36 False
## 4976 0 1.10 True
## 4977 2 4.61 False
## 4978 1 2.42 False
## 4979 1 3.68 False
## 4980 0 2.53 True
## 4981 2 3.41 False
## 4982 2 4.15 True
## 4983 0 4.10 False
## 4984 2 3.47 False
## 4985 2 4.98 True
## 4986 1 3.21 False
## 4987 2 4.67 False
## 4988 0 2.69 False
## 4989 0 2.73 False
## 4990 1 3.77 False
## 4991 1 4.06 False
## 4992 2 3.83 False
## 4993 1 4.01 False
## 4994 0 4.38 False
## 4995 2 4.93 False
## 4996 1 2.18 False
## 4997 1 4.24 False
## 4998 1 2.99 True
## 4999 2 1.68 False
## [ reached 'max' / getOption("max.print") -- omitted 95001 rows ]# Tambah kolom Marital_Status secara acak
set.seed(123) # agar hasil random konsisten
status_options <- c("Single", "Married", "Divorced")
data$Marital_Status <- sample(status_options, size = nrow(data), replace = TRUE)
# Lihat hasil
head(data)## Employee_ID Department Gender Age Job_Title Hire_Date
## 1 1 IT Male 55 Specialist 2022-01-19 08:03:05.556036
## 2 2 Finance Male 29 Developer 2024-04-18 08:03:05.556036
## 3 3 Finance Male 55 Specialist 2015-10-26 08:03:05.556036
## 4 4 Customer Support Female 48 Analyst 2016-10-22 08:03:05.556036
## 5 5 Engineering Female 36 Analyst 2021-07-23 08:03:05.556036
## 6 6 IT Male 43 Manager 2016-08-14 08:03:05.556036
## Years_At_Company Education_Level Performance_Score Monthly_Salary
## 1 2 High School 5 6750
## 2 0 High School 5 7500
## 3 8 High School 3 5850
## 4 7 Bachelor 2 4800
## 5 3 Bachelor 2 4800
## 6 8 High School 3 7800
## Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days
## 1 33 32 22 2
## 2 34 34 13 14
## 3 37 27 6 3
## 4 52 10 28 12
## 5 38 11 29 13
## 6 46 31 8 0
## Remote_Work_Frequency Team_Size Training_Hours Promotions
## 1 0 14 66 0
## 2 100 12 61 2
## 3 50 10 1 0
## 4 100 10 0 1
## 5 100 15 9 1
## 6 100 15 95 0
## Employee_Satisfaction_Score Resigned Marital_Status
## 1 2.63 False Divorced
## 2 1.72 False Divorced
## 3 3.17 False Divorced
## 4 1.86 False Married
## 5 1.25 False Divorced
## 6 2.77 False Married# Simpan dataset ke file baru CSV
write.csv(data,"C:/Users/Shellomitha Sulvana/Downloads/Employee_Data_with_MaritalStatus.csv", row.names = FALSE)
data <- read.csv("C:/Users/Shellomitha Sulvana/Downloads/Employee_Data_with_MaritalStatus.csv", stringsAsFactors = FALSE)
#Lihat struktur dan ringkasan data
str(data)## 'data.frame': 100000 obs. of 21 variables:
## $ Employee_ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Department : chr "IT" "Finance" "Finance" "Customer Support" ...
## $ Gender : chr "Male" "Male" "Male" "Female" ...
## $ Age : int 55 29 55 48 36 43 37 55 55 45 ...
## $ Job_Title : chr "Specialist" "Developer" "Specialist" "Analyst" ...
## $ Hire_Date : chr "2022-01-19 08:03:05.556036" "2024-04-18 08:03:05.556036" "2015-10-26 08:03:05.556036" "2016-10-22 08:03:05.556036" ...
## $ Years_At_Company : int 2 0 8 7 3 8 1 9 1 7 ...
## $ Education_Level : chr "High School" "High School" "High School" "Bachelor" ...
## $ Performance_Score : int 5 5 3 2 2 3 5 2 2 1 ...
## $ Monthly_Salary : int 6750 7500 5850 4800 4800 7800 5250 7200 4200 6050 ...
## $ Work_Hours_Per_Week : int 33 34 37 52 38 46 55 42 51 41 ...
## $ Projects_Handled : int 32 34 27 10 11 31 20 46 23 33 ...
## $ Overtime_Hours : int 22 13 6 28 29 8 29 7 21 2 ...
## $ Sick_Days : int 2 14 3 12 13 0 2 8 14 6 ...
## $ Remote_Work_Frequency : int 0 100 50 100 100 100 0 100 0 75 ...
## $ Team_Size : int 14 12 10 10 15 15 16 7 1 4 ...
## $ Training_Hours : int 66 61 1 0 9 95 27 64 0 53 ...
## $ Promotions : int 0 2 0 1 1 0 0 0 1 2 ...
## $ Employee_Satisfaction_Score: num 2.63 1.72 3.17 1.86 1.25 2.77 4.46 2.09 1.44 2.93 ...
## $ Resigned : chr "False" "False" "False" "False" ...
## $ Marital_Status : chr "Divorced" "Divorced" "Divorced" "Married" ...summary(data)## Employee_ID Department Gender Age
## Min. : 1 Length:100000 Length:100000 Min. :22.00
## 1st Qu.: 25001 Class :character Class :character 1st Qu.:31.00
## Median : 50001 Mode :character Mode :character Median :41.00
## Mean : 50001 Mean :41.03
## 3rd Qu.: 75000 3rd Qu.:51.00
## Max. :100000 Max. :60.00
## Job_Title Hire_Date Years_At_Company Education_Level
## Length:100000 Length:100000 Min. : 0.000 Length:100000
## Class :character Class :character 1st Qu.: 2.000 Class :character
## Mode :character Mode :character Median : 4.000 Mode :character
## Mean : 4.476
## 3rd Qu.: 7.000
## Max. :10.000
## Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled
## Min. :1.000 Min. :3850 Min. :30.00 Min. : 0.00
## 1st Qu.:2.000 1st Qu.:5250 1st Qu.:37.00 1st Qu.:12.00
## Median :3.000 Median :6500 Median :45.00 Median :24.00
## Mean :2.995 Mean :6403 Mean :44.96 Mean :24.43
## 3rd Qu.:4.000 3rd Qu.:7500 3rd Qu.:53.00 3rd Qu.:37.00
## Max. :5.000 Max. :9000 Max. :60.00 Max. :49.00
## Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 1.00
## 1st Qu.: 7.00 1st Qu.: 3.000 1st Qu.: 25.00 1st Qu.: 5.00
## Median :15.00 Median : 7.000 Median : 50.00 Median :10.00
## Mean :14.51 Mean : 7.009 Mean : 50.09 Mean :10.01
## 3rd Qu.:22.00 3rd Qu.:11.000 3rd Qu.: 75.00 3rd Qu.:15.00
## Max. :29.00 Max. :14.000 Max. :100.00 Max. :19.00
## Training_Hours Promotions Employee_Satisfaction_Score
## Min. : 0.00 Min. :0.0000 Min. :1.000
## 1st Qu.:25.00 1st Qu.:0.0000 1st Qu.:2.010
## Median :49.00 Median :1.0000 Median :3.000
## Mean :49.51 Mean :0.9997 Mean :2.999
## 3rd Qu.:75.00 3rd Qu.:2.0000 3rd Qu.:3.990
## Max. :99.00 Max. :2.0000 Max. :5.000
## Resigned Marital_Status
## Length:100000 Length:100000
## Class :character Class :character
## Mode :character Mode :character
##
##
## ———-PREPROCESSING———-
# 1.Cek missing values per kolom
colSums(is.na(data))## Employee_ID Department
## 0 0
## Gender Age
## 0 0
## Job_Title Hire_Date
## 0 0
## Years_At_Company Education_Level
## 0 0
## Performance_Score Monthly_Salary
## 0 0
## Work_Hours_Per_Week Projects_Handled
## 0 0
## Overtime_Hours Sick_Days
## 0 0
## Remote_Work_Frequency Team_Size
## 0 0
## Training_Hours Promotions
## 0 0
## Employee_Satisfaction_Score Resigned
## 0 0
## Marital_Status
## 0# 2. Encoding variabel kategorik
# Mengubah variabel kategorik jadi faktor (encoding kategorik)
data$Job_Title <- as.factor(data$Job_Title)
data$Department <- as.factor(data$Department)
data$Gender <- as.factor(data$Gender)
data$Education_Level <- as.factor(data$Education_Level)
data$ Marital_Status<- as.factor(data$Marital_Status)
# 3. Normalisasi variabel numerik yang penting
num_cols <- c("Monthly_Salary", "Employee_Satisfaction_Score", "Age", "Years_At_Company")
data[num_cols] <- scale(data[num_cols])———-EKSPLORASI EDA BESERTA VISUALISASI———-
# Load library yang dibutuhkan
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.4.3library(reshape2)## Warning: package 'reshape2' was built under R version 4.4.3library(psych) # buat deskriptif dan uji normalitas## Warning: package 'psych' was built under R version 4.4.3##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(corrplot) # buat visualisasi korelasi## Warning: package 'corrplot' was built under R version 4.4.3## corrplot 0.95 loaded# 1. Statistik deskriptif
describe(data[num_cols])## vars n mean sd median trimmed mad min max
## Monthly_Salary 1 1e+05 0 1 0.07 -0.01 1.19 -1.86 1.89
## Employee_Satisfaction_Score 2 1e+05 0 1 0.00 0.00 1.28 -1.74 1.74
## Age 3 1e+05 0 1 0.00 0.00 1.32 -1.69 1.69
## Years_At_Company 4 1e+05 0 1 -0.17 0.00 1.03 -1.56 1.93
## range skew kurtosis se
## Monthly_Salary 3.75 0.10 -0.88 0
## Employee_Satisfaction_Score 3.48 0.00 -1.19 0
## Age 3.38 -0.01 -1.20 0
## Years_At_Company 3.49 0.01 -1.22 0summary(data[, c("Performance_Score", "Employee_Satisfaction_Score", "Age", "Years_At_Company", "Monthly_Salary")])## Performance_Score Employee_Satisfaction_Score Age
## Min. :1.000 Min. :-1.7372510 Min. :-1.692388
## 1st Qu.:2.000 1st Qu.:-0.8595392 1st Qu.:-0.891969
## Median :3.000 Median : 0.0007922 Median :-0.002616
## Mean :2.995 Mean : 0.0000000 Mean : 0.000000
## 3rd Qu.:4.000 3rd Qu.: 0.8611236 3rd Qu.: 0.886738
## Max. :5.000 Max. : 1.7388354 Max. : 1.687156
## Years_At_Company Monthly_Salary
## Min. :-1.5600 Min. :-1.86025
## 1st Qu.:-0.8629 1st Qu.:-0.84022
## Median :-0.1659 Median : 0.07052
## Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.8796 3rd Qu.: 0.79911
## Max. : 1.9252 Max. : 1.89200# Jumlah dan proporsi kategori
table(data$Gender)##
## Female Male Other
## 48001 48031 3968prop.table(table(data$Marital_Status))##
## Divorced Married Single
## 0.33294 0.33305 0.33401# 2. Uji normalitas dengan Shapiro-Wilk (untuk sample kecil, coba 500 baris)
shapiro.test(data$Monthly_Salary[1:500])##
## Shapiro-Wilk normality test
##
## data: data$Monthly_Salary[1:500]
## W = 0.97062, p-value = 1.825e-08shapiro.test(data$Employee_Satisfaction_Score[1:500])##
## Shapiro-Wilk normality test
##
## data: data$Employee_Satisfaction_Score[1:500]
## W = 0.95419, p-value = 2.442e-11shapiro.test(data$Age[1:500])##
## Shapiro-Wilk normality test
##
## data: data$Age[1:500]
## W = 0.95513, p-value = 3.403e-11shapiro.test(data$Years_At_Company[1:500])##
## Shapiro-Wilk normality test
##
## data: data$Years_At_Company[1:500]
## W = 0.94204, p-value = 4.667e-13# 3. Visualisasi Boxplot untuk outlier
for (col in num_cols) {
print(
ggplot(data, aes_string(y = col)) +
geom_boxplot(fill="skyblue") +
ggtitle(paste("Boxplot of", col))
)
}## 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.
# 4. Histogram untuk distribusi data
for (col in num_cols) {
print(
ggplot(data, aes_string(x = col)) +
geom_histogram(bins=30, fill="lightgreen", color="black") +
ggtitle(paste("Histogram of", col))
)
}
# 5. Korelasi antar variabel numerik dan plot korelasi
cor_matrix <- cor(data[num_cols], use="complete.obs")
print(cor_matrix)## Monthly_Salary Employee_Satisfaction_Score
## Monthly_Salary 1.0000000000 0.0010827334
## Employee_Satisfaction_Score 0.0010827334 1.0000000000
## Age 0.0027567514 -0.0001242389
## Years_At_Company -0.0006446329 -0.0031801797
## Age Years_At_Company
## Monthly_Salary 2.756751e-03 -6.446329e-04
## Employee_Satisfaction_Score -1.242389e-04 -3.180180e-03
## Age 1.000000e+00 7.807067e-05
## Years_At_Company 7.807067e-05 1.000000e+00corrplot(cor_matrix, method = "color", addCoef.col = "black", number.cex=0.8)
———-Uji Asumsi MANOVA dan MANCOVA———-
# install.packages("MVN")
# install.packages("biotools")
library(MVN)## Warning: package 'MVN' was built under R version 4.4.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(car)## Warning: package 'car' was built under R version 4.4.3## Loading required package: carData## Warning: package 'carData' was built under R version 4.4.3##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:psych':
##
## logitlibrary(biotools)## Warning: package 'biotools' was built under R version 4.4.3## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## ---
## biotools version 4.3set.seed(123)
data_sample <- data %>% sample_n(1000)
# Uji normalitas untuk variabel dependen(y)
mvn(data = data_sample[, c("Monthly_Salary", "Employee_Satisfaction_Score")], multivariatePlot = "qq")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 7.40563 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling Monthly_Salary 6.3440 <0.001 NO
## 2 Anderson-Darling Employee_Satisfaction_Score 11.7781 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min
## Monthly_Salary 1000 0.028079239 1.018747 0.14337905 -1.860251
## Employee_Satisfaction_Score 1000 0.009612769 1.026405 -0.02527845 -1.737251
## Max 25th 75th Skew Kurtosis
## Monthly_Salary 1.892002 -0.8402213 0.7991126 0.06847576 -0.9013698
## Employee_Satisfaction_Score 1.738835 -0.8943000 0.9328178 0.03102002 -1.2125339# uji Multikolinearitas & Korelasi
cor(data_sample[, c("Monthly_Salary", "Employee_Satisfaction_Score")]) ## Monthly_Salary Employee_Satisfaction_Score
## Monthly_Salary 1.00000000 -0.01189362
## Employee_Satisfaction_Score -0.01189362 1.00000000# uji Homegenitas
boxM(data = data_sample[, c("Monthly_Salary", "Employee_Satisfaction_Score")], grouping = data_sample$Gender)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data_sample[, c("Monthly_Salary", "Employee_Satisfaction_Score")]
## Chi-Sq (approx.) = 4.0848, df = 6, p-value = 0.6652———-PENERAPAN MANOVA———-
# Pastikan Department sebagai faktor
data$Job_Title <- as.factor(data$Job_Title)
data$Department <- as.factor(data$Department)
data$Gender <- as.factor(data$Gender)
data$Education_Level <- as.factor(data$Education_Level)
data$ Marital_Status<- as.factor(data$Marital_Status)
# MANOVA dengan 3 DV: Performance_Score, Employee_Satisfaction_Score, Monthly_Salary
manova_model <- manova(cbind(Employee_Satisfaction_Score, Monthly_Salary) ~ Department + Job_Title + Gender + Education_Level + Marital_Status , data = data)
# Ringkasan hasil MANOVA (uji Pillai)
summary(manova_model, test = "Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## Department 8 0.00051 3.2 16 199956 1.587e-05 ***
## Job_Title 6 0.73332 9646.7 12 199956 < 2.2e-16 ***
## Gender 2 0.00003 0.9 4 199956 0.4923
## Education_Level 3 0.00008 1.4 6 199956 0.2237
## Marital_Status 2 0.00002 0.6 4 199956 0.6684
## Residuals 99978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Analisis varians univariat untuk masing-masing DV
summary.aov(manova_model)## Response Employee_Satisfaction_Score :
## Df Sum Sq Mean Sq F value Pr(>F)
## Department 8 22 2.71027 2.7105 0.00554 **
## Job_Title 6 3 0.57659 0.5766 0.74930
## Gender 2 2 1.12732 1.1274 0.32387
## Education_Level 3 1 0.39323 0.3933 0.75785
## Marital_Status 2 1 0.38402 0.3841 0.68110
## Residuals 99978 99970 0.99992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Monthly_Salary :
## Df Sum Sq Mean Sq F value Pr(>F)
## Department 8 8 1.0 3.6704 0.0002741 ***
## Job_Title 6 73320 12220.0 45811.9130 < 2.2e-16 ***
## Gender 2 0 0.2 0.5726 0.5640354
## Education_Level 3 2 0.6 2.3391 0.0713578 .
## Marital_Status 2 0 0.2 0.8017 0.4485741
## Residuals 99978 26668 0.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1———-MANCOVA———-
# Pastikan Department faktor
data$Department <- as.factor(data$Department)
data$Job_Title <- as.factor(data$Job_Title)
data$Gender <- as.factor(data$Gender)
data$Education_Level <- as.factor(data$Education_Level)
data$ Marital_Status<- as.factor(data$Marital_Status)
# MANCOVA dengan DV: Employee_Satisfaction_Score & Monthly_Salary
mancova_model <- manova(cbind(Employee_Satisfaction_Score, Monthly_Salary) ~ Department + Job_Title + Gender + Education_Level + Marital_Status + Years_At_Company + Age, data = data)
# Ringkasan hasil MANCOVA (uji Pillai)
summary(mancova_model, test = "Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## Department 8 0.00051 3.2 16 199952 1.587e-05 ***
## Job_Title 6 0.73332 9646.6 12 199952 < 2.2e-16 ***
## Gender 2 0.00003 0.9 4 199952 0.4923
## Education_Level 3 0.00008 1.4 6 199952 0.2237
## Marital_Status 2 0.00002 0.6 4 199952 0.6684
## Years_At_Company 1 0.00001 0.6 2 99975 0.5378
## Age 1 0.00000 0.2 2 99975 0.8502
## Residuals 99976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Analisis varians univariat untuk masing-masing DV
summary.aov(mancova_model)## Response Employee_Satisfaction_Score :
## Df Sum Sq Mean Sq F value Pr(>F)
## Department 8 22 2.71027 2.7105 0.00554 **
## Job_Title 6 3 0.57659 0.5766 0.74931
## Gender 2 2 1.12732 1.1274 0.32388
## Education_Level 3 1 0.39323 0.3933 0.75786
## Marital_Status 2 1 0.38402 0.3840 0.68110
## Years_At_Company 1 1 0.99174 0.9918 0.31930
## Age 1 0 0.00498 0.0050 0.94373
## Residuals 99976 99969 0.99993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Monthly_Salary :
## Df Sum Sq Mean Sq F value Pr(>F)
## Department 8 8 1.0 3.6704 0.0002742 ***
## Job_Title 6 73320 12220.0 45811.2561 < 2.2e-16 ***
## Gender 2 0 0.2 0.5726 0.5640400
## Education_Level 3 2 0.6 2.3390 0.0713610 .
## Marital_Status 2 0 0.2 0.8017 0.4485792
## Years_At_Company 1 0 0.1 0.2468 0.6193270
## Age 1 0 0.1 0.3195 0.5719218
## Residuals 99976 26668 0.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Dari model yang sudah dibuat, tampilkan ringkasan lengkap dengan berbagai uji statistik
summary(manova_model, test = c("Pillai", "Wilks", "Hotelling-Lawley", "Roy"))## Df Pillai approx F num Df den Df Pr(>F)
## Department 8 0.00051 3.2 16 199956 1.587e-05 ***
## Job_Title 6 0.73332 9646.7 12 199956 < 2.2e-16 ***
## Gender 2 0.00003 0.9 4 199956 0.4923
## Education_Level 3 0.00008 1.4 6 199956 0.2237
## Marital_Status 2 0.00002 0.6 4 199956 0.6684
## Residuals 99978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mancova_model, test = c("Pillai", "Wilks", "Hotelling-Lawley", "Roy"))## Df Pillai approx F num Df den Df Pr(>F)
## Department 8 0.00051 3.2 16 199952 1.587e-05 ***
## Job_Title 6 0.73332 9646.6 12 199952 < 2.2e-16 ***
## Gender 2 0.00003 0.9 4 199952 0.4923
## Education_Level 3 0.00008 1.4 6 199952 0.2237
## Marital_Status 2 0.00002 0.6 4 199952 0.6684
## Years_At_Company 1 0.00001 0.6 2 99975 0.5378
## Age 1 0.00000 0.2 2 99975 0.8502
## Residuals 99976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1