------------------------------------------------------------------------------------------------------------- log: C:\AAA Miker Files\current class files\methods tabular arrays\class 8_2002.log log type: text opened on: 23 Oct 2002, 13:13:10 . *I'm going to start out by using my cps individual level dataset, and contracting it. . set mem 20m Current memory allocation current memory usage settable value description (1M = 1024k) -------------------------------------------------------------------- set maxvar 5000 max. variables allowed 1.733M set memory 20M max. data space 20.000M set matsize 400 max. RHS vars in models 1.254M ----------- 22.987M . use "C:\AAA Miker Files\Archived old class files\Intro Soc. Methods Class\Data and info for project 3\cps y > 2k full data (all numeric) newer.dta", clear . describe Contains data from C:\AAA Miker Files\Archived old class files\Intro Soc. Methods Class\Data and info for pro > ject 3\cps y2k full data (all numeric) newer.dta obs: 133,710 vars: 39 30 May 2001 12:57 size: 9,493,410 (54.7% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- phseq str5 %9s household sequence number p2 pernum byte %8.0g age byte %8.0g p15 maritl byte %26.0g marlbl Marital Status p17 sex byte %8.0g sexnm p20 vet byte %22.0g vetnm veteran status p21 hga byte %8.0g Educational Attainment p22 race byte %11.0g racenm p25 reorigin byte %8.0g Hispanic Origin p27 hrs1 byte %8.0g hours worked last week p76 clswkr byte %32.0g cwrknm sector of worker p109 grswk int %9.0g gross weekly wages p135 unmem byte %13.0g unnm labor union member p139 lfsr byte %28.0g lfsrnm labor force status p145 ernval float %9.0g main job last year earnings p228 ssval long %12.0g last year soc security payments p291 pawval int %12.0g last year welfare payments p305 wgt2 int %9.0g rounded weight based on p50 ernval2 float %9.0g main job earnings, losses recoded to zero htype byte %37.0g htpnm household type h25 state byte %8.0g HG-ST60, or simply state of residence h40 hpmsasz byte %8.0g metropolitan area size h56 hcccr byte %8.0g residence in central city h58 frelu18 byte %8.0g number of kids in fam under 18 f29 povll byte %8.0g ratio of fam income to poverty level f38 fwsval float %9.0g family income f48 famwgt2 int %8.0g adjusted family weight f233 yrsed float %9.0g years of education, from hga citizen byte %33.0g citnm citizenship p733 health byte %11.0g hlthnm self reported health status p800 occ int %8.0g occupation P 106 ptotr byte %8.0g total person income categories P466 penatvty int %8.0g country of birth P 722, Appendix H pemntvty int %8.0g Mother's country of birth, P725, appendix H pefntvty int %8.0g Father's country of birth, P728, appendix H peinusyr byte %8.0g time of immigration, P 731 pxnatvty byte %8.0g allocation flag for country of birth P 734 hgmsac int %8.0g metropolitan area code, h44, appendix E pppos2 byte %8.0g family sequence number within each household p46 ------------------------------------------------------------------------------- Sorted by: race . *let's take a closer look at race by occupation . tabulate occ occupation | P 106 | Freq. Percent Cum. ------------+----------------------------------- 0 | 64975 48.59 48.59 4 | 14 0.01 48.60 5 | 339 0.25 48.86 6 | 31 0.02 48.88 7 | 362 0.27 49.15 8 | 125 0.09 49.25 9 | 49 0.04 49.28 13 | 361 0.27 49.55 14 | 443 0.33 49.88 15 | 374 0.28 50.16 17 | 747 0.56 50.72 18 | 279 0.21 50.93 19 | 21 0.02 50.95 21 | 378 0.28 51.23 22 | 3696 2.76 53.99 23 | 730 0.55 54.54 24 | 47 0.04 54.57 25 | 405 0.30 54.88 26 | 220 0.16 55.04 27 | 280 0.21 55.25 28 | 8 0.01 55.26 29 | 109 0.08 55.34 33 | 131 0.10 55.44 34 | 18 0.01 55.45 35 | 43 0.03 55.48 36 | 150 0.11 55.59 37 | 237 0.18 55.77 43 | 90 0.07 55.84 44 | 33 0.02 55.86 45 | 18 0.01 55.88 46 | 3 0.00 55.88 47 | 10 0.01 55.89 48 | 34 0.03 55.91 49 | 7 0.01 55.92 53 | 146 0.11 56.03 54 | 2 0.00 56.03 55 | 308 0.23 56.26 56 | 113 0.08 56.34 57 | 163 0.12 56.46 58 | 6 0.00 56.47 59 | 112 0.08 56.55 63 | 11 0.01 56.56 64 | 737 0.55 57.11 65 | 92 0.07 57.18 66 | 7 0.01 57.19 67 | 11 0.01 57.19 68 | 2 0.00 57.20 69 | 8 0.01 57.20 73 | 57 0.04 57.24 74 | 7 0.01 57.25 75 | 23 0.02 57.27 76 | 25 0.02 57.29 77 | 29 0.02 57.31 78 | 60 0.04 57.35 79 | 19 0.01 57.37 83 | 27 0.02 57.39 84 | 346 0.26 57.65 85 | 79 0.06 57.70 86 | 24 0.02 57.72 87 | 16 0.01 57.73 88 | 6 0.00 57.74 89 | 25 0.02 57.76 95 | 966 0.72 58.48 96 | 113 0.08 58.56 97 | 44 0.03 58.60 98 | 35 0.03 58.62 99 | 26 0.02 58.64 103 | 66 0.05 58.69 104 | 39 0.03 58.72 105 | 38 0.03 58.75 106 | 26 0.02 58.77 113 | 6 0.00 58.77 114 | 16 0.01 58.79 115 | 12 0.01 58.80 116 | 9 0.01 58.80 117 | 3 0.00 58.80 118 | 15 0.01 58.82 119 | 9 0.01 58.82 123 | 10 0.01 58.83 124 | 7 0.01 58.83 125 | 6 0.00 58.84 127 | 14 0.01 58.85 128 | 25 0.02 58.87 129 | 11 0.01 58.88 133 | 6 0.00 58.88 134 | 26 0.02 58.90 135 | 8 0.01 58.91 136 | 4 0.00 58.91 137 | 17 0.01 58.92 138 | 7 0.01 58.93 139 | 9 0.01 58.93 143 | 33 0.02 58.96 144 | 15 0.01 58.97 145 | 6 0.00 58.97 146 | 1 0.00 58.98 147 | 8 0.01 58.98 148 | 1 0.00 58.98 153 | 4 0.00 58.99 154 | 226 0.17 59.15 155 | 301 0.23 59.38 156 | 1065 0.80 60.18 157 | 679 0.51 60.68 158 | 190 0.14 60.83 159 | 450 0.34 61.16 163 | 127 0.09 61.26 164 | 138 0.10 61.36 165 | 25 0.02 61.38 166 | 57 0.04 61.42 167 | 122 0.09 61.51 169 | 20 0.01 61.53 173 | 15 0.01 61.54 174 | 412 0.31 61.85 175 | 59 0.04 61.89 176 | 171 0.13 62.02 177 | 74 0.06 62.07 178 | 441 0.33 62.40 183 | 69 0.05 62.46 184 | 27 0.02 62.48 185 | 318 0.24 62.71 186 | 71 0.05 62.77 187 | 49 0.04 62.80 188 | 104 0.08 62.88 189 | 78 0.06 62.94 193 | 24 0.02 62.96 194 | 83 0.06 63.02 195 | 167 0.12 63.14 197 | 89 0.07 63.21 198 | 32 0.02 63.24 199 | 57 0.04 63.28 203 | 164 0.12 63.40 204 | 52 0.04 63.44 205 | 11 0.01 63.45 206 | 67 0.05 63.50 207 | 197 0.15 63.65 208 | 358 0.27 63.91 213 | 246 0.18 64.10 214 | 4 0.00 64.10 215 | 6 0.00 64.10 216 | 108 0.08 64.19 217 | 91 0.07 64.25 218 | 36 0.03 64.28 223 | 57 0.04 64.32 224 | 41 0.03 64.35 225 | 38 0.03 64.38 226 | 60 0.04 64.43 227 | 14 0.01 64.44 228 | 16 0.01 64.45 229 | 308 0.23 64.68 233 | 3 0.00 64.68 234 | 181 0.14 64.82 235 | 51 0.04 64.86 243 | 2388 1.79 66.64 253 | 263 0.20 66.84 254 | 382 0.29 67.12 255 | 277 0.21 67.33 256 | 78 0.06 67.39 257 | 336 0.25 67.64 258 | 10 0.01 67.65 259 | 721 0.54 68.19 263 | 155 0.12 68.30 264 | 233 0.17 68.48 265 | 49 0.04 68.51 266 | 81 0.06 68.57 267 | 125 0.09 68.67 268 | 150 0.11 68.78 269 | 97 0.07 68.85 274 | 739 0.55 69.41 275 | 105 0.08 69.48 276 | 1654 1.24 70.72 277 | 166 0.12 70.85 278 | 66 0.05 70.89 283 | 41 0.03 70.93 284 | 10 0.01 70.93 285 | 9 0.01 70.94 303 | 175 0.13 71.07 304 | 8 0.01 71.08 305 | 35 0.03 71.10 307 | 115 0.09 71.19 308 | 156 0.12 71.31 309 | 1 0.00 71.31 313 | 1420 1.06 72.37 314 | 79 0.06 72.43 315 | 267 0.20 72.63 316 | 119 0.09 72.72 317 | 64 0.05 72.76 318 | 147 0.11 72.87 319 | 550 0.41 73.28 323 | 229 0.17 73.46 325 | 3 0.00 73.46 326 | 7 0.01 73.46 327 | 134 0.10 73.56 328 | 48 0.04 73.60 329 | 88 0.07 73.67 335 | 174 0.13 73.80 336 | 112 0.08 73.88 337 | 891 0.67 74.55 338 | 81 0.06 74.61 339 | 94 0.07 74.68 343 | 30 0.02 74.70 344 | 57 0.04 74.74 345 | 21 0.02 74.76 346 | 7 0.01 74.76 347 | 11 0.01 74.77 348 | 78 0.06 74.83 353 | 6 0.00 74.83 354 | 162 0.12 74.95 355 | 163 0.12 75.08 356 | 89 0.07 75.14 357 | 73 0.05 75.20 359 | 135 0.10 75.30 363 | 112 0.08 75.38 364 | 347 0.26 75.64 365 | 228 0.17 75.81 366 | 17 0.01 75.83 368 | 34 0.03 75.85 373 | 144 0.11 75.96 374 | 10 0.01 75.97 375 | 214 0.16 76.13 376 | 523 0.39 76.52 377 | 57 0.04 76.56 378 | 85 0.06 76.62 379 | 425 0.32 76.94 383 | 213 0.16 77.10 384 | 9 0.01 77.11 385 | 375 0.28 77.39 386 | 52 0.04 77.43 387 | 407 0.30 77.73 389 | 488 0.36 78.10 403 | 1 0.00 78.10 404 | 2 0.00 78.10 405 | 6 0.00 78.10 406 | 186 0.14 78.24 407 | 321 0.24 78.48 413 | 14 0.01 78.49 414 | 54 0.04 78.53 415 | 27 0.02 78.55 416 | 10 0.01 78.56 417 | 114 0.09 78.65 418 | 286 0.21 78.86 423 | 69 0.05 78.91 424 | 140 0.10 79.02 425 | 16 0.01 79.03 426 | 388 0.29 79.32 427 | 40 0.03 79.35 433 | 235 0.18 79.52 434 | 198 0.15 79.67 435 | 793 0.59 80.26 436 | 1238 0.93 81.19 438 | 199 0.15 81.34 439 | 173 0.13 81.47 443 | 339 0.25 81.72 444 | 444 0.33 82.05 445 | 126 0.09 82.15 446 | 196 0.15 82.30 447 | 1033 0.77 83.07 448 | 89 0.07 83.13 449 | 436 0.33 83.46 453 | 1309 0.98 84.44 454 | 3 0.00 84.44 455 | 43 0.03 84.47 456 | 76 0.06 84.53 457 | 49 0.04 84.57 458 | 393 0.29 84.86 459 | 134 0.10 84.96 461 | 31 0.02 84.98 462 | 18 0.01 85.00 463 | 61 0.05 85.04 464 | 25 0.02 85.06 465 | 57 0.04 85.11 466 | 272 0.20 85.31 467 | 269 0.20 85.51 468 | 139 0.10 85.61 469 | 145 0.11 85.72 473 | 548 0.41 86.13 474 | 25 0.02 86.15 475 | 80 0.06 86.21 476 | 10 0.01 86.22 477 | 22 0.02 86.23 479 | 536 0.40 86.64 483 | 1 0.00 86.64 484 | 27 0.02 86.66 485 | 85 0.06 86.72 486 | 449 0.34 87.06 487 | 78 0.06 87.11 488 | 52 0.04 87.15 489 | 5 0.00 87.16 494 | 9 0.01 87.16 495 | 13 0.01 87.17 496 | 42 0.03 87.20 497 | 5 0.00 87.21 498 | 38 0.03 87.24 499 | 2 0.00 87.24 503 | 105 0.08 87.32 505 | 429 0.32 87.64 506 | 1 0.00 87.64 507 | 194 0.15 87.78 508 | 54 0.04 87.82 509 | 24 0.02 87.84 514 | 99 0.07 87.92 515 | 5 0.00 87.92 516 | 80 0.06 87.98 517 | 11 0.01 87.99 518 | 252 0.19 88.18 519 | 7 0.01 88.18 523 | 92 0.07 88.25 525 | 146 0.11 88.36 526 | 15 0.01 88.37 527 | 37 0.03 88.40 529 | 131 0.10 88.50 533 | 35 0.03 88.52 534 | 169 0.13 88.65 535 | 17 0.01 88.66 536 | 25 0.02 88.68 538 | 32 0.02 88.70 539 | 9 0.01 88.71 543 | 12 0.01 88.72 544 | 45 0.03 88.75 547 | 208 0.16 88.91 549 | 126 0.09 89.00 554 | 18 0.01 89.02 555 | 23 0.02 89.03 556 | 13 0.01 89.04 557 | 8 0.01 89.05 558 | 343 0.26 89.31 563 | 98 0.07 89.38 564 | 2 0.00 89.38 565 | 48 0.04 89.42 566 | 57 0.04 89.46 567 | 783 0.59 90.04 569 | 10 0.01 90.05 573 | 123 0.09 90.14 575 | 391 0.29 90.44 576 | 21 0.02 90.45 577 | 65 0.05 90.50 579 | 330 0.25 90.75 583 | 16 0.01 90.76 584 | 39 0.03 90.79 585 | 273 0.20 90.99 587 | 9 0.01 91.00 588 | 47 0.04 91.04 589 | 16 0.01 91.05 593 | 36 0.03 91.07 594 | 5 0.00 91.08 595 | 109 0.08 91.16 596 | 15 0.01 91.17 597 | 43 0.03 91.20 598 | 18 0.01 91.22 599 | 140 0.10 91.32 613 | 34 0.03 91.35 614 | 13 0.01 91.36 615 | 1 0.00 91.36 616 | 24 0.02 91.37 617 | 17 0.01 91.39 628 | 528 0.39 91.78 634 | 52 0.04 91.82 636 | 16 0.01 91.83 637 | 247 0.18 92.02 639 | 2 0.00 92.02 643 | 11 0.01 92.03 644 | 9 0.01 92.03 645 | 2 0.00 92.04 646 | 1 0.00 92.04 647 | 34 0.03 92.06 649 | 6 0.00 92.07 653 | 47 0.04 92.10 654 | 1 0.00 92.10 655 | 1 0.00 92.10 656 | 1 0.00 92.10 657 | 41 0.03 92.13 658 | 17 0.01 92.15 659 | 5 0.00 92.15 666 | 36 0.03 92.18 667 | 17 0.01 92.19 668 | 35 0.03 92.22 669 | 6 0.00 92.22 674 | 7 0.01 92.23 675 | 10 0.01 92.23 676 | 4 0.00 92.24 677 | 34 0.03 92.26 678 | 25 0.02 92.28 679 | 20 0.01 92.30 683 | 159 0.12 92.41 684 | 11 0.01 92.42 686 | 188 0.14 92.56 687 | 83 0.06 92.63 688 | 40 0.03 92.66 689 | 64 0.05 92.70 693 | 3 0.00 92.71 694 | 44 0.03 92.74 695 | 14 0.01 92.75 696 | 52 0.04 92.79 699 | 20 0.01 92.80 703 | 8 0.01 92.81 704 | 15 0.01 92.82 705 | 1 0.00 92.82 706 | 52 0.04 92.86 707 | 4 0.00 92.86 708 | 9 0.01 92.87 709 | 47 0.04 92.90 713 | 4 0.00 92.91 714 | 11 0.01 92.92 715 | 15 0.01 92.93 717 | 8 0.01 92.93 719 | 36 0.03 92.96 723 | 15 0.01 92.97 724 | 5 0.00 92.98 725 | 13 0.01 92.98 726 | 6 0.00 92.99 727 | 41 0.03 93.02 728 | 5 0.00 93.02 729 | 3 0.00 93.03 733 | 31 0.02 93.05 734 | 148 0.11 93.16 735 | 17 0.01 93.17 736 | 10 0.01 93.18 737 | 16 0.01 93.19 738 | 29 0.02 93.21 739 | 10 0.01 93.22 743 | 2 0.00 93.22 744 | 277 0.21 93.43 745 | 4 0.00 93.43 747 | 47 0.04 93.47 748 | 117 0.09 93.56 749 | 25 0.02 93.57 753 | 15 0.01 93.59 754 | 200 0.15 93.73 755 | 16 0.01 93.75 756 | 65 0.05 93.80 757 | 32 0.02 93.82 758 | 10 0.01 93.83 759 | 95 0.07 93.90 763 | 4 0.00 93.90 764 | 3 0.00 93.90 765 | 9 0.01 93.91 766 | 35 0.03 93.94 768 | 20 0.01 93.95 769 | 84 0.06 94.01 773 | 3 0.00 94.02 774 | 55 0.04 94.06 777 | 570 0.43 94.48 779 | 205 0.15 94.64 783 | 327 0.24 94.88 784 | 11 0.01 94.89 785 | 633 0.47 95.36 786 | 7 0.01 95.37 787 | 8 0.01 95.37 789 | 13 0.01 95.38 793 | 12 0.01 95.39 795 | 29 0.02 95.41 796 | 237 0.18 95.59 797 | 39 0.03 95.62 798 | 2 0.00 95.62 799 | 87 0.07 95.69 803 | 40 0.03 95.72 804 | 1568 1.17 96.89 806 | 59 0.04 96.93 808 | 284 0.21 97.15 809 | 144 0.11 97.25 813 | 27 0.02 97.27 814 | 6 0.00 97.28 823 | 28 0.02 97.30 824 | 27 0.02 97.32 825 | 4 0.00 97.32 826 | 2 0.00 97.32 828 | 16 0.01 97.34 829 | 7 0.01 97.34 843 | 9 0.01 97.35 844 | 146 0.11 97.46 845 | 2 0.00 97.46 848 | 13 0.01 97.47 849 | 31 0.02 97.49 853 | 48 0.04 97.53 855 | 35 0.03 97.55 856 | 282 0.21 97.77 859 | 42 0.03 97.80 864 | 4 0.00 97.80 865 | 21 0.02 97.82 866 | 71 0.05 97.87 867 | 3 0.00 97.87 868 | 2 0.00 97.87 869 | 556 0.42 98.29 874 | 47 0.04 98.32 875 | 28 0.02 98.34 876 | 7 0.01 98.35 877 | 580 0.43 98.78 878 | 47 0.04 98.82 883 | 401 0.30 99.12 885 | 93 0.07 99.19 887 | 202 0.15 99.34 888 | 210 0.16 99.50 889 | 669 0.50 100.00 905 | 5 0.00 100.00 ------------+----------------------------------- Total | 133710 100.00 . *create a new occupational variable that has 10 categories, just using the first digit of occ . gen occ1digit=int(occ/100) . tabulate occ1digit occ1digit | Freq. Percent Cum. ------------+----------------------------------- 0 | 78412 58.64 58.64 1 | 6197 4.63 63.28 2 | 10244 7.66 70.94 3 | 9569 7.16 78.10 4 | 12224 9.14 87.24 5 | 5459 4.08 91.32 6 | 1982 1.48 92.80 7 | 3857 2.88 95.69 8 | 5761 4.31 100.00 9 | 5 0.00 100.00 ------------+----------------------------------- Total | 133710 100.00 . table occ1digit race, by(sex) -------------------------------------------------------------- p20 and | p25 occ1digit | White Black Amer Indian Asian ----------+--------------------------------------------------- male | 0 | 30,385 3,688 543 1,387 1 | 2,060 148 19 70 2 | 4,422 271 35 209 3 | 1,570 236 27 88 4 | 4,765 570 91 192 5 | 4,785 324 66 75 6 | 1,305 90 18 43 7 | 2,037 256 34 87 8 | 4,123 562 97 108 9 | 5 ----------+--------------------------------------------------- female | 0 | 35,650 4,521 616 1,622 1 | 3,420 340 44 96 2 | 4,567 491 60 189 3 | 6,552 806 97 193 4 | 5,360 919 93 234 5 | 180 20 4 5 6 | 440 51 14 21 7 | 1,140 207 20 76 8 | 709 126 16 20 9 | -------------------------------------------------------------- . *If we look at the data this way, we would have 80 cells, 4X2X10 . table occ1digit race if age>29 & age<40, by(sex) -------------------------------------------------------------- p20 and | p25 occ1digit | White Black Amer Indian Asian ----------+--------------------------------------------------- male | 0 | 2,273 218 26 156 1 | 477 43 4 22 2 | 1,098 71 10 51 3 | 341 72 6 17 4 | 1,022 130 17 51 5 | 1,449 88 20 22 6 | 351 22 2 19 7 | 603 80 8 21 8 | 958 164 21 29 9 | 3 ----------+--------------------------------------------------- female | 0 | 3,505 364 48 206 1 | 771 81 8 18 2 | 1,055 101 12 44 3 | 1,537 244 29 53 4 | 1,203 221 27 51 5 | 56 7 1 1 6 | 111 14 5 5 7 | 308 57 5 21 8 | 177 35 6 6 9 | -------------------------------------------------------------- . *the way we create a cross-tab dataset from an individual level dataset is contract . contract occ1digit race sex if age>29 & age<40 . *brings up a new dataset to save and use . describe Contains data from C:\AAA Miker Files\Archived old class files\Intro Soc. Methods Class\Data and info for > project 3\cps y2k full data (all numeric) newer.dta obs: 73 vars: 4 30 May 2001 12:57 size: 876 (99.9% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- sex byte %8.0g sexnm p20 race byte %11.0g racenm p25 occ1digit float %9.0g _freq int %12.0g Frequency ------------------------------------------------------------------------------- Sorted by: occ1digit race sex Note: dataset has changed since last saved . rename _freq count . *The reason there are fewer than 80 cells in this dataset is that stata dropped the zero cells. That's > not cool, because we need those zero cells. These are sampling zeros, rather than structural zeros, and > sampling zeros must be fit in the model. . clear . use "C:\AAA Miker Files\Archived old class files\Intro Soc. Methods Class\Data and info for project 3\cp > s y2k full data (all numeric) newer.dta", clear . gen occ1digit=int(occ/100) . contract occ1digit race sex if age>29 & age<40, zero . rename _freq count . describe Contains data from C:\AAA Miker Files\Archived old class files\Intro Soc. Methods Class\Data and info for > project 3\cps y2k full data (all numeric) newer.dta obs: 80 vars: 4 30 May 2001 12:57 size: 960 (100.0% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- sex byte %8.0g sexnm p20 race byte %11.0g racenm p25 occ1digit float %9.0g count int %12.0g Frequency ------------------------------------------------------------------------------- Sorted by: occ1digit race sex Note: dataset has changed since last saved . table occ1digit race, by(sex) contents (sum count) -------------------------------------------------------------- p20 and | p25 occ1digit | White Black Amer Indian Asian ----------+--------------------------------------------------- male | 0 | 2273 218 26 156 1 | 477 43 4 22 2 | 1098 71 10 51 3 | 341 72 6 17 4 | 1022 130 17 51 5 | 1449 88 20 22 6 | 351 22 2 19 7 | 603 80 8 21 8 | 958 164 21 29 9 | 3 0 0 0 ----------+--------------------------------------------------- female | 0 | 3505 364 48 206 1 | 771 81 8 18 2 | 1055 101 12 44 3 | 1537 244 29 53 4 | 1203 221 27 51 5 | 56 7 1 1 6 | 111 14 5 5 7 | 308 57 5 21 8 | 177 35 6 6 9 | 0 0 0 0 -------------------------------------------------------------- . *Now let's say we analyze this for a while, and decide (reasonably) that occupational category 9 doesn't > stand on its own . *let's say, we've studied the occupational categories, and category 9 fits most easily along with catego > ry 8 . gen newocc= occ1digit . replace newocc=8 if occ1digit==9 (8 real changes made) . table newocc race, by(sex) contents (sum count) -------------------------------------------------------------- p20 and | p25 newocc | White Black Amer Indian Asian ----------+--------------------------------------------------- male | 0 | 2273 218 26 156 1 | 477 43 4 22 2 | 1098 71 10 51 3 | 341 72 6 17 4 | 1022 130 17 51 5 | 1449 88 20 22 6 | 351 22 2 19 7 | 603 80 8 21 8 | 961 164 21 29 ----------+--------------------------------------------------- female | 0 | 3505 364 48 206 1 | 771 81 8 18 2 | 1055 101 12 44 3 | 1537 244 29 53 4 | 1203 221 27 51 5 | 56 7 1 1 6 | 111 14 5 5 7 | 308 57 5 21 8 | 177 35 6 6 -------------------------------------------------------------- . table newocc race, by(sex) contents (sum count) row col --------------------------------------------------------------------------- p20 and | p25 newocc | White Black Amer Indian Asian Total ----------+---------------------------------------------------------------- male | 0 | 2273 218 26 156 2673 1 | 477 43 4 22 546 2 | 1098 71 10 51 1230 3 | 341 72 6 17 436 4 | 1022 130 17 51 1220 5 | 1449 88 20 22 1579 6 | 351 22 2 19 394 7 | 603 80 8 21 712 8 | 961 164 21 29 1175 | Total | 8575 888 114 388 9965 ----------+---------------------------------------------------------------- female | 0 | 3505 364 48 206 4123 1 | 771 81 8 18 878 2 | 1055 101 12 44 1212 3 | 1537 244 29 53 1863 4 | 1203 221 27 51 1502 5 | 56 7 1 1 65 6 | 111 14 5 5 135 7 | 308 57 5 21 391 8 | 177 35 6 6 224 | Total | 8723 1124 141 405 10393 --------------------------------------------------------------------------- . contract sex race newocc [fweight=count] . rename _freq count . describe Contains data from C:\AAA Miker Files\Archived old class files\Intro Soc. Methods Class\Data and info for > project 3\cps y2k full data (all numeric) newer.dta obs: 72 vars: 4 30 May 2001 12:57 size: 864 (99.9% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- sex byte %8.0g sexnm p20 race byte %11.0g racenm p25 newocc float %9.0g count int %12.0g Frequency ------------------------------------------------------------------------------- Sorted by: sex race newocc Note: dataset has changed since last saved . table newocc race, by(sex) contents (sum count) row col --------------------------------------------------------------------------- p20 and | p25 newocc | White Black Amer Indian Asian Total ----------+---------------------------------------------------------------- male | 0 | 2273 218 26 156 2673 1 | 477 43 4 22 546 2 | 1098 71 10 51 1230 3 | 341 72 6 17 436 4 | 1022 130 17 51 1220 5 | 1449 88 20 22 1579 6 | 351 22 2 19 394 7 | 603 80 8 21 712 8 | 961 164 21 29 1175 | Total | 8575 888 114 388 9965 ----------+---------------------------------------------------------------- female | 0 | 3505 364 48 206 4123 1 | 771 81 8 18 878 2 | 1055 101 12 44 1212 3 | 1537 244 29 53 1863 4 | 1203 221 27 51 1502 5 | 56 7 1 1 65 6 | 111 14 5 5 135 7 | 308 57 5 21 391 8 | 177 35 6 6 224 | Total | 8723 1124 141 405 10393 --------------------------------------------------------------------------- . *OK. That was a little bit of info on dealing with datasets. . clear all . *Now I'm going to pull up the Qian 80-90 intermarriage dataset, which is also on my website, which has a > lot of cells and a lot of zeros . use "C:\AAA Miker Files\New stata files\Qian redo\Qian 80-90 basic.dta", clear . keep mfulleth med4 ffulleth fed4 count year inmarry . *This is the basic dataset from Qian, with inmarry as the racial endogamy variable. . table mfulleth ffulleth, contents (mean inmarry) -------------------------------------- | ffulleth mfulleth | Asian Hisp black white ----------+--------------------------- Asian | 1 0 0 0 Hisp | 0 2 0 0 black | 0 0 3 0 white | 0 0 0 4 -------------------------------------- . describe Contains data from C:\AAA Miker Files\New stata files\Qian redo\Qian 80-90 basic.dta obs: 512 vars: 7 13 Dec 2001 16:37 size: 12,800 (99.8% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- mfulleth str5 %9s med4 byte %8.0g ffulleth str5 %9s fed4 byte %8.0g count long %12.0g COUNT year byte %8.0g inmarry float %9.0g ------------------------------------------------------------------------------- Sorted by: year Note: dataset has changed since last saved . table mfulleth ffulleth, by (med4 fed4 year) contents (sum count) -------------------------------------- med4, | fed4, | year and | ffulleth mfulleth | Asian Hisp black white ----------+--------------------------- 1 | 1 | 80 | Asian | 1 0 0 2 Hisp | 1 2180 13 341 black | 1 24 2039 50 white | 6 165 12 15801 ----------+--------------------------- 1 | 1 | 90 | Asian | 0 0 0 4 Hisp | 1 870 7 187 black | 0 11 713 36 white | 4 156 11 9380 ----------+--------------------------- 1 | 2 | 80 | Asian | 1 2 1 6 Hisp | 0 1119 10 351 black | 0 15 2173 59 white | 8 235 17 17604 ----------+--------------------------- 1 | 2 | 90 | Asian | 0 0 0 2 Hisp | 0 565 8 217 black | 0 12 633 36 white | 2 149 7 10142 ----------+--------------------------- 1 | 3 | 80 | Asian | 0 0 0 0 Hisp | 0 204 6 65 black | 0 3 598 21 white | 1 43 3 2509 ----------+--------------------------- 1 | 3 | 90 | Asian | 0 0 0 1 Hisp | 1 222 3 100 black | 0 4 394 21 white | 5 71 10 4122 ----------+--------------------------- 1 | 4 | 80 | Asian | 1 0 0 0 Hisp | 0 29 0 6 black | 0 2 78 0 white | 2 5 1 367 ----------+--------------------------- 1 | 4 | 90 | Asian | 0 0 0 0 Hisp | 0 13 0 11 black | 0 0 43 1 white | 0 5 1 423 ----------+--------------------------- 2 | 1 | 80 | Asian | 2 0 0 5 Hisp | 1 1129 4 311 black | 0 23 1716 73 white | 4 283 19 15539 ----------+--------------------------- 2 | 1 | 90 | Asian | 2 2 0 3 Hisp | 0 468 6 146 black | 0 13 557 42 white | 3 140 11 7868 ----------+--------------------------- 2 | 2 | 80 | Asian | 30 4 1 26 Hisp | 7 2383 31 1132 black | 3 62 6734 227 white | 46 1082 59 81301 ----------+--------------------------- 2 | 2 | 90 | Asian | 1 2 0 11 Hisp | 3 1227 17 547 black | 0 48 2643 148 white | 18 572 44 39467 ----------+--------------------------- 2 | 3 | 80 | Asian | 16 2 0 9 Hisp | 3 477 13 257 black | 1 21 2054 71 white | 17 257 21 18173 ----------+--------------------------- 2 | 3 | 90 | Asian | 4 3 1 5 Hisp | 0 497 19 334 black | 0 21 1513 77 white | 16 343 32 19526 ----------+--------------------------- 2 | 4 | 80 | Asian | 3 1 0 0 Hisp | 0 45 3 37 black | 1 3 405 14 white | 3 37 5 4161 ----------+--------------------------- 2 | 4 | 90 | Asian | 0 0 0 0 Hisp | 1 42 1 34 black | 0 1 216 5 white | 4 41 2 3381 ----------+--------------------------- 3 | 1 | 80 | Asian | 1 1 0 5 Hisp | 1 264 3 87 black | 0 10 374 22 white | 1 71 1 2982 ----------+--------------------------- 3 | 1 | 90 | Asian | 0 0 0 2 Hisp | 0 163 1 76 black | 0 3 210 22 white | 4 85 5 2657 ----------+--------------------------- 3 | 2 | 80 | Asian | 18 2 1 19 Hisp | 3 782 12 473 black | 0 32 1911 108 white | 25 390 17 27314 ----------+--------------------------- 3 | 2 | 90 | Asian | 2 3 1 10 Hisp | 2 419 7 313 black | 0 18 870 89 white | 6 295 23 15601 ----------+--------------------------- 3 | 3 | 80 | Asian | 48 4 0 32 Hisp | 2 607 12 404 black | 2 31 2162 93 white | 36 390 25 24167 ----------+--------------------------- 3 | 3 | 90 | Asian | 14 6 1 26 Hisp | 3 783 23 624 black | 1 39 2119 160 white | 21 636 53 29109 ----------+--------------------------- 3 | 4 | 80 | Asian | 23 1 0 8 Hisp | 1 128 3 97 black | 1 7 660 27 white | 21 88 3 8301 ----------+--------------------------- 3 | 4 | 90 | Asian | 10 2 0 6 Hisp | 0 102 4 145 black | 0 5 501 43 white | 15 115 10 7868 ----------+--------------------------- 4 | 1 | 80 | Asian | 0 0 0 1 Hisp | 0 24 3 12 black | 0 0 53 1 white | 1 11 1 519 ----------+--------------------------- 4 | 1 | 90 | Asian | 0 0 0 0 Hisp | 0 10 0 3 black | 0 1 27 1 white | 0 13 0 303 ----------+--------------------------- 4 | 2 | 80 | Asian | 5 2 0 12 Hisp | 1 141 0 112 black | 0 10 354 27 white | 3 132 5 9821 ----------+--------------------------- 4 | 2 | 90 | Asian | 1 3 0 5 Hisp | 0 35 2 34 black | 0 3 94 6 white | 2 42 3 2939 ----------+--------------------------- 4 | 3 | 80 | Asian | 39 0 1 24 Hisp | 2 157 5 161 black | 0 15 667 37 white | 24 198 12 16340 ----------+--------------------------- 4 | 3 | 90 | Asian | 10 0 0 9 Hisp | 1 130 1 118 black | 0 6 324 32 white | 12 205 10 10095 ----------+--------------------------- 4 | 4 | 80 | Asian | 89 0 0 50 Hisp | 5 164 4 234 black | 0 8 1045 38 white | 75 192 14 27573 ----------+--------------------------- 4 | 4 | 90 | Asian | 51 3 0 37 Hisp | 1 90 6 220 black | 1 8 454 38 white | 62 297 21 20444 -------------------------------------- . *just scrolling through, it is apparent that there are plenty of zeros in this dataset. . tabulate count COUNT | Freq. Percent Cum. ------------+----------------------------------- 0 | 102 19.92 19.92 1 | 49 9.57 29.49 2 | 23 4.49 33.98 3 | 27 5.27 39.26 4 | 12 2.34 41.60 5 | 15 2.93 44.53 6 | 11 2.15 46.68 7 | 5 0.98 47.66 8 | 5 0.98 48.63 9 | 2 0.39 49.02 10 | 10 1.95 50.98 11 | 6 1.17 52.15 12 | 8 1.56 53.71 13 | 5 0.98 54.69 14 | 3 0.59 55.27 15 | 3 0.59 55.86 16 | 2 0.39 56.25 17 | 4 0.78 57.03 18 | 3 0.59 57.62 19 | 3 0.59 58.20 21 | 8 1.56 59.77 22 | 2 0.39 60.16 23 | 4 0.78 60.94 24 | 4 0.78 61.72 25 | 2 0.39 62.11 26 | 2 0.39 62.50 27 | 3 0.59 63.09 29 | 1 0.20 63.28 30 | 1 0.20 63.48 31 | 2 0.39 63.87 32 | 4 0.78 64.65 34 | 2 0.39 65.04 35 | 1 0.20 65.23 36 | 3 0.59 65.82 37 | 4 0.78 66.60 38 | 2 0.39 66.99 39 | 2 0.39 67.38 41 | 1 0.20 67.58 42 | 3 0.59 68.16 43 | 3 0.59 68.75 44 | 1 0.20 68.95 45 | 1 0.20 69.14 46 | 1 0.20 69.34 48 | 2 0.39 69.73 50 | 2 0.39 70.12 51 | 1 0.20 70.31 53 | 2 0.39 70.70 59 | 2 0.39 71.09 62 | 2 0.39 71.48 65 | 1 0.20 71.68 71 | 3 0.59 72.27 73 | 1 0.20 72.46 75 | 1 0.20 72.66 76 | 1 0.20 72.85 77 | 1 0.20 73.05 78 | 1 0.20 73.24 85 | 1 0.20 73.44 87 | 1 0.20 73.63 88 | 1 0.20 73.83 89 | 2 0.39 74.22 90 | 1 0.20 74.41 93 | 1 0.20 74.61 94 | 1 0.20 74.80 97 | 1 0.20 75.00 100 | 1 0.20 75.20 102 | 1 0.20 75.39 108 | 1 0.20 75.59 112 | 1 0.20 75.78 115 | 1 0.20 75.98 118 | 1 0.20 76.17 128 | 1 0.20 76.37 130 | 1 0.20 76.56 132 | 1 0.20 76.76 140 | 1 0.20 76.95 141 | 1 0.20 77.15 145 | 1 0.20 77.34 146 | 1 0.20 77.54 148 | 1 0.20 77.73 149 | 1 0.20 77.93 156 | 1 0.20 78.13 157 | 1 0.20 78.32 160 | 1 0.20 78.52 161 | 1 0.20 78.71 163 | 1 0.20 78.91 164 | 1 0.20 79.10 165 | 1 0.20 79.30 187 | 1 0.20 79.49 192 | 1 0.20 79.69 198 | 1 0.20 79.88 204 | 1 0.20 80.08 205 | 1 0.20 80.27 210 | 1 0.20 80.47 216 | 1 0.20 80.66 217 | 1 0.20 80.86 220 | 1 0.20 81.05 222 | 1 0.20 81.25 227 | 1 0.20 81.45 234 | 1 0.20 81.64 235 | 1 0.20 81.84 257 | 2 0.39 82.23 264 | 1 0.20 82.42 283 | 1 0.20 82.62 295 | 1 0.20 82.81 297 | 1 0.20 83.01 303 | 1 0.20 83.20 311 | 1 0.20 83.40 313 | 1 0.20 83.59 324 | 1 0.20 83.79 334 | 1 0.20 83.98 341 | 1 0.20 84.18 343 | 1 0.20 84.38 351 | 1 0.20 84.57 354 | 1 0.20 84.77 367 | 1 0.20 84.96 374 | 1 0.20 85.16 390 | 2 0.39 85.55 394 | 1 0.20 85.74 404 | 1 0.20 85.94 405 | 1 0.20 86.13 419 | 1 0.20 86.33 423 | 1 0.20 86.52 454 | 1 0.20 86.72 468 | 1 0.20 86.91 473 | 1 0.20 87.11 477 | 1 0.20 87.30 497 | 1 0.20 87.50 501 | 1 0.20 87.70 519 | 1 0.20 87.89 547 | 1 0.20 88.09 557 | 1 0.20 88.28 565 | 1 0.20 88.48 572 | 1 0.20 88.67 598 | 1 0.20 88.87 607 | 1 0.20 89.06 624 | 1 0.20 89.26 633 | 1 0.20 89.45 636 | 1 0.20 89.65 660 | 1 0.20 89.84 667 | 1 0.20 90.04 713 | 1 0.20 90.23 782 | 1 0.20 90.43 783 | 1 0.20 90.63 870 | 2 0.39 91.02 1045 | 1 0.20 91.21 1082 | 1 0.20 91.41 1119 | 1 0.20 91.60 1129 | 1 0.20 91.80 1132 | 1 0.20 91.99 1227 | 1 0.20 92.19 1513 | 1 0.20 92.38 1716 | 1 0.20 92.58 1911 | 1 0.20 92.77 2039 | 1 0.20 92.97 2054 | 1 0.20 93.16 2119 | 1 0.20 93.36 2162 | 1 0.20 93.55 2173 | 1 0.20 93.75 2180 | 1 0.20 93.95 2383 | 1 0.20 94.14 2509 | 1 0.20 94.34 2643 | 1 0.20 94.53 2657 | 1 0.20 94.73 2939 | 1 0.20 94.92 2982 | 1 0.20 95.12 3381 | 1 0.20 95.31 4122 | 1 0.20 95.51 4161 | 1 0.20 95.70 6734 | 1 0.20 95.90 7868 | 2 0.39 96.29 8301 | 1 0.20 96.48 9380 | 1 0.20 96.68 9821 | 1 0.20 96.88 10095 | 1 0.20 97.07 10142 | 1 0.20 97.27 15539 | 1 0.20 97.46 15601 | 1 0.20 97.66 15801 | 1 0.20 97.85 16340 | 1 0.20 98.05 17604 | 1 0.20 98.24 18173 | 1 0.20 98.44 19526 | 1 0.20 98.63 20444 | 1 0.20 98.83 24167 | 1 0.20 99.02 27314 | 1 0.20 99.22 27573 | 1 0.20 99.41 29109 | 1 0.20 99.61 39467 | 1 0.20 99.80 81301 | 1 0.20 100.00 ------------+----------------------------------- Total | 512 100.00 . *There are 102 cells with count=0, which is and seems like a lot . *how is the modeling process going to be affected by the zeros . *Before I build a full model for this data, let me briefly also discuss matsize . matsize unrecognized command: matsize r(199); . mem unrecognized command: mem r(199); . memory (see query memory for total usage) Details of -set memory- usage Allocated 20,971,520 bytes 100.00% overhead (pointers) 2,048 0.01% data 10,752 0.05% ------------ data + overhead 12,800 0.06% programs, saved results, etc. 25,696 0.12% ------------ Total used 38,496 0.18% Free 20,933,024 99.82% . set matsize 500 Current memory allocation current memory usage settable value description (1M = 1024k) -------------------------------------------------------------------- set maxvar 5000 max. variables allowed 1.733M set memory 20M max. data space 20.000M set matsize 500 max. RHS vars in models 1.949M ----------- 23.682M . desmat mfulleth*med4*fed4*year ffulleth*med4*fed4*year inmarry*med4 inmarry*ffulleth Desmat generated the following design matrix: nr Variables Term Parameterization First Last 1 _x_1 _x_3 mfulleth ind(1) 2 _x_4 _x_6 med4 ind(1) 3 _x_7 _x_15 mfulleth.med4 ind(1).ind(1) 4 _x_16 _x_18 fed4 ind(1) 5 _x_19 _x_27 mfulleth.fed4 ind(1).ind(1) 6 _x_28 _x_36 med4.fed4 ind(1).ind(1) 7 _x_37 _x_63 mfulleth.med4.fed4 ind(1).ind(1).ind(1) 8 _x_64 year ind(80) 9 _x_65 _x_67 mfulleth.year ind(1).ind(80) 10 _x_68 _x_70 med4.year ind(1).ind(80) 11 _x_71 _x_79 mfulleth.med4.year ind(1).ind(1).ind(80) 12 _x_80 _x_82 fed4.year ind(1).ind(80) 13 _x_83 _x_91 mfulleth.fed4.year ind(1).ind(1).ind(80) 14 _x_92 _x_99 med4.fed4.year ind(1).ind(1).ind(80) 15 _x_100 _x_126 mfulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80) 16 _x_127 _x_129 ffulleth ind(1) 17 _x_130 _x_138 ffulleth.med4 ind(1).ind(1) 18 _x_139 _x_147 ffulleth.fed4 ind(1).ind(1) 19 _x_148 _x_174 ffulleth.med4.fed4 ind(1).ind(1).ind(1) 20 _x_175 _x_177 ffulleth.year ind(1).ind(80) 21 _x_178 _x_186 ffulleth.med4.year ind(1).ind(1).ind(80) 22 _x_187 _x_195 ffulleth.fed4.year ind(1).ind(1).ind(80) 23 _x_196 med4.fed4.year ind(1).ind(1).ind(80) 24 _x_197 _x_223 ffulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80) 25 _x_224 _x_227 inmarry ind(0) 26 _x_228 _x_239 inmarry.med4 ind(0).ind(1) . poisson count _x* Iteration 0: log likelihood = -12769785 (not concave) Iteration 1: log likelihood = -11748206 (not concave) Iteration 2: log likelihood = -11645763 (not concave) Iteration 3: log likelihood = -10995851 (not concave) Iteration 4: log likelihood = -10014024 (not concave) Iteration 5: log likelihood = -8583389.8 (not concave) Iteration 6: log likelihood = -8411727.9 (not concave) Iteration 7: log likelihood = -7335974.3 (not concave) Iteration 8: log likelihood = -6178594.6 (not concave) Iteration 9: log likelihood = -5193080 (not concave) Iteration 10: log likelihood = -4413445.6 (not concave) Iteration 11: log likelihood = -3950443.9 (not concave) Iteration 12: log likelihood = -3558453.9 (not concave) Iteration 13: log likelihood = -3325650.4 (not concave) Iteration 14: log likelihood = -3193544.6 (not concave) Iteration 15: log likelihood = -2678439.1 (not concave) Iteration 16: log likelihood = -2514465.9 (not concave) Iteration 17: log likelihood = -2249359.1 (not concave) Iteration 18: log likelihood = -2131360.5 (not concave) Iteration 19: log likelihood = -1964163.3 (not concave) Iteration 20: log likelihood = -1833788.4 (not concave) Iteration 21: log likelihood = -1649899.1 (not concave) Iteration 22: log likelihood = -1559898 (not concave) Iteration 23: log likelihood = -1409539.3 (not concave) Iteration 24: log likelihood = -1307448.3 (not concave) Iteration 25: log likelihood = -1204397.5 (not concave) Iteration 26: log likelihood = -1114434.8 (not concave) Iteration 27: log likelihood = -1035972.4 (not concave) Iteration 28: log likelihood = -995883.9 (not concave) Iteration 29: log likelihood = -825082.13 (not concave) Iteration 30: log likelihood = -808652.68 (not concave) Iteration 31: log likelihood = -783333.35 (not concave) Iteration 32: log likelihood = -712991.24 (not concave) Iteration 33: log likelihood = -643558.89 (not concave) Iteration 34: log likelihood = -573677.38 (not concave) Iteration 35: log likelihood = -544688.32 (not concave) Iteration 36: log likelihood = -492900.82 (not concave) Iteration 37: log likelihood = -468675.91 (not concave) Iteration 38: log likelihood = -408073.35 (not concave) Iteration 39: log likelihood = -377533.88 (not concave) Iteration 40: log likelihood = -333454.18 (not concave) Iteration 41: log likelihood = -312218.6 (not concave) Iteration 42: log likelihood = -288611.96 (not concave) Iteration 43: log likelihood = -271151.42 (not concave) Iteration 44: log likelihood = -255940.18 (not concave) Iteration 45: log likelihood = -238945.73 (not concave) Iteration 46: log likelihood = -214915.51 (not concave) Iteration 47: log likelihood = -193559 (not concave) Iteration 48: log likelihood = -181343.66 (not concave) Iteration 49: log likelihood = -172422.36 (not concave) Iteration 50: log likelihood = -165100.88 (not concave) Iteration 51: log likelihood = -158044.45 (not concave) Iteration 52: log likelihood = -152976.13 (not concave) Iteration 53: log likelihood = -148572.26 (not concave) Iteration 54: log likelihood = -144577.57 (not concave) Iteration 55: log likelihood = -141377.66 (not concave) Iteration 56: log likelihood = -138622.35 (not concave) Iteration 57: log likelihood = -136040.9 (not concave) Iteration 58: log likelihood = -133713.98 (not concave) Iteration 59: log likelihood = -131541.32 (not concave) Iteration 60: log likelihood = -129511.45 (not concave) Iteration 61: log likelihood = -127585.21 (not concave) Iteration 62: log likelihood = -125778.03 (not concave) Iteration 63: log likelihood = -124048.44 (not concave) Iteration 64: log likelihood = -122414.35 (not concave) Iteration 65: log likelihood = -120848.41 (not concave) Iteration 66: log likelihood = -119366.83 (not concave) Iteration 67: log likelihood = -117945.54 (not concave) Iteration 68: log likelihood = -116599.88 (not concave) Iteration 69: log likelihood = -115309.68 (not concave) Iteration 70: log likelihood = -114088.02 (not concave) Iteration 71: log likelihood = -112917.21 (not concave) Iteration 72: log likelihood = -111808.5 (not concave) Iteration 73: log likelihood = -110746.32 (not concave) Iteration 74: log likelihood = -109740.07 (not concave) Iteration 75: log likelihood = -108776.01 (not concave) Iteration 76: log likelihood = -107862 (not concave) Iteration 77: log likelihood = -106985.87 (not concave) Iteration 78: log likelihood = -106154.19 (not concave) Iteration 79: log likelihood = -105356.19 (not concave) Iteration 80: log likelihood = -104597.37 (not concave) Iteration 81: log likelihood = -103868.22 Iteration 82: log likelihood = -85305.121 (not concave) Iteration 83: log likelihood = -84592.135 (not concave) Iteration 84: log likelihood = -83849.199 (not concave) Iteration 85: log likelihood = -72653.43 Iteration 86: log likelihood = -66241.386 (backed up) Iteration 87: log likelihood = -61824.327 (backed up) Iteration 88: log likelihood = -48561.895 (backed up) Iteration 89: log likelihood = -27737.079 (backed up) Iteration 90: log likelihood = -21629.239 Iteration 91: log likelihood = -4357.9694 Iteration 92: log likelihood = -1823.8584 Iteration 93: log likelihood = -1509.348 Iteration 94: log likelihood = -1471.0526 Iteration 95: log likelihood = -1468.5956 Iteration 96: log likelihood = -1468.045 Iteration 97: log likelihood = -1467.9605 Iteration 98: log likelihood = -1467.9483 (not concave) Iteration 99: log likelihood = -1467.9482 (not concave) Poisson regression Number of obs = 512 LR chi2(239) = 2801880.68 Prob > chi2 = 0.0000 Log likelihood = -1467.9482 Pseudo R2 = 0.9990 ------------------------------------------------------------------------------ count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 5.448549 .585526 9.31 0.000 4.300939 6.596159 _x_2 | 3.486594 .5915616 5.89 0.000 2.327154 4.646033 _x_3 | 5.87817 .5895042 9.97 0.000 4.722763 7.033577 _x_4 | .2193598 .8196515 0.27 0.789 -1.387128 1.825847 _x_5 | .2657539 .9044073 0.29 0.769 -1.506852 2.03836 _x_6 | -.8797525 1.388505 -0.63 0.526 -3.601171 1.841666 _x_7 | -.8975445 .7045176 -1.27 0.203 -2.278374 .4832847 _x_8 | -2.153647 .7110799 -3.03 0.002 -3.547338 -.7599559 _x_9 | -1.993146 1.227901 -1.62 0.105 -4.399787 .4134952 _x_10 | -.5588119 .7139814 -0.78 0.434 -1.95819 .8405661 _x_11 | -1.666749 .7298374 -2.28 0.022 -3.097204 -.2362937 _x_12 | -3.34051 1.310946 -2.55 0.011 -5.909918 -.7711022 _x_13 | -.5855016 .7085832 -0.83 0.409 -1.974299 .803296 _x_14 | -1.896747 .7140046 -2.66 0.008 -3.296171 -.497324 _x_15 | -1.487175 1.222867 -1.22 0.224 -3.88395 .9095992 _x_16 | 1.203974 .7822878 1.54 0.124 -.3292819 2.73723 _x_17 | -9.473122 59.15742 -0.16 0.873 -125.4195 106.4733 _x_18 | .6161725 1.267197 0.49 0.627 -1.867488 3.099833 _x_19 | -1.563871 .6621824 -2.36 0.018 -2.861725 -.2660174 _x_20 | 7.169991 59.14985 0.12 0.904 -108.7616 123.1016 _x_21 | -2.341304 1.284191 -1.82 0.068 -4.858273 .1756645 _x_22 | -1.245584 .6726268 -1.85 0.064 -2.563908 .0727403 _x_23 | 7.837847 59.15015 0.13 0.895 -108.0943 123.77 _x_24 | -1.743138 1.372926 -1.27 0.204 -4.434023 .9477477 _x_25 | -1.166897 .6608538 -1.77 0.077 -2.462146 .1283532 _x_26 | 7.337828 59.1498 0.12 0.901 -108.5936 123.2693 _x_27 | -1.725353 1.267598 -1.36 0.173 -4.2098 .7590942 _x_28 | 1.621767 .9310959 1.74 0.082 -.2031478 3.446681 _x_29 | 11.73206 59.15979 0.20 0.843 -104.219 127.6831 _x_30 | -.2263173 1.456914 -0.16 0.877 -3.081817 2.629182 _x_31 | 1.254053 1.011264 1.24 0.215 -.7279878 3.236094 _x_32 | 12.93558 59.16074 0.22 0.827 -103.0173 128.8885 _x_33 | 2.114516 1.42071 1.49 0.137 -.6700242 4.899056 _x_34 | 1.265613 1.492061 0.85 0.396 -1.658774 4.189999 _x_35 | 14.05134 59.17016 0.24 0.812 -101.92 130.0227 _x_36 | 4.939519 1.760957 2.81 0.005 1.488107 8.390931 _x_37 | .8080635 .7813933 1.03 0.301 -.7234392 2.339566 _x_38 | -8.410857 59.15158 -0.14 0.887 -124.3458 107.5241 _x_39 | 1.069207 1.459111 0.73 0.464 -1.790598 3.929012 _x_40 | 1.627989 .7947458 2.05 0.041 .0703158 3.185662 _x_41 | -7.832315 59.15138 -0.13 0.895 -123.7669 108.1023 _x_42 | 1.622749 1.366095 1.19 0.235 -1.054749 4.300247 _x_43 | .4375246 1.2901 0.34 0.735 -2.091026 2.966075 _x_44 | -8.714824 59.15989 -0.15 0.883 -124.6661 107.2364 _x_45 | .6566 1.681155 0.39 0.696 -2.638403 3.951603 _x_46 | .6014189 .7954949 0.76 0.450 -.9577226 2.16056 _x_47 | -8.709346 59.15198 -0.15 0.883 -124.6451 107.2264 _x_48 | 1.140763 1.548362 0.74 0.461 -1.893971 4.175496 _x_49 | 1.331342 .8183996 1.63 0.104 -.272692 2.935375 _x_50 | -8.361315 59.15189 -0.14 0.888 -124.2969 107.5742 _x_51 | 1.392772 1.46067 0.95 0.340 -1.470088 4.255632 _x_52 | 2.05761 1.377016 1.49 0.135 -.6412914 4.75651 _x_53 | -7.490677 59.16197 -0.13 0.899 -123.446 108.4646 _x_54 | 1.77986 1.80744 0.98 0.325 -1.762658 5.322378 _x_55 | .8728797 .7795502 1.12 0.263 -.6550107 2.40077 _x_56 | -8.064573 59.1515 -0.14 0.892 -123.9994 107.8702 _x_57 | 1.409768 1.439768 0.98 0.327 -1.412125 4.231661 _x_58 | 1.819037 .7912515 2.30 0.022 .2682125 3.369861 _x_59 | -7.296997 59.15129 -0.12 0.902 -123.2314 108.6374 _x_60 | 1.978852 1.347762 1.47 0.142 -.6627134 4.620418 _x_61 | .8558027 1.278062 0.67 0.503 -1.649153 3.360759 _x_62 | -7.871357 59.1596 -0.13 0.894 -123.822 108.0793 _x_63 | 1.166901 1.660258 0.70 0.482 -2.087144 4.420946 _x_64 | .2900972 .9112573 0.32 0.750 -1.495934 2.076129 _x_65 | -1.144925 .7683322 -1.49 0.136 -2.650828 .3609786 _x_66 | -.9943502 .782957 -1.27 0.204 -2.528918 .5402174 _x_67 | -.8151772 .766921 -1.06 0.288 -2.318315 .6879603 _x_68 | -.1389539 1.151918 -0.12 0.904 -2.396672 2.118765 _x_69 | -.8904157 1.344727 -0.66 0.508 -3.526032 1.7452 _x_70 | -14.47491 69.17111 -0.21 0.834 -150.0478 121.098 _x_71 | .3770737 .9500353 0.40 0.691 -1.484961 2.239109 _x_72 | 2.277788 1.140384 2.00 0.046 .0426772 4.512899 _x_73 | 7.186763 49.98544 0.14 0.886 -90.78289 105.1564 _x_74 | .2858249 .9730465 0.29 0.769 -1.621311 2.192961 _x_75 | 2.125272 1.171913 1.81 0.070 -.1716344 4.422179 _x_76 | 9.527559 49.9905 0.19 0.849 -88.45203 107.5071 _x_77 | .1592276 .9474927 0.17 0.867 -1.697824 2.016279 _x_78 | 2.228574 1.13629 1.96 0.050 .0014867 4.455661 _x_79 | 7.835005 49.98441 0.16 0.875 -90.13263 105.8026 _x_80 | -2.670971 1.407251 -1.90 0.058 -5.429133 .0871914 _x_81 | 9.013327 59.17018 0.15 0.879 -106.9581 124.9847 _x_82 | -15.26365 63.21355 -0.24 0.809 -139.1599 108.6326 _x_83 | 2.088382 1.094586 1.91 0.056 -.0569666 4.233731 _x_84 | -6.543983 59.16079 -0.11 0.912 -122.497 109.409 _x_85 | 8.148235 49.56808 0.16 0.869 -89.00342 105.2999 _x_86 | 1.909267 1.114896 1.71 0.087 -.275889 4.094423 _x_87 | -7.052856 59.16148 -0.12 0.905 -123.0072 108.9015 _x_88 | 7.207378 49.57415 0.15 0.884 -89.95616 104.3709 _x_89 | 1.783697 1.092101 1.63 0.102 -.3567829 3.924176 _x_90 | -6.628045 59.16068 -0.11 0.911 -122.5808 109.3248 _x_91 | 7.535926 49.56695 0.15 0.879 -89.61351 104.6854 _x_92 | .7300433 1.610403 0.45 0.650 -2.426289 3.886375 _x_93 | -1.849647 39.27698 -0.05 0.962 -78.83111 75.13182 _x_94 | 1.697163 1.766611 0.96 0.337 -1.765332 5.159658 _x_95 | -9.463522 59.17886 -0.16 0.873 -125.452 106.5249 _x_96 | 15.11482 63.22207 0.24 0.811 -108.7982 139.0278 _x_97 | 16.00152 69.18294 0.23 0.817 -119.5945 151.5976 _x_98 | 3.776007 91.02203 0.04 0.967 -174.6239 182.1759 _x_99 | 29.00372 93.70051 0.31 0.757 -154.6459 212.6534 _x_100 | -.7635876 1.267707 -0.60 0.547 -3.248249 1.721073 _x_101 | 7.896373 59.16461 0.13 0.894 -108.0641 123.8569 _x_102 | 9.680302 1.032371 9.38 0.000 7.656893 11.70371 _x_103 | -3.165011 1.4178 -2.23 0.026 -5.943848 -.3861742 _x_104 | 5.919135 59.16717 0.10 0.920 -110.0464 121.8847 _x_105 | -8.893154 49.57655 -0.18 0.858 -106.0614 88.27509 _x_106 | -8.68607 49.99354 -0.17 0.862 -106.6716 89.29948 _x_107 | 1.158224 77.44704 0.01 0.988 -150.6352 152.9516 _x_108 | -14.16806 70.39162 -0.20 0.840 -152.1331 123.797 _x_109 | -.5269121 1.295639 -0.41 0.684 -3.066318 2.012494 _x_110 | 8.075699 59.1656 0.14 0.891 -107.8867 124.0381 _x_111 | 10.0734 . . . . . _x_112 | -2.902974 1.454602 -2.00 0.046 -5.75394 -.0520071 _x_113 | 6.357136 59.16835 0.11 0.914 -109.6107 122.325 _x_114 | -8.035125 49.58336 -0.16 0.871 -105.2167 89.14647 _x_115 | -11.4242 49.99987 -0.23 0.819 -109.4221 86.57374 _x_116 | -.8053825 77.45088 -0.01 0.992 -152.6063 150.9956 _x_117 | -15.82189 70.39949 -0.22 0.822 -153.8023 122.1586 _x_118 | -.5576096 1.264125 -0.44 0.659 -3.03525 1.920031 _x_119 | 7.849627 59.16445 0.13 0.894 -108.1106 123.8098 _x_120 | 10.17512 .9694105 10.50 0.000 8.27511 12.07513 _x_121 | -3.121795 1.412532 -2.21 0.027 -5.890308 -.3532826 _x_122 | 5.638006 59.16698 0.10 0.924 -110.3272 121.6032 _x_123 | -8.729828 49.57526 -0.18 0.860 -105.8955 88.43589 _x_124 | -9.284333 49.99229 -0.19 0.853 -107.2674 88.69876 _x_125 | .2404912 77.44626 0.00 0.998 -151.5514 152.0324 _x_126 | -14.41614 70.39007 -0.20 0.838 -152.3781 123.5459 _x_127 | 3.958734 .3395735 11.66 0.000 3.293182 4.624286 _x_128 | 1.456924 .3556298 4.10 0.000 .7599026 2.153946 _x_129 | 4.899214 .3416346 14.34 0.000 4.229623 5.568806 _x_130 | .5726205 .5184998 1.10 0.269 -.4436205 1.588862 _x_131 | .5309316 .7018592 0.76 0.449 -.844687 1.90655 _x_132 | -.7154407 1.133261 -0.63 0.528 -2.936591 1.505709 _x_133 | .1980381 .5376183 0.37 0.713 -.8556744 1.251751 _x_134 | -.0490488 .7274648 -0.07 0.946 -1.474854 1.376756 _x_135 | .2825397 1.21595 0.23 0.816 -2.100678 2.665757 _x_136 | .4675693 .520575 0.90 0.369 -.5527389 1.487878 _x_137 | .4668402 .7026197 0.66 0.506 -.9102692 1.84395 _x_138 | -.4897114 1.124423 -0.44 0.663 -2.69354 1.714117 _x_139 | -.2438156 .4792926 -0.51 0.611 -1.183212 .6955806 _x_140 | .0257083 1.05886 0.02 0.981 -2.049618 2.101035 _x_141 | -2.56102 .7429417 -3.45 0.001 -4.017159 -1.104881 _x_142 | .1067926 .4932888 0.22 0.829 -.8600357 1.073621 _x_143 | .4188371 1.073925 0.39 0.697 -1.686018 2.523692 _x_144 | -2.133255 .8851377 -2.41 0.016 -3.868093 -.3984167 _x_145 | .0785646 .4776138 0.16 0.869 -.8575413 1.01467 _x_146 | .3060545 1.056079 0.29 0.772 -1.763822 2.375931 _x_147 | -2.649831 .7125508 -3.72 0.000 -4.046405 -1.253257 _x_148 | -1.051967 .6286696 -1.67 0.094 -2.284137 .180203 _x_149 | -1.833468 1.144466 -1.60 0.109 -4.076581 .4096443 _x_150 | .3064874 .9462955 0.32 0.746 -1.548218 2.161193 _x_151 | -1.212671 .793359 -1.53 0.126 -2.767626 .3422845 _x_152 | -1.967399 1.229098 -1.60 0.109 -4.376386 .4415886 _x_153 | -.2430213 .9810922 -0.25 0.804 -2.165927 1.679884 _x_154 | .6143964 1.234316 0.50 0.619 -1.804818 3.03361 _x_155 | -1.195276 1.520517 -0.79 0.432 -4.175433 1.784882 _x_156 | .4714685 1.315474 0.36 0.720 -2.106814 3.049751 _x_157 | -.9249549 .645853 -1.43 0.152 -2.190804 .3408938 _x_158 | -1.623555 1.163784 -1.40 0.163 -3.90453 .6574204 _x_159 | .9070802 1.076744 0.84 0.400 -1.2033 3.01746 _x_160 | -1.022417 .8175089 -1.25 0.211 -2.624705 .5798708 _x_161 | -1.604421 1.252285 -1.28 0.200 -4.058854 .850012 _x_162 | .3121253 1.107608 0.28 0.778 -1.858747 2.482997 _x_163 | -1.499222 1.32206 -1.13 0.257 -4.090413 1.091968 _x_164 | -2.813595 1.595605 -1.76 0.078 -5.940924 .3137339 _x_165 | -.4964234 1.4688 -0.34 0.735 -3.375219 2.382373 _x_166 | -.9539027 .6265116 -1.52 0.128 -2.181843 .2740374 _x_167 | -1.678769 1.140881 -1.47 0.141 -3.914855 .5573169 _x_168 | 1.260116 .9149545 1.38 0.168 -.5331621 3.053394 _x_169 | -.9705093 .7900581 -1.23 0.219 -2.518995 .5779761 _x_170 | -1.712098 1.225174 -1.40 0.162 -4.113395 .6891981 _x_171 | .6928559 .9543781 0.73 0.468 -1.177691 2.563403 _x_172 | .7053308 1.220541 0.58 0.563 -1.686885 3.097547 _x_173 | -.8983884 1.508187 -0.60 0.551 -3.854381 2.057604 _x_174 | 1.628008 1.286364 1.27 0.206 -.8932191 4.149234 _x_175 | -.0154853 .5635907 -0.03 0.978 -1.120103 1.089132 _x_176 | -.3348357 .5830646 -0.57 0.566 -1.477621 .8079499 _x_177 | .0109089 .5618289 0.02 0.985 -1.090255 1.112073 _x_178 | -.2307211 .8328712 -0.28 0.782 -1.863119 1.401676 _x_179 | -.9130739 .9838699 -0.93 0.353 -2.841423 1.015276 _x_180 | 7.301407 47.85268 0.15 0.879 -86.48813 101.0909 _x_181 | -.2165503 .8590728 -0.25 0.801 -1.900302 1.467201 _x_182 | -.758998 1.021294 -0.74 0.457 -2.760697 1.242701 _x_183 | 5.294768 47.85826 0.11 0.912 -88.5057 99.09523 _x_184 | -.185034 .8301419 -0.22 0.824 -1.812082 1.442014 _x_185 | -.9286923 .9793296 -0.95 0.343 -2.848143 .9907584 _x_186 | 6.616887 47.8519 0.14 0.890 -87.1711 100.4049 _x_187 | .8145124 .9700342 0.84 0.401 -1.08672 2.715744 _x_188 | -1.456419 1.224617 -1.19 0.234 -3.856625 .9437861 _x_189 | 7.350724 39.25835 0.19 0.851 -69.59422 84.29567 _x_190 | .5773223 .9924732 0.58 0.561 -1.367889 2.522534 _x_191 | -1.327944 1.256293 -1.06 0.290 -3.790233 1.134344 _x_192 | 8.498734 39.26584 0.22 0.829 -68.46089 85.45836 _x_193 | .8523603 .9674173 0.88 0.378 -1.043743 2.748463 _x_194 | -1.371543 1.219617 -1.12 0.261 -3.761948 1.018863 _x_195 | 8.392604 39.25685 0.21 0.831 -68.54942 85.33463 _x_196 | -10.14288 59.17556 -0.17 0.864 -126.1248 105.8391 _x_197 | .0117951 1.175079 0.01 0.992 -2.291318 2.314908 _x_198 | 2.242454 1.404234 1.60 0.110 -.5097941 4.994702 _x_199 | -7.209719 39.26835 -0.18 0.854 -84.17428 69.75484 _x_200 | 1.12773 1.314607 0.86 0.391 -1.448852 3.704313 _x_201 | 3.315047 1.482306 2.24 0.025 .4097806 6.220313 _x_202 | -5.981547 39.26784 -0.15 0.879 -82.94509 70.982 _x_203 | -7.962718 47.86458 -0.17 0.868 -101.7756 85.85014 _x_204 | -5.152533 47.86585 -0.11 0.914 -98.96787 88.6628 _x_205 | -14.19517 61.89351 -0.23 0.819 -135.5042 107.1139 _x_206 | .1661335 1.205013 0.14 0.890 -2.195648 2.527915 _x_207 | 2.250665 1.444192 1.56 0.119 -.5798994 5.08123 _x_208 | -8.201832 39.2771 -0.21 0.835 -85.18354 68.77987 _x_209 | 1.187504 1.355024 0.88 0.381 -1.468295 3.843302 _x_210 | 3.03573 1.528454 1.99 0.047 .0400152 6.031445 _x_211 | -7.21475 39.2763 -0.18 0.854 -84.19488 69.76538 _x_212 | -5.001646 47.87156 -0.10 0.917 -98.82818 88.82489 _x_213 | -3.625649 47.87235 -0.08 0.940 -97.45373 90.20243 _x_214 | -13.73257 61.90258 -0.22 0.824 -135.0594 107.5942 _x_215 | -.1804664 1.171413 -0.15 0.878 -2.476393 2.11546 _x_216 | 2.033757 1.397921 1.45 0.146 -.7061188 4.773632 _x_217 | -8.518172 39.26648 -0.22 0.828 -85.47905 68.44271 _x_218 | 1.008641 1.309146 0.77 0.441 -1.557238 3.57452 _x_219 | 3.107189 1.475247 2.11 0.035 .2157584 5.998619 _x_220 | -6.992744 39.26614 -0.18 0.859 -83.95295 69.96747 _x_221 | -7.350182 47.86361 -0.15 0.878 -101.1611 86.46076 _x_222 | -4.972133 47.86489 -0.10 0.917 -98.7856 88.84134 _x_223 | -15.01008 61.89193 -0.24 0.808 -136.316 106.2959 _x_224 | 3.209494 .6962562 4.61 0.000 1.844856 4.574131 _x_225 | 2.509985 .1093085 22.96 0.000 2.295745 2.724226 _x_226 | 6.946866 .1231528 56.41 0.000 6.705491 7.188241 _x_227 | 3.159108 .1089526 29.00 0.000 2.945564 3.372651 _x_228 | 1.309479 .7252085 1.81 0.071 -.111904 2.730861 _x_229 | 1.15543 .7151916 1.62 0.106 -.2463195 2.55718 _x_230 | .8837832 .7143335 1.24 0.216 -.5162847 2.283851 _x_231 | -.5486555 .1283 -4.28 0.000 -.8001189 -.2971921 _x_232 | -.7725324 .1331611 -5.80 0.000 -1.033523 -.5115415 _x_233 | -.9295628 .1642882 -5.66 0.000 -1.251562 -.6075638 _x_234 | -.0281725 .1456745 -0.19 0.847 -.3136892 .2573442 _x_235 | -.2463211 .1535255 -1.60 0.109 -.5472257 .0545834 _x_236 | .297592 .1975985 1.51 0.132 -.089694 .684878 _x_237 | -.1138934 .1273436 -0.89 0.371 -.3634822 .1356954 _x_238 | -.5030708 .1316589 -3.82 0.000 -.7611175 -.2450241 _x_239 | -.5567342 .1591507 -3.50 0.000 -.8686638 -.2448045 _cons | -4.275213 .6577115 -6.50 0.000 -5.564304 -2.986122 ------------------------------------------------------------------------------ . *The fact that some of the coefficients have no standard errors, and other coefficients have big standar > d errors, is a sign that not all has gone well in the modeling and convergence process. These are not n > ecessarily fatal problems, but they may be problems. . poisgof Goodness-of-fit chi2 = 738.8964 Prob > chi2(272) = 0.0000 . poisgof, pearson Goodness-of-fit chi2 = 718.8111 Prob > chi2(272) = 0.0000 . *The LRT goodness of fit, and the pearson goodness of fit come out not too far apart, which is a good si > gn. . desrep ------------------------------------------------------------------------------- poisson ------------------------------------------------------------------------------- Dependent variable count Number of observations: 512 Initial log likelihood: -1402408.286 Log likelihood: -1467.948 LR chi square: 2801880.677 Model degrees of freedom: 239 Pseudo R-squared: 0.999 Prob: 0.000 ------------------------------------------------------------------------------- nr Effect Coeff s.e. ------------------------------------------------------------------------------- count mfulleth 1 Hisp 5.449** 0.586 2 black 3.487** 0.592 3 white 5.878** 0.590 med4 4 2 0.219 0.820 5 3 0.266 0.904 6 4 -0.880 1.389 mfulleth.med4 7 Hisp.2 -0.898 0.705 8 Hisp.3 -2.154** 0.711 9 Hisp.4 -1.993 1.228 10 black.2 -0.559 0.714 11 black.3 -1.667* 0.730 12 black.4 -3.341* 1.311 13 white.2 -0.586 0.709 14 white.3 -1.897** 0.714 15 white.4 -1.487 1.223 fed4 16 2 1.204 0.782 17 3 -9.473 59.157 18 4 0.616 1.267 mfulleth.fed4 19 Hisp.2 -1.564* 0.662 20 Hisp.3 7.170 59.150 21 Hisp.4 -2.341 1.284 22 black.2 -1.246 0.673 23 black.3 7.838 59.150 24 black.4 -1.743 1.373 25 white.2 -1.167 0.661 26 white.3 7.338 59.150 27 white.4 -1.725 1.268 med4.fed4 28 2.2 1.622 0.931 29 2.3 11.732 59.160 30 2.4 -0.226 1.457 31 3.2 1.254 1.011 32 3.3 12.936 59.161 33 3.4 2.115 1.421 34 4.2 1.266 1.492 35 4.3 14.051 59.170 36 4.4 4.940** 1.761 mfulleth.med4.fed4 37 Hisp.2.2 0.808 0.781 38 Hisp.2.3 -8.411 59.152 39 Hisp.2.4 1.069 1.459 40 Hisp.3.2 1.628* 0.795 41 Hisp.3.3 -7.832 59.151 42 Hisp.3.4 1.623 1.366 43 Hisp.4.2 0.438 1.290 44 Hisp.4.3 -8.715 59.160 45 Hisp.4.4 0.657 1.681 46 black.2.2 0.601 0.795 47 black.2.3 -8.709 59.152 48 black.2.4 1.141 1.548 49 black.3.2 1.331 0.818 50 black.3.3 -8.361 59.152 51 black.3.4 1.393 1.461 52 black.4.2 2.058 1.377 53 black.4.3 -7.491 59.162 54 black.4.4 1.780 1.807 55 white.2.2 0.873 0.780 56 white.2.3 -8.065 59.152 57 white.2.4 1.410 1.440 58 white.3.2 1.819* 0.791 59 white.3.3 -7.297 59.151 60 white.3.4 1.979 1.348 61 white.4.2 0.856 1.278 62 white.4.3 -7.871 59.160 63 white.4.4 1.167 1.660 year 64 90 0.290 0.911 mfulleth.year 65 Hisp.90 -1.145 0.768 66 black.90 -0.994 0.783 67 white.90 -0.815 0.767 med4.year 68 2.90 -0.139 1.152 69 3.90 -0.890 1.345 70 4.90 -14.475 69.171 mfulleth.med4.year 71 Hisp.2.90 0.377 0.950 72 Hisp.3.90 2.278* 1.140 73 Hisp.4.90 7.187 49.985 74 black.2.90 0.286 0.973 75 black.3.90 2.125 1.172 76 black.4.90 9.528 49.991 77 white.2.90 0.159 0.947 78 white.3.90 2.229* 1.136 79 white.4.90 7.835 49.984 fed4.year 80 2.90 -2.671 1.407 81 3.90 9.013 59.170 82 4.90 -15.264 63.214 mfulleth.fed4.year 83 Hisp.2.90 2.088 1.095 84 Hisp.3.90 -6.544 59.161 85 Hisp.4.90 8.148 49.568 86 black.2.90 1.909 1.115 87 black.3.90 -7.053 59.161 88 black.4.90 7.207 49.574 89 white.2.90 1.784 1.092 90 white.3.90 -6.628 59.161 91 white.4.90 7.536 49.567 med4.fed4.year 92 2.2.90 0.730 1.610 93 2.4.90 -1.850 39.277 94 3.2.90 1.697 1.767 95 3.3.90 -9.464 59.179 96 3.4.90 15.115 63.222 97 4.2.90 16.002 69.183 98 4.3.90 3.776 91.022 99 4.4.90 29.004 93.701 mfulleth.med4.fed4.year 100 Hisp.2.2.90 -0.764 1.268 101 Hisp.2.3.90 7.896 59.165 102 Hisp.2.4.90 9.680** 1.032 103 Hisp.3.2.90 -3.165* 1.418 104 Hisp.3.3.90 5.919 59.167 105 Hisp.3.4.90 -8.893 49.577 106 Hisp.4.2.90 -8.686 49.994 107 Hisp.4.3.90 1.158 77.447 108 Hisp.4.4.90 -14.168 70.392 109 black.2.2.90 -0.527 1.296 110 black.2.3.90 8.076 59.166 111 black.2.4.90 10.073 . 112 black.3.2.90 -2.903* 1.455 113 black.3.3.90 6.357 59.168 114 black.3.4.90 -8.035 49.583 115 black.4.2.90 -11.424 50.000 116 black.4.3.90 -0.805 77.451 117 black.4.4.90 -15.822 70.399 118 white.2.2.90 -0.558 1.264 119 white.2.3.90 7.850 59.164 120 white.2.4.90 10.175** 0.969 121 white.3.2.90 -3.122* 1.413 122 white.3.3.90 5.638 59.167 123 white.3.4.90 -8.730 49.575 124 white.4.2.90 -9.284 49.992 125 white.4.3.90 0.240 77.446 126 white.4.4.90 -14.416 70.390 ffulleth 127 Hisp 3.959** 0.340 128 black 1.457** 0.356 129 white 4.899** 0.342 ffulleth.med4 130 Hisp.2 0.573 0.518 131 Hisp.3 0.531 0.702 132 Hisp.4 -0.715 1.133 133 black.2 0.198 0.538 134 black.3 -0.049 0.727 135 black.4 0.283 1.216 136 white.2 0.468 0.521 137 white.3 0.467 0.703 138 white.4 -0.490 1.124 ffulleth.fed4 139 Hisp.2 -0.244 0.479 140 Hisp.3 0.026 1.059 141 Hisp.4 -2.561** 0.743 142 black.2 0.107 0.493 143 black.3 0.419 1.074 144 black.4 -2.133* 0.885 145 white.2 0.079 0.478 146 white.3 0.306 1.056 147 white.4 -2.650** 0.713 ffulleth.med4.fed4 148 Hisp.2.2 -1.052 0.629 149 Hisp.2.3 -1.833 1.144 150 Hisp.2.4 0.306 0.946 151 Hisp.3.2 -1.213 0.793 152 Hisp.3.3 -1.967 1.229 153 Hisp.3.4 -0.243 0.981 154 Hisp.4.2 0.614 1.234 155 Hisp.4.3 -1.195 1.521 156 Hisp.4.4 0.471 1.315 157 black.2.2 -0.925 0.646 158 black.2.3 -1.624 1.164 159 black.2.4 0.907 1.077 160 black.3.2 -1.022 0.818 161 black.3.3 -1.604 1.252 162 black.3.4 0.312 1.108 163 black.4.2 -1.499 1.322 164 black.4.3 -2.814 1.596 165 black.4.4 -0.496 1.469 166 white.2.2 -0.954 0.627 167 white.2.3 -1.679 1.141 168 white.2.4 1.260 0.915 169 white.3.2 -0.971 0.790 170 white.3.3 -1.712 1.225 171 white.3.4 0.693 0.954 172 white.4.2 0.705 1.221 173 white.4.3 -0.898 1.508 174 white.4.4 1.628 1.286 ffulleth.year 175 Hisp.90 -0.015 0.564 176 black.90 -0.335 0.583 177 white.90 0.011 0.562 ffulleth.med4.year 178 Hisp.2.90 -0.231 0.833 179 Hisp.3.90 -0.913 0.984 180 Hisp.4.90 7.301 47.853 181 black.2.90 -0.217 0.859 182 black.3.90 -0.759 1.021 183 black.4.90 5.295 47.858 184 white.2.90 -0.185 0.830 185 white.3.90 -0.929 0.979 186 white.4.90 6.617 47.852 ffulleth.fed4.year 187 Hisp.2.90 0.815 0.970 188 Hisp.3.90 -1.456 1.225 189 Hisp.4.90 7.351 39.258 190 black.2.90 0.577 0.992 191 black.3.90 -1.328 1.256 192 black.4.90 8.499 39.266 193 white.2.90 0.852 0.967 194 white.3.90 -1.372 1.220 195 white.4.90 8.393 39.257 med4.fed4.year 196 2.3.90 -10.143 59.176 ffulleth.med4.fed4.year 197 Hisp.2.2.90 0.012 1.175 198 Hisp.2.3.90 2.242 1.404 199 Hisp.2.4.90 -7.210 39.268 200 Hisp.3.2.90 1.128 1.315 201 Hisp.3.3.90 3.315* 1.482 202 Hisp.3.4.90 -5.982 39.268 203 Hisp.4.2.90 -7.963 47.865 204 Hisp.4.3.90 -5.153 47.866 205 Hisp.4.4.90 -14.195 61.894 206 black.2.2.90 0.166 1.205 207 black.2.3.90 2.251 1.444 208 black.2.4.90 -8.202 39.277 209 black.3.2.90 1.188 1.355 210 black.3.3.90 3.036* 1.528 211 black.3.4.90 -7.215 39.276 212 black.4.2.90 -5.002 47.872 213 black.4.3.90 -3.626 47.872 214 black.4.4.90 -13.733 61.903 215 white.2.2.90 -0.180 1.171 216 white.2.3.90 2.034 1.398 217 white.2.4.90 -8.518 39.266 218 white.3.2.90 1.009 1.309 219 white.3.3.90 3.107* 1.475 220 white.3.4.90 -6.993 39.266 221 white.4.2.90 -7.350 47.864 222 white.4.3.90 -4.972 47.865 223 white.4.4.90 -15.010 61.892 inmarry 224 1 3.209** 0.696 225 2 2.510** 0.109 226 3 6.947** 0.123 227 4 3.159** 0.109 inmarry.med4 228 1.2 1.309 0.725 229 1.3 1.155 0.715 230 1.4 0.884 0.714 231 2.2 -0.549** 0.128 232 2.3 -0.773** 0.133 233 2.4 -0.930** 0.164 234 3.2 -0.028 0.146 235 3.3 -0.246 0.154 236 3.4 0.298 0.198 237 4.2 -0.114 0.127 238 4.3 -0.503** 0.132 239 4.4 -0.557** 0.159 240 _cons -4.275** 0.658 ------------------------------------------------------------------------------- * p < .05 ** p < .01 . *What can be done? . *one thing that can be done if stata is not converging fast enough on the ML solution, is to use a diffe > rent maximization strategy . poisson count _x*, difficult Iteration 0: log likelihood = -12769785 (not concave) Iteration 1: log likelihood = -8606569.3 (not concave) Iteration 2: log likelihood = -6723271 (not concave) Iteration 3: log likelihood = -3817302.4 (not concave) Iteration 4: log likelihood = -2212493 (not concave) Iteration 5: log likelihood = -1828306.6 Iteration 6: log likelihood = -1823717.2 (backed up) Iteration 7: log likelihood = -1815264.3 (backed up) Iteration 8: log likelihood = -1750989.8 (backed up) Iteration 9: log likelihood = -968710.23 (backed up) Iteration 10: log likelihood = -774860.09 Iteration 11: log likelihood = -264457.42 Iteration 12: log likelihood = -32124.484 Iteration 13: log likelihood = -8094.5472 Iteration 14: log likelihood = -2232.2108 Iteration 15: log likelihood = -1524.1091 Iteration 16: log likelihood = -1473.3158 Iteration 17: log likelihood = -1468.05 Iteration 18: log likelihood = -1467.9473 Iteration 19: log likelihood = -1467.9458 (not concave) Iteration 20: log likelihood = -1467.9457 (not concave) Poisson regression Number of obs = 512 LR chi2(239) = 2801880.68 Prob > chi2 = 0.0000 Log likelihood = -1467.9457 Pseudo R2 = 0.9990 ------------------------------------------------------------------------------ count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 5.448842 .5854329 9.31 0.000 4.301415 6.596269 _x_2 | 3.486754 .5914697 5.90 0.000 2.327494 4.646013 _x_3 | 5.87897 .5894137 9.97 0.000 4.72374 7.034199 _x_4 | .2208218 .8194933 0.27 0.788 -1.385356 1.826999 _x_5 | .2665535 .9043372 0.29 0.768 -1.505915 2.039022 _x_6 | -.880277 1.389095 -0.63 0.526 -3.602853 1.842299 _x_7 | -.8979871 .7044135 -1.27 0.202 -2.278612 .482638 _x_8 | -2.153938 .7110074 -3.03 0.002 -3.547486 -.7603888 _x_9 | -1.992881 1.228178 -1.62 0.105 -4.400066 .414303 _x_10 | -.5591931 .7138785 -0.78 0.433 -1.958369 .8399831 _x_11 | -1.666817 .7297652 -2.28 0.022 -3.097131 -.2365035 _x_12 | -3.339366 1.311161 -2.55 0.011 -5.909195 -.7695372 _x_13 | -.5868745 .7084807 -0.83 0.407 -1.975471 .8017221 _x_14 | -1.897559 .7139334 -2.66 0.008 -3.296843 -.498275 _x_15 | -1.487361 1.223145 -1.22 0.224 -3.884682 .9099589 _x_16 | 1.203237 .7820577 1.54 0.124 -.3295681 2.736042 _x_17 | -10.72357 110.5804 -0.10 0.923 -227.4572 206.01 _x_18 | .613593 1.266638 0.48 0.628 -1.868972 3.096158 _x_19 | -1.562984 .6621086 -2.36 0.018 -2.860693 -.265275 _x_20 | 8.421872 110.5764 0.08 0.939 -208.3038 225.1475 _x_21 | -2.339046 1.284067 -1.82 0.069 -4.855772 .1776787 _x_22 | -1.244847 .6725551 -1.85 0.064 -2.563031 .0733363 _x_23 | 9.087706 110.5765 0.08 0.934 -207.6383 225.8137 _x_24 | -1.741181 1.372827 -1.27 0.205 -4.431871 .9495099 _x_25 | -1.166126 .6607803 -1.76 0.078 -2.461232 .1289793 _x_26 | 8.589637 110.5763 0.08 0.938 -208.136 225.3153 _x_27 | -1.723229 1.267473 -1.36 0.174 -4.207431 .7609727 _x_28 | 1.622186 .9308722 1.74 0.081 -.2022902 3.446662 _x_29 | 12.98244 110.5817 0.12 0.907 -203.7536 229.7185 _x_30 | -.2238355 1.456404 -0.15 0.878 -3.078335 2.630664 _x_31 | 1.255001 1.011126 1.24 0.215 -.7267706 3.236772 _x_32 | 14.18659 110.5822 0.13 0.898 -202.5505 230.9237 _x_33 | 2.117649 1.420237 1.49 0.136 -.665965 4.901263 _x_34 | 1.268205 1.492555 0.85 0.395 -1.657149 4.193559 _x_35 | 15.30398 110.5872 0.14 0.890 -201.443 232.0509 _x_36 | 4.943819 1.76108 2.81 0.005 1.492165 8.395473 _x_37 | .8073091 .7813059 1.03 0.301 -.7240223 2.338641 _x_38 | -9.662625 110.5773 -0.09 0.930 -226.3901 207.0649 _x_39 | 1.067305 1.458985 0.73 0.464 -1.792252 3.926863 _x_40 | 1.627188 .7946896 2.05 0.041 .069625 3.184751 _x_41 | -9.084436 110.5772 -0.08 0.935 -225.8117 207.6428 _x_42 | 1.620334 1.365977 1.19 0.236 -1.056932 4.297601 _x_43 | .4357003 1.290362 0.34 0.736 -2.093362 2.964763 _x_44 | -9.96799 110.5817 -0.09 0.928 -226.7042 206.7682 _x_45 | .6535757 1.681295 0.39 0.697 -2.641701 3.948853 _x_46 | .6008636 .7954094 0.76 0.450 -.9581103 2.159837 _x_47 | -9.959037 110.5775 -0.09 0.928 -226.6869 206.7689 _x_48 | 1.139287 1.548257 0.74 0.462 -1.89524 4.173814 _x_49 | 1.330606 .818344 1.63 0.104 -.2733185 2.934531 _x_50 | -9.611525 110.5774 -0.09 0.931 -226.3393 207.1163 _x_51 | 1.390522 1.460574 0.95 0.341 -1.472151 4.253195 _x_52 | 2.055482 1.377217 1.49 0.136 -.6438129 4.754777 _x_53 | -8.742236 110.5828 -0.08 0.937 -225.4806 207.9961 _x_54 | 1.776667 1.807551 0.98 0.326 -1.766068 5.319402 _x_55 | .8722162 .7794632 1.12 0.263 -.6555036 2.399936 _x_56 | -9.316305 110.5772 -0.08 0.933 -226.0437 207.4111 _x_57 | 1.407947 1.439641 0.98 0.328 -1.413699 4.229592 _x_58 | 1.818304 .7911954 2.30 0.022 .2675895 3.369018 _x_59 | -8.549134 110.5771 -0.08 0.938 -225.2763 208.1781 _x_60 | 1.976516 1.347643 1.47 0.142 -.6648166 4.617849 _x_61 | .8541972 1.278327 0.67 0.504 -1.651278 3.359672 _x_62 | -9.124281 110.5816 -0.08 0.934 -225.8602 207.6116 _x_63 | 1.16412 1.6604 0.70 0.483 -2.090204 4.418445 _x_64 | .2897992 .910966 0.32 0.750 -1.495661 2.07526 _x_65 | -1.144705 .7681988 -1.49 0.136 -2.650347 .3609372 _x_66 | -.9943135 .7828272 -1.27 0.204 -2.528627 .5399997 _x_67 | -.8150896 .7667877 -1.06 0.288 -2.317966 .6877867 _x_68 | -.1384998 1.151626 -0.12 0.904 -2.395645 2.118645 _x_69 | -.8887258 1.344338 -0.66 0.509 -3.523579 1.746127 _x_70 | -22.12836 358.1551 -0.06 0.951 -724.0995 679.8428 _x_71 | .3766701 .9498741 0.40 0.692 -1.485049 2.238389 _x_72 | 2.276232 1.140013 2.00 0.046 .0418474 4.510617 _x_73 | 11.03926 343.1508 0.03 0.974 -661.5239 683.6024 _x_74 | .2856185 .9728893 0.29 0.769 -1.621209 2.192446 _x_75 | 2.123721 1.171551 1.81 0.070 -.1724765 4.419919 _x_76 | 13.37972 343.1515 0.04 0.969 -659.1849 685.9444 _x_77 | .158952 .9473318 0.17 0.867 -1.697784 2.015688 _x_78 | 2.226995 1.135919 1.96 0.050 .0006354 4.453355 _x_79 | 11.6878 343.1506 0.03 0.973 -660.8751 684.2507 _x_80 | -2.668965 1.406711 -1.90 0.058 -5.426067 .0881377 _x_81 | 10.26404 110.5872 0.09 0.926 -206.4829 227.011 _x_82 | -18.9028 151.0934 -0.13 0.900 -315.0405 277.2349 _x_83 | 2.08716 1.094428 1.91 0.057 -.0578791 4.232199 _x_84 | -7.795899 110.5822 -0.07 0.944 -224.533 208.9412 _x_85 | 9.759003 110.9407 0.09 0.930 -207.6808 227.1988 _x_86 | 1.908377 1.114742 1.71 0.087 -.2764768 4.093231 _x_87 | -8.302599 110.5826 -0.08 0.940 -225.0405 208.4353 _x_88 | 8.816296 110.9434 0.08 0.937 -208.6288 226.2614 _x_89 | 1.782627 1.091944 1.63 0.103 -.3575432 3.922798 _x_90 | -7.879837 110.5821 -0.07 0.943 -224.6168 208.8572 _x_91 | 9.145964 110.9402 0.08 0.934 -208.2928 226.5847 _x_92 | .7280907 1.609881 0.45 0.651 -2.427219 3.8834 _x_93 | .8277494 102.591 0.01 0.994 -200.247 201.9025 _x_94 | 1.693843 1.766035 0.96 0.337 -1.767522 5.155207 _x_95 | -10.71582 110.5919 -0.10 0.923 -227.4719 206.0402 _x_96 | 18.75229 151.097 0.12 0.901 -277.3924 314.897 _x_97 | 23.65309 358.1574 0.07 0.947 -678.3225 725.6287 _x_98 | 10.17861 374.8385 0.03 0.978 -724.4914 744.8486 _x_99 | 40.29683 360.6376 0.11 0.911 -666.5399 747.1335 _x_100 | -.7624906 1.267521 -0.60 0.547 -3.246787 1.721806 _x_101 | 9.14853 110.5842 0.08 0.934 -207.5926 225.8897 _x_102 | 9.031144 1.032261 8.75 0.000 7.007949 11.05434 _x_103 | -3.162639 1.41745 -2.23 0.026 -5.940791 -.3844878 _x_104 | 7.172519 110.5856 0.06 0.948 -209.5713 223.9163 _x_105 | -10.50227 110.9445 -0.09 0.925 -227.9494 206.9449 _x_106 | -12.53757 343.152 -0.04 0.971 -685.1031 660.0279 _x_107 | -1.442206 360.5279 -0.00 0.997 -708.064 705.1796 _x_108 | -19.63173 360.6379 -0.05 0.957 -726.469 687.2056 _x_109 | -.5261659 1.295457 -0.41 0.685 -3.065215 2.012883 _x_110 | 9.325671 110.5848 0.08 0.933 -207.4165 226.0678 _x_111 | 9.426078 . . . . . _x_112 | -2.900777 1.45426 -1.99 0.046 -5.751074 -.0504805 _x_113 | 7.608556 110.5862 0.07 0.945 -209.1365 224.3536 _x_114 | -9.64214 110.9475 -0.09 0.931 -227.0953 207.811 _x_115 | -15.27554 343.1529 -0.04 0.964 -687.8428 657.2918 _x_116 | -3.4075 360.5288 -0.01 0.992 -710.0309 703.2159 _x_117 | -21.28315 360.6394 -0.06 0.953 -728.1234 685.5572 _x_118 | -.5566388 1.26394 -0.44 0.660 -3.033915 1.920638 _x_119 | 9.101695 110.5842 0.08 0.934 -207.6393 225.8427 _x_120 | 9.526773 .9693475 9.83 0.000 7.626887 11.42666 _x_121 | -3.119473 1.412182 -2.21 0.027 -5.887299 -.3516463 _x_122 | 6.891409 110.5855 0.06 0.950 -209.8522 223.635 _x_123 | -10.33805 110.9439 -0.09 0.926 -227.7841 207.108 _x_124 | -13.13617 343.1518 -0.04 0.969 -685.7013 659.429 _x_125 | -2.360339 360.5278 -0.01 0.995 -708.9818 704.2611 _x_126 | -19.87927 360.6376 -0.06 0.956 -726.716 686.9574 _x_127 | 3.959304 .3395704 11.66 0.000 3.293759 4.62485 _x_128 | 1.456769 .3556317 4.10 0.000 .7597439 2.153794 _x_129 | 4.899826 .3416369 14.34 0.000 4.23023 5.569422 _x_130 | .5718325 .5184519 1.10 0.270 -.4443146 1.58798 _x_131 | .5303223 .7018797 0.76 0.450 -.8453366 1.905981 _x_132 | -.7152556 1.133662 -0.63 0.528 -2.937192 1.50668 _x_133 | .1980946 .537574 0.37 0.713 -.8555311 1.25172 _x_134 | -.0487518 .7274842 -0.07 0.947 -1.474595 1.377091 _x_135 | .283714 1.216285 0.23 0.816 -2.10016 2.667588 _x_136 | .4663141 .5205298 0.90 0.370 -.5539055 1.486534 _x_137 | .4660651 .7026423 0.66 0.507 -.9110885 1.843219 _x_138 | -.489542 1.124829 -0.44 0.663 -2.694167 1.715083 _x_139 | -.2440144 .4792488 -0.51 0.611 -1.183325 .6952961 _x_140 | .0240741 1.05835 0.02 0.982 -2.050253 2.098401 _x_141 | -2.560806 .7430534 -3.45 0.001 -4.017164 -1.104448 _x_142 | .1067904 .4932481 0.22 0.829 -.8599582 1.073539 _x_143 | .4193681 1.073444 0.39 0.696 -1.684544 2.52328 _x_144 | -2.132661 .8852673 -2.41 0.016 -3.867753 -.3975685 _x_145 | .0785768 .4775698 0.16 0.869 -.8574428 1.014596 _x_146 | .3047036 1.055567 0.29 0.773 -1.764169 2.373577 _x_147 | -2.649255 .7126686 -3.72 0.000 -4.04606 -1.25245 _x_148 | -1.051589 .6285978 -1.67 0.094 -2.283618 .1804401 _x_149 | -1.831863 1.14397 -1.60 0.109 -4.074002 .4102759 _x_150 | .3060227 .94632 0.32 0.746 -1.54873 2.160776 _x_151 | -1.212731 .79335 -1.53 0.126 -2.767669 .3422063 _x_152 | -1.966039 1.22867 -1.60 0.110 -4.374189 .4421098 _x_153 | -.2436184 .9811875 -0.25 0.804 -2.166711 1.679474 _x_154 | .6134067 1.234656 0.50 0.619 -1.806475 3.033289 _x_155 | -1.19476 1.520459 -0.79 0.432 -4.174805 1.785284 _x_156 | .4700296 1.31588 0.36 0.721 -2.109048 3.049108 _x_157 | -.92481 .645784 -1.43 0.152 -2.190523 .3409033 _x_158 | -1.624166 1.163315 -1.40 0.163 -3.904221 .6558894 _x_159 | .9061073 1.076793 0.84 0.400 -1.204367 3.016582 _x_160 | -1.022575 .8174995 -1.25 0.211 -2.624845 .5796945 _x_161 | -1.605097 1.251882 -1.28 0.200 -4.058741 .8485465 _x_162 | .3113242 1.107719 0.28 0.779 -1.859766 2.482414 _x_163 | -1.500079 1.322337 -1.13 0.257 -4.091813 1.091655 _x_164 | -2.815004 1.595531 -1.76 0.078 -5.942187 .3121789 _x_165 | -.4978953 1.46915 -0.34 0.735 -3.377376 2.381585 _x_166 | -.9537088 .6264397 -1.52 0.128 -2.181508 .2740905 _x_167 | -1.677419 1.140382 -1.47 0.141 -3.912528 .5576892 _x_168 | 1.259329 .9149814 1.38 0.169 -.534002 3.052659 _x_169 | -.9708053 .7900492 -1.23 0.219 -2.519273 .5776627 _x_170 | -1.711011 1.224744 -1.40 0.162 -4.111465 .6894438 _x_171 | .691901 .9544771 0.72 0.469 -1.17884 2.562642 _x_172 | .7042728 1.220887 0.58 0.564 -1.688621 3.097167 _x_173 | -.898134 1.50813 -0.60 0.551 -3.854014 2.057746 _x_174 | 1.626341 1.286781 1.26 0.206 -.8957035 4.148385 _x_175 | -.0154506 .5635725 -0.03 0.978 -1.120033 1.089131 _x_176 | -.3346116 .5830497 -0.57 0.566 -1.477368 .8081448 _x_177 | .0111698 .5618106 0.02 0.984 -1.089959 1.112298 _x_178 | -.230751 .8327887 -0.28 0.782 -1.862987 1.401485 _x_179 | -.9130638 .9838494 -0.93 0.353 -2.841373 1.015246 _x_180 | 11.1023 102.5827 0.11 0.914 -189.956 212.1606 _x_181 | -.2167575 .8589936 -0.25 0.801 -1.900354 1.466839 _x_182 | -.7587914 1.021273 -0.74 0.457 -2.760449 1.242866 _x_183 | 9.095666 102.5854 0.09 0.929 -191.968 210.1593 _x_184 | -.1852757 .8300595 -0.22 0.823 -1.812162 1.441611 _x_185 | -.9289297 .9793093 -0.95 0.343 -2.848341 .9904813 _x_186 | 10.41795 102.5821 0.10 0.919 -190.6393 211.4752 _x_187 | .8137075 .9697563 0.84 0.401 -1.08698 2.714395 _x_188 | -1.45506 1.224156 -1.19 0.235 -3.854361 .9442422 _x_189 | 9.380122 102.5839 0.09 0.927 -191.6806 210.4409 _x_190 | .5762417 .992204 0.58 0.561 -1.368442 2.520926 _x_191 | -1.328758 1.255863 -1.06 0.290 -3.790205 1.132689 _x_192 | 10.52912 102.5868 0.10 0.918 -190.5373 211.5955 _x_193 | .8513578 .9671387 0.88 0.379 -1.044199 2.746915 _x_194 | -1.370513 1.219154 -1.12 0.261 -3.760011 1.018984 _x_195 | 10.42207 102.5833 0.10 0.919 -190.6375 211.4816 _x_196 | -11.39402 110.5901 -0.10 0.918 -228.1466 205.3586 _x_197 | .0127039 1.174799 0.01 0.991 -2.289859 2.315267 _x_198 | 2.241304 1.403785 1.60 0.110 -.510064 4.992672 _x_199 | -9.238998 102.5877 -0.09 0.928 -210.3073 191.8293 _x_200 | 1.128579 1.314387 0.86 0.391 -1.447573 3.704731 _x_201 | 3.313688 1.481915 2.24 0.025 .4091881 6.218187 _x_202 | -8.011051 102.5875 -0.08 0.938 -209.0789 193.0568 _x_203 | -11.76265 102.5882 -0.11 0.909 -212.8318 189.3065 _x_204 | -8.954822 102.5888 -0.09 0.930 -210.0252 192.1156 _x_205 | -20.02553 .5700743 -35.13 0.000 -21.14285 -18.9082 _x_206 | .1673043 1.20474 0.14 0.890 -2.193942 2.528551 _x_207 | 2.25167 1.443772 1.56 0.119 -.5780705 5.081411 _x_208 | -10.23207 102.5911 -0.10 0.921 -211.3069 190.8428 _x_209 | 1.188277 1.35481 0.88 0.380 -1.467102 3.843656 _x_210 | 3.03612 1.528089 1.99 0.047 .041121 6.031118 _x_211 | -9.245682 102.5908 -0.09 0.928 -210.3199 191.8285 _x_212 | -8.801493 102.5915 -0.09 0.932 -209.8772 192.2742 _x_213 | -7.425881 102.592 -0.07 0.942 -208.5025 193.6507 _x_214 | -19.56417 1.217122 -16.07 0.000 -21.94969 -17.17865 _x_215 | -.1794002 1.171132 -0.15 0.878 -2.474776 2.115976 _x_216 | 2.032896 1.39747 1.45 0.146 -.7060953 4.771888 _x_217 | -10.54757 102.587 -0.10 0.918 -211.6143 190.5192 _x_218 | 1.009786 1.308926 0.77 0.440 -1.555661 3.575233 _x_219 | 3.106209 1.474853 2.11 0.035 .2155495 5.996869 _x_220 | -9.022275 102.5868 -0.09 0.930 -210.0888 192.0442 _x_221 | -11.15032 102.5875 -0.11 0.913 -212.2182 189.9176 _x_222 | -8.774322 102.5882 -0.09 0.932 -209.8435 192.2949 _x_223 | -20.84089 . . . . . _x_224 | 3.215039 .6951872 4.62 0.000 1.852497 4.57758 _x_225 | 2.509815 .109318 22.96 0.000 2.295556 2.724074 _x_226 | 6.947558 .123162 56.41 0.000 6.706165 7.188951 _x_227 | 3.158353 .1089616 28.99 0.000 2.944793 3.371914 _x_228 | 1.303314 .72418 1.80 0.072 -.1160532 2.72268 _x_229 | 1.149326 .7141485 1.61 0.108 -.2503797 2.549031 _x_230 | .8775182 .7132874 1.23 0.219 -.5204995 2.275536 _x_231 | -.5488871 .1283031 -4.28 0.000 -.8003566 -.2974176 _x_232 | -.7724765 .1331653 -5.80 0.000 -1.033476 -.5114773 _x_233 | -.9292498 .1642838 -5.66 0.000 -1.25124 -.6072594 _x_234 | -.0293088 .1456767 -0.20 0.841 -.31483 .2562124 _x_235 | -.2473995 .1535259 -1.61 0.107 -.5483047 .0535058 _x_236 | .2961753 .1975808 1.50 0.134 -.0910759 .6834264 _x_237 | -.112677 .1273462 -0.88 0.376 -.362271 .136917 _x_238 | -.5022029 .1316626 -3.81 0.000 -.7602569 -.2441488 _x_239 | -.5561234 .1591456 -3.49 0.000 -.868043 -.2442038 _cons | -4.275907 .6575594 -6.50 0.000 -5.5647 -2.987114 ------------------------------------------------------------------------------ . poisgof Goodness-of-fit chi2 = 738.8914 Prob > chi2(272) = 0.0000 . *Using difficult will get you to the same model (hopefully), but may get you there faster. It doesn't s > olve the problem of inflated standard errors, or unknown standard errors . *One old school way to deal with zeros was to add 0.5 to every cell . countplus=count+0.l5 unrecognized command: countplus r(199); . gen countplus=count+0.l5 invalid syntax r(198); . gen countplus=count+0.5 . poisson countplus _x*, difficult note: you are responsible for interpretation of non-count dep. variable Iteration 0: log likelihood = -12771949 (not concave) Iteration 1: log likelihood = -8607946 (not concave) Iteration 2: log likelihood = -6725779 (not concave) Iteration 3: log likelihood = -3827917.2 (not concave) Iteration 4: log likelihood = -2220021.8 (not concave) Iteration 5: log likelihood = -1840654.3 (not concave) Iteration 6: log likelihood = -1508429 Iteration 7: log likelihood = -1504946.8 (backed up) Iteration 8: log likelihood = -1489385.4 (backed up) Iteration 9: log likelihood = -1370803.6 (backed up) Iteration 10: log likelihood = -1261578.8 Iteration 11: log likelihood = -237447.38 Iteration 12: log likelihood = -27294.45 Iteration 13: log likelihood = -6973.9805 Iteration 14: log likelihood = -2386.4181 Iteration 15: log likelihood = -1606.2893 Iteration 16: log likelihood = -1554.3432 Iteration 17: log likelihood = -1553.7337 Iteration 18: log likelihood = -1553.7318 Iteration 19: log likelihood = -1553.7318 Poisson regression Number of obs = 512 LR chi2(239) = 2799617.76 Prob > chi2 = 0.0000 Log likelihood = -1553.7318 Pseudo R2 = 0.9989 ------------------------------------------------------------------------------ countplus | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 4.964836 .4536548 10.94 0.000 4.075689 5.853983 _x_2 | 3.017696 .4609056 6.55 0.000 2.114338 3.921054 _x_3 | 5.297085 .4576867 11.57 0.000 4.400035 6.194134 _x_4 | -.0132924 .6816799 -0.02 0.984 -1.34936 1.322776 _x_5 | .226372 .7223827 0.31 0.754 -1.189472 1.642216 _x_6 | .100741 .8572172 0.12 0.906 -1.579374 1.780856 _x_7 | -.6362316 .5712044 -1.11 0.265 -1.755772 .4833084 _x_8 | -1.813538 .5813624 -3.12 0.002 -2.952988 -.6740888 _x_9 | -2.253148 .8238871 -2.73 0.006 -3.867937 -.6383592 _x_10 | -.315335 .5823019 -0.54 0.588 -1.456626 .8259557 _x_11 | -1.351988 .6032888 -2.24 0.025 -2.534412 -.1695637 _x_12 | -3.452733 .9333248 -3.70 0.000 -5.282016 -1.62345 _x_13 | -.2763099 .5752192 -0.48 0.631 -1.403719 .851099 _x_14 | -1.527923 .5840561 -2.62 0.009 -2.672652 -.3831946 _x_15 | -1.85261 .8174137 -2.27 0.023 -3.454711 -.2505083 _x_16 | .9019546 .6551155 1.38 0.169 -.3820482 2.185957 _x_17 | -.4441293 1.002857 -0.44 0.658 -2.409693 1.521435 _x_18 | 1.247326 .8454973 1.48 0.140 -.4098184 2.90447 _x_19 | -1.236656 .5363611 -2.31 0.021 -2.287905 -.1854077 _x_20 | -1.071056 .8485236 -1.26 0.207 -2.734131 .5920199 _x_21 | -2.86691 .8557045 -3.35 0.001 -4.54406 -1.18976 _x_22 | -.9157162 .5483491 -1.67 0.095 -1.990461 .1590283 _x_23 | -.4166345 .8675015 -0.48 0.631 -2.116906 1.283637 _x_24 | -2.295145 .971743 -2.36 0.018 -4.199727 -.3905643 _x_25 | -.840062 .5348276 -1.57 0.116 -1.888305 .2081807 _x_26 | -.9040657 .8449454 -1.07 0.285 -2.560128 .7519968 _x_27 | -2.278488 .8340871 -2.73 0.006 -3.913269 -.6437072 _x_28 | 1.590353 .7937771 2.00 0.045 .034578 3.146127 _x_29 | 2.434511 1.108827 2.20 0.028 .2612497 4.607773 _x_30 | -.7485665 1.043679 -0.72 0.473 -2.79414 1.297007 _x_31 | 1.042069 .8343878 1.25 0.212 -.5933015 2.677439 _x_32 | 3.37407 1.120517 3.01 0.003 1.177898 5.570243 _x_33 | .9970544 .9914218 1.01 0.315 -.9460965 2.940205 _x_34 | .2738612 .990611 0.28 0.782 -1.667701 2.215423 _x_35 | 3.556506 1.212475 2.93 0.003 1.180098 5.932913 _x_36 | 2.828477 1.080605 2.62 0.009 .7105309 4.946423 _x_37 | .6294428 .6522266 0.97 0.335 -.6488977 1.907783 _x_38 | -.0633609 .9412339 -0.07 0.946 -1.908145 1.781424 _x_39 | 1.431478 1.040365 1.38 0.169 -.6075997 3.470556 _x_40 | 1.382414 .6691371 2.07 0.039 .0709297 2.693899 _x_41 | .5050269 .9320551 0.54 0.588 -1.321768 2.331821 _x_42 | 2.195998 .955851 2.30 0.022 .3225644 4.069431 _x_43 | .8183847 .9032688 0.91 0.365 -.9519895 2.588759 _x_44 | .2453646 1.101751 0.22 0.824 -1.914028 2.404757 _x_45 | 1.891747 1.102609 1.72 0.086 -.2693257 4.052821 _x_46 | .4268141 .6682032 0.64 0.523 -.8828401 1.736468 _x_47 | -.344223 .9647061 -0.36 0.721 -2.235012 1.546566 _x_48 | 1.523503 1.15162 1.32 0.186 -.7336302 3.780637 _x_49 | 1.096334 .6958683 1.58 0.115 -.2675431 2.460211 _x_50 | .0047462 .9616845 0.00 0.996 -1.880121 1.889613 _x_51 | 2.004418 1.075295 1.86 0.062 -.103121 4.111957 _x_52 | 2.262257 1.013797 2.23 0.026 .2752511 4.249263 _x_53 | 1.331815 1.199211 1.11 0.267 -1.018596 3.682225 _x_54 | 2.918167 1.270709 2.30 0.022 .4276239 5.40871 _x_55 | .6954802 .650134 1.07 0.285 -.5787591 1.969719 _x_56 | .2834816 .9368114 0.30 0.762 -1.552635 2.119598 _x_57 | 1.787013 1.01614 1.76 0.079 -.2045859 3.778611 _x_58 | 1.576528 .6652238 2.37 0.018 .2727138 2.880343 _x_59 | 1.043841 .9267661 1.13 0.260 -.7725877 2.860269 _x_60 | 2.578893 .9327412 2.76 0.006 .7507537 4.407032 _x_61 | 1.246512 .8885937 1.40 0.161 -.4950998 2.988123 _x_62 | 1.103135 1.088175 1.01 0.311 -1.029648 3.235917 _x_63 | 2.447707 1.075253 2.28 0.023 .3402506 4.555163 _x_64 | .3081423 .742037 0.42 0.678 -1.146223 1.762508 _x_65 | -1.022996 .6104409 -1.68 0.094 -2.219438 .1734466 _x_66 | -.8831786 .6276443 -1.41 0.159 -2.113339 .3469816 _x_67 | -.6947338 .6088034 -1.14 0.254 -1.887967 .4984989 _x_68 | -.0952986 .9604037 -0.10 0.921 -1.977655 1.787058 _x_69 | -.6500703 1.0476 -0.62 0.535 -2.703328 1.403188 _x_70 | -.6750284 1.285687 -0.53 0.600 -3.194928 1.844871 _x_71 | .2826113 .7863378 0.36 0.719 -1.258583 1.823805 _x_72 | 1.691338 .8909776 1.90 0.058 -.054946 3.437622 _x_73 | .2372332 1.264909 0.19 0.851 -2.241943 2.71641 _x_74 | .1980242 .8128494 0.24 0.808 -1.395131 1.79118 _x_75 | 1.547272 .9294686 1.66 0.096 -.2744526 3.368997 _x_76 | 2.250686 1.435103 1.57 0.117 -.5620639 5.063436 _x_77 | .0651625 .7834386 0.08 0.934 -1.470349 1.600674 _x_78 | 1.641935 .8860508 1.85 0.064 -.0946929 3.378562 _x_79 | .7782117 1.230622 0.63 0.527 -1.633764 3.190187 _x_80 | -1.746008 1.082446 -1.61 0.107 -3.867563 .3755464 _x_81 | .5958743 1.295595 0.46 0.646 -1.943445 3.135194 _x_82 | -1.152556 1.277731 -0.90 0.367 -3.656862 1.351749 _x_83 | 1.484465 .8441566 1.76 0.079 -.1700517 3.138981 _x_84 | .9879542 1.114448 0.89 0.375 -1.196323 3.172232 _x_85 | 1.108702 1.239012 0.89 0.371 -1.319716 3.53712 _x_86 | 1.301913 .8688219 1.50 0.134 -.4009462 3.004773 _x_87 | .4996681 1.148539 0.44 0.664 -1.751427 2.750763 _x_88 | .2491323 1.439947 0.17 0.863 -2.573113 3.071377 _x_89 | 1.180358 .8411503 1.40 0.161 -.4682664 2.828982 _x_90 | .9055212 1.109085 0.82 0.414 -1.268246 3.079289 _x_91 | .5230088 1.202645 0.43 0.664 -1.834133 2.88015 _x_92 | -.1422368 1.284355 -0.11 0.912 -2.659526 2.375053 _x_93 | -.0682191 1.648333 -0.04 0.967 -3.298893 3.162455 _x_94 | .6707915 1.36411 0.49 0.623 -2.002815 3.344398 _x_95 | -1.287296 1.50793 -0.85 0.393 -4.242783 1.668192 _x_96 | .7917343 1.507173 0.53 0.599 -2.162271 3.74574 _x_97 | 1.587389 1.612724 0.98 0.325 -1.573492 4.74827 _x_98 | -1.55029 1.6924 -0.92 0.360 -4.867334 1.766753 _x_99 | 1.080631 1.660076 0.65 0.515 -2.173059 4.33432 _x_100 | -.2609422 1.021843 -0.26 0.798 -2.263717 1.741832 _x_101 | .2880663 1.269123 0.23 0.820 -2.19937 2.775502 _x_102 | .7084754 1.609518 0.44 0.660 -2.446121 3.863072 _x_103 | -2.122686 1.107163 -1.92 0.055 -4.292685 .047313 _x_104 | -1.143845 1.30603 -0.88 0.381 -3.703617 1.415927 _x_105 | -1.412906 1.440187 -0.98 0.327 -4.23562 1.409808 _x_106 | -1.271618 1.455657 -0.87 0.382 -4.124653 1.581416 _x_107 | .4110326 1.597854 0.26 0.797 -2.720704 3.542769 _x_108 | -.2980077 1.671428 -0.18 0.858 -3.573946 2.97793 _x_109 | -.022049 1.054778 -0.02 0.983 -2.089377 2.045279 _x_110 | .4523421 1.312165 0.34 0.730 -2.119454 3.024139 _x_111 | 1.029863 1.795734 0.57 0.566 -2.489711 4.549437 _x_112 | -1.860546 1.152023 -1.62 0.106 -4.11847 .3973773 _x_113 | -.7275607 1.355992 -0.54 0.592 -3.385256 1.930135 _x_114 | -.6365622 1.638344 -0.39 0.698 -3.847657 2.574532 _x_115 | -3.648963 1.64103 -2.22 0.026 -6.865323 -.4326036 _x_116 | -1.25834 1.759474 -0.72 0.474 -4.706845 2.190166 _x_117 | -1.709007 1.946852 -0.88 0.380 -5.524767 2.106753 _x_118 | -.0551898 1.017635 -0.05 0.957 -2.049717 1.939337 _x_119 | .2409489 1.262297 0.19 0.849 -2.233108 2.715006 _x_120 | 1.176333 1.572615 0.75 0.454 -1.905937 4.258602 _x_121 | -2.079658 1.100781 -1.89 0.059 -4.23715 .0778332 _x_122 | -1.425216 1.298184 -1.10 0.272 -3.96961 1.119178 _x_123 | -1.274228 1.403681 -0.91 0.364 -4.025392 1.476937 _x_124 | -1.774813 1.418615 -1.25 0.211 -4.555247 1.005621 _x_125 | -.4030384 1.56553 -0.26 0.797 -3.47142 2.665344 _x_126 | -.4671718 1.61743 -0.29 0.773 -3.637277 2.702933 _x_127 | 3.814488 .3081158 12.38 0.000 3.210592 4.418384 _x_128 | 1.375904 .3239963 4.25 0.000 .740883 2.010925 _x_129 | 4.657504 .309379 15.05 0.000 4.051132 5.263876 _x_130 | .4948176 .4641478 1.07 0.286 -.4148954 1.404531 _x_131 | .2014452 .5762238 0.35 0.727 -.9279326 1.330823 _x_132 | -1.294039 .7561504 -1.71 0.087 -2.776067 .1879881 _x_133 | .0903742 .4839453 0.19 0.852 -.8581411 1.03889 _x_134 | -.3869656 .6054312 -0.64 0.523 -1.573589 .7996579 _x_135 | -.4155551 .8649465 -0.48 0.631 -2.110819 1.279709 _x_136 | .4396763 .4656601 0.94 0.345 -.4730008 1.352353 _x_137 | .1694971 .5764464 0.29 0.769 -.9603171 1.299311 _x_138 | -1.192332 .7444478 -1.60 0.109 -2.651423 .2667586 _x_139 | -.2689759 .4321608 -0.62 0.534 -1.115996 .5780437 _x_140 | -.7573638 .6634663 -1.14 0.254 -2.057734 .5430063 _x_141 | -2.614419 .6397909 -4.09 0.000 -3.868386 -1.360452 _x_142 | .0791871 .4465959 0.18 0.859 -.7961248 .9544989 _x_143 | -.3527517 .6859532 -0.51 0.607 -1.697195 .9916918 _x_144 | -2.188338 .7864324 -2.78 0.005 -3.729717 -.6469585 _x_145 | .053778 .4304487 0.12 0.901 -.789886 .897442 _x_146 | -.4807671 .6593406 -0.73 0.466 -1.773051 .8115167 _x_147 | -2.724037 .6096165 -4.47 0.000 -3.918864 -1.529211 _x_148 | -.8430033 .5645343 -1.49 0.135 -1.94947 .2634635 _x_149 | -.887414 .770221 -1.15 0.249 -2.39702 .6221915 _x_150 | .4399715 .8278501 0.53 0.595 -1.182585 2.062528 _x_151 | -.759276 .6673184 -1.14 0.255 -2.067196 .5486439 _x_152 | -.7524507 .8302186 -0.91 0.365 -2.379649 .8747478 _x_153 | .252838 .8234852 0.31 0.759 -1.361163 1.866839 _x_154 | 1.176303 .8749105 1.34 0.179 -.5384905 2.891096 _x_155 | .281566 .9679382 0.29 0.771 -1.615558 2.17869 _x_156 | 1.239564 .9458703 1.31 0.190 -.614308 3.093435 _x_157 | -.7200019 .5826397 -1.24 0.217 -1.861955 .421951 _x_158 | -.694091 .7973252 -0.87 0.384 -2.25682 .8686376 _x_159 | 1.026341 .9623709 1.07 0.286 -.8598714 2.912553 _x_160 | -.5799477 .6946348 -0.83 0.404 -1.941407 .7815115 _x_161 | -.4161913 .8626797 -0.48 0.629 -2.107012 1.27463 _x_162 | .7917181 .9586227 0.83 0.409 -1.087148 2.670584 _x_163 | -.7436695 .9843721 -0.76 0.450 -2.673003 1.185664 _x_164 | -1.17829 1.072264 -1.10 0.272 -3.279889 .9233087 _x_165 | .4169217 1.131591 0.37 0.713 -1.800956 2.634799 _x_166 | -.7451909 .5622781 -1.33 0.185 -1.847236 .3568538 _x_167 | -.7295836 .7652034 -0.95 0.340 -2.229355 .7701875 _x_168 | 1.401431 .7962905 1.76 0.078 -.1592699 2.962132 _x_169 | -.5164712 .6636162 -0.78 0.436 -1.817135 .7841926 _x_170 | -.492143 .8247597 -0.60 0.551 -2.108642 1.124356 _x_171 | 1.206089 .7957508 1.52 0.130 -.3535535 2.765732 _x_172 | 1.304317 .8582246 1.52 0.129 -.3777721 2.986406 _x_173 | .6195886 .9511487 0.65 0.515 -1.244629 2.483806 _x_174 | 2.452292 .9111093 2.69 0.007 .6665502 4.238033 _x_175 | -.1545308 .4893172 -0.32 0.752 -1.113575 .8045132 _x_176 | -.4625163 .5102872 -0.91 0.365 -1.462661 .5376282 _x_177 | -.1274758 .4874718 -0.26 0.794 -1.082903 .8279514 _x_178 | -.1789986 .7219073 -0.25 0.804 -1.593911 1.235914 _x_179 | -.5650164 .8172325 -0.69 0.489 -2.166763 1.03673 _x_180 | .5298647 1.198122 0.44 0.658 -1.818411 2.87814 _x_181 | -.1716734 .7507638 -0.23 0.819 -1.643143 1.299797 _x_182 | -.4188374 .8603679 -0.49 0.626 -2.105127 1.267453 _x_183 | -1.19531 1.391087 -0.86 0.390 -3.92179 1.53117 _x_184 | -.134665 .7189489 -0.19 0.851 -1.543779 1.274449 _x_185 | -.5825845 .8121235 -0.72 0.473 -2.174317 1.009148 _x_186 | -.1243974 1.174924 -0.11 0.916 -2.427207 2.178412 _x_187 | .4939825 .766852 0.64 0.519 -1.00902 1.996985 _x_188 | -.5719656 .8496865 -0.67 0.501 -2.237321 1.093389 _x_189 | .3232552 1.108892 0.29 0.771 -1.850132 2.496643 _x_190 | .2600203 .7934385 0.33 0.743 -1.295091 1.815131 _x_191 | -.4633571 .8921413 -0.52 0.603 -2.211922 1.285208 _x_192 | 1.363454 1.322401 1.03 0.303 -1.228404 3.955313 _x_193 | .5306676 .7637846 0.69 0.487 -.9663227 2.027658 _x_194 | -.4878493 .8429722 -0.58 0.563 -2.140044 1.164346 _x_195 | 1.293276 1.060761 1.22 0.223 -.7857771 3.372329 _x_196 | -1.715344 1.475607 -1.16 0.245 -4.607481 1.176793 _x_197 | .3798479 .9636148 0.39 0.693 -1.508802 2.268498 _x_198 | 1.422483 1.044788 1.36 0.173 -.6252649 3.470231 _x_199 | -.0637003 1.358885 -0.05 0.963 -2.727067 2.599666 _x_200 | 1.10985 1.064853 1.04 0.297 -.977223 3.196922 _x_201 | 2.199982 1.093184 2.01 0.044 .0573807 4.342584 _x_202 | .8142506 1.321627 0.62 0.538 -1.776091 3.404593 _x_203 | -1.019797 1.461481 -0.70 0.485 -3.884247 1.844654 _x_204 | .8459539 1.411132 0.60 0.549 -1.919813 3.611721 _x_205 | -.2633122 1.565804 -0.17 0.866 -3.332232 2.805608 _x_206 | .5314188 .9983258 0.53 0.595 -1.425264 2.488101 _x_207 | 1.44473 1.095419 1.32 0.187 -.7022509 3.591711 _x_208 | -.9554961 1.569117 -0.61 0.543 -4.03091 2.119918 _x_209 | 1.168257 1.112483 1.05 0.294 -1.01217 3.348684 _x_210 | 1.940393 1.15229 1.68 0.092 -.3180538 4.19884 _x_211 | -.311011 1.529805 -0.20 0.839 -3.309374 2.687352 _x_212 | 1.615857 1.660691 0.97 0.331 -1.639039 4.870752 _x_213 | 2.122158 1.604466 1.32 0.186 -1.022537 5.266853 _x_214 | .0266877 1.862962 0.01 0.989 -3.62465 3.678026 _x_215 | .1894308 .9593965 0.20 0.843 -1.690952 2.069813 _x_216 | 1.215038 1.03678 1.17 0.241 -.8170147 3.24709 _x_217 | -1.299643 1.308901 -0.99 0.321 -3.865043 1.265756 _x_218 | .9930672 1.0585 0.94 0.348 -1.081554 3.067688 _x_219 | 1.994484 1.084193 1.84 0.066 -.1304961 4.119464 _x_220 | -.1240069 1.275331 -0.10 0.923 -2.623609 2.375595 _x_221 | -.4472552 1.436679 -0.31 0.756 -3.263094 2.368584 _x_222 | .99605 1.386114 0.72 0.472 -1.720683 3.712783 _x_223 | -1.036687 1.512946 -0.69 0.493 -4.002006 1.928632 _x_224 | 2.960796 .4921936 6.02 0.000 1.996114 3.925478 _x_225 | 2.411712 .1023382 23.57 0.000 2.211133 2.612292 _x_226 | 6.770252 .1163353 58.20 0.000 6.542239 6.998265 _x_227 | 3.256256 .1018712 31.96 0.000 3.056593 3.45592 _x_228 | 1.358103 .5285333 2.57 0.010 .3221964 2.394009 _x_229 | 1.240165 .5166792 2.40 0.016 .2274927 2.252838 _x_230 | .8970955 .5144191 1.74 0.081 -.1111474 1.905338 _x_231 | -.4985 .1214374 -4.10 0.000 -.736513 -.2604871 _x_232 | -.7391458 .1259831 -5.87 0.000 -.9860681 -.4922235 _x_233 | -1.018207 .1521844 -6.69 0.000 -1.316483 -.7199306 _x_234 | .069975 .1388875 0.50 0.614 -.2022395 .3421895 _x_235 | -.1789051 .1462319 -1.22 0.221 -.4655144 .1077041 _x_236 | .1618308 .1836188 0.88 0.378 -.1980555 .5217171 _x_237 | -.1624294 .1203512 -1.35 0.177 -.3983135 .0734546 _x_238 | -.5346892 .1243222 -4.30 0.000 -.7783561 -.2910222 _x_239 | -.4670163 .1466294 -3.19 0.001 -.7544046 -.179628 _cons | -3.549607 .5281492 -6.72 0.000 -4.584761 -2.514454 ------------------------------------------------------------------------------ . *using count+0.5 brings all the SEs down to reasonable size, though whether this better reflects reality > is open to debate . poisgof Goodness-of-fit chi2 = 729.4636 Prob > chi2(272) = 0.0000 . *another strategy for dealing with standard errors which we will talk more about in the future, is robus > t vs. asymptotic SE . poisson count _x*, difficult robust Iteration 0: log likelihood = -12769785 (not concave) Iteration 1: log likelihood = -8606569.3 (not concave) Iteration 2: log likelihood = -6723271 (not concave) Iteration 3: log likelihood = -3817302.4 (not concave) Iteration 4: log likelihood = -2212493 (not concave) Iteration 5: log likelihood = -1828306.6 Iteration 6: log likelihood = -1823717.2 (backed up) Iteration 7: log likelihood = -1815264.3 (backed up) Iteration 8: log likelihood = -1750989.8 (backed up) Iteration 9: log likelihood = -968710.23 (backed up) Iteration 10: log likelihood = -774860.09 Iteration 11: log likelihood = -264457.42 Iteration 12: log likelihood = -32124.484 Iteration 13: log likelihood = -8094.5472 Iteration 14: log likelihood = -2232.2108 Iteration 15: log likelihood = -1524.1091 Iteration 16: log likelihood = -1473.3158 Iteration 17: log likelihood = -1468.05 Iteration 18: log likelihood = -1467.9473 Iteration 19: log likelihood = -1467.9458 (not concave) Iteration 20: log likelihood = -1467.9457 (not concave) Poisson regression Number of obs = 512 Wald chi2(163) = . Prob > chi2 = . Log likelihood = -1467.9457 Pseudo R2 = 0.9990 ------------------------------------------------------------------------------ | Robust count | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _x_1 | 5.448842 .3439793 15.84 0.000 4.774655 6.123029 _x_2 | 3.486754 .336315 10.37 0.000 2.827588 4.145919 _x_3 | 5.87897 .3473395 16.93 0.000 5.198197 6.559743 _x_4 | .2208218 .5289789 0.42 0.676 -.8159577 1.257601 _x_5 | .2665535 .5111529 0.52 0.602 -.7352877 1.268395 _x_6 | -.880277 .9042923 -0.97 0.330 -2.652657 .8921033 _x_7 | -.8979871 .4748753 -1.89 0.059 -1.828726 .0327513 _x_8 | -2.153938 .4157657 -5.18 0.000 -2.968823 -1.339052 _x_9 | -1.992881 .7978806 -2.50 0.012 -3.556699 -.4290642 _x_10 | -.5591931 .4709207 -1.19 0.235 -1.482181 .3637945 _x_11 | -1.666817 .406591 -4.10 0.000 -2.463721 -.8699133 _x_12 | -3.339366 .9708611 -3.44 0.001 -5.242219 -1.436514 _x_13 | -.5868745 .4799699 -1.22 0.221 -1.527598 .3538491 _x_14 | -1.897559 .4149483 -4.57 0.000 -2.710843 -1.084275 _x_15 | -1.487361 .8046901 -1.85 0.065 -3.064525 .0898021 _x_16 | 1.203237 .5981371 2.01 0.044 .0309096 2.375564 _x_17 | -10.72357 .9648711 -11.11 0.000 -12.61468 -8.832458 _x_18 | .613593 .7183125 0.85 0.393 -.7942737 2.02146 _x_19 | -1.562984 .3741228 -4.18 0.000 -2.296251 -.8297167 _x_20 | 8.421872 .8009648 10.51 0.000 6.85201 9.991735 _x_21 | -2.339046 .6793995 -3.44 0.001 -3.670645 -1.007448 _x_22 | -1.244847 .3755964 -3.31 0.001 -1.981003 -.5086919 _x_23 | 9.087706 .8271464 10.99 0.000 7.466529 10.70888 _x_24 | -1.741181 .8854399 -1.97 0.049 -3.476611 -.0057503 _x_25 | -1.166126 .3523401 -3.31 0.001 -1.8567 -.4755524 _x_26 | 8.589637 .7916676 10.85 0.000 7.037997 10.14128 _x_27 | -1.723229 .6566826 -2.62 0.009 -3.010303 -.436155 _x_28 | 1.622186 .6671694 2.43 0.015 .3145578 2.929814 _x_29 | 12.98244 1.006572 12.90 0.000 11.00959 14.95528 _x_30 | -.2238355 .8632941 -0.26 0.795 -1.915861 1.46819 _x_31 | 1.255001 .654365 1.92 0.055 -.0275312 2.537532 _x_32 | 14.18659 1.000531 14.18 0.000 12.22558 16.14759 _x_33 | 2.117649 .7618591 2.78 0.005 .6244328 3.610865 _x_34 | 1.268205 .9951824 1.27 0.203 -.682317 3.218726 _x_35 | 15.30398 1.247242 12.27 0.000 12.85943 17.74853 _x_36 | 4.943819 1.062393 4.65 0.000 2.861568 7.026071 _x_37 | .8073091 .5009829 1.61 0.107 -.1745993 1.789218 _x_38 | -9.662625 .8686378 -11.12 0.000 -11.36512 -7.960126 _x_39 | 1.067305 .919576 1.16 0.246 -.7350305 2.869641 _x_40 | 1.627188 .4558677 3.57 0.000 .7337036 2.520672 _x_41 | -9.084436 .8431285 -10.77 0.000 -10.73694 -7.431934 _x_42 | 1.620334 .7288681 2.22 0.026 .1917789 3.048889 _x_43 | .4357003 .815092 0.53 0.593 -1.161851 2.033251 _x_44 | -9.96799 1.086587 -9.17 0.000 -12.09766 -7.838319 _x_45 | .6535757 .9978876 0.65 0.512 -1.302248 2.609399 _x_46 | .6008636 .512785 1.17 0.241 -.4041766 1.605904 _x_47 | -9.959037 .8940851 -11.14 0.000 -11.71141 -8.206662 _x_48 | 1.139287 1.084715 1.05 0.294 -.9867146 3.265289 _x_49 | 1.330606 .4647917 2.86 0.004 .4196312 2.241581 _x_50 | -9.611525 .8730826 -11.01 0.000 -11.32274 -7.900315 _x_51 | 1.390522 .9438935 1.47 0.141 -.4594751 3.240519 _x_52 | 2.055482 .9894348 2.08 0.038 .1162254 3.994739 _x_53 | -8.742236 1.238229 -7.06 0.000 -11.16912 -6.315351 _x_54 | 1.776667 1.28914 1.38 0.168 -.7500005 4.303335 _x_55 | .8722162 .4807414 1.81 0.070 -.0700197 1.814452 _x_56 | -9.316305 .8508975 -10.95 0.000 -10.98403 -7.648577 _x_57 | 1.407947 .8993099 1.57 0.117 -.3546684 3.170562 _x_58 | 1.818304 .4224229 4.30 0.000 .9903703 2.646238 _x_59 | -8.549134 .8213834 -10.41 0.000 -10.15902 -6.939252 _x_60 | 1.976516 .695526 2.84 0.004 .61331 3.339722 _x_61 | .8541972 .7949586 1.07 0.283 -.703893 2.412287 _x_62 | -9.124281 1.06846 -8.54 0.000 -11.21842 -7.030139 _x_63 | 1.16412 .9729152 1.20 0.231 -.7427584 3.070999 _x_64 | .2897992 .6653179 0.44 0.663 -1.0142 1.593798 _x_65 | -1.144705 .5787397 -1.98 0.048 -2.279014 -.0103958 _x_66 | -.9943135 .586375 -1.70 0.090 -2.143587 .1549604 _x_67 | -.8150896 .5664645 -1.44 0.150 -1.92534 .2951603 _x_68 | -.1384998 .757727 -0.18 0.855 -1.623617 1.346618 _x_69 | -.8887258 1.052347 -0.84 0.398 -2.951288 1.173837 _x_70 | -22.12836 1.369152 -16.16 0.000 -24.81185 -19.44487 _x_71 | .3766701 .6813712 0.55 0.580 -.958793 1.712133 _x_72 | 2.276232 .9868702 2.31 0.021 .3420025 4.210462 _x_73 | 11.03926 1.191816 9.26 0.000 8.703345 13.37518 _x_74 | .2856185 .7108061 0.40 0.688 -1.107536 1.678773 _x_75 | 2.123721 1.021672 2.08 0.038 .1212803 4.126162 _x_76 | 13.37972 1.323369 10.11 0.000 10.78597 15.97348 _x_77 | .158952 .6669229 0.24 0.812 -1.148193 1.466097 _x_78 | 2.226995 .9764606 2.28 0.023 .3131676 4.140823 _x_79 | 11.6878 1.170807 9.98 0.000 9.393061 13.98254 _x_80 | -2.668965 .9624092 -2.77 0.006 -4.555252 -.7826774 _x_81 | 10.26404 1.225909 8.37 0.000 7.861306 12.66678 _x_82 | -18.9028 1.403851 -13.46 0.000 -21.6543 -16.1513 _x_83 | 2.08716 .6956586 3.00 0.003 .7236939 3.450626 _x_84 | -7.795899 1.095056 -7.12 0.000 -9.94217 -5.649628 _x_85 | 9.759003 1.232273 7.92 0.000 7.343791 12.17421 _x_86 | 1.908377 .702381 2.72 0.007 .5317357 3.285019 _x_87 | -8.302599 1.145972 -7.25 0.000 -10.54866 -6.056535 _x_88 | 8.816296 1.349158 6.53 0.000 6.171994 11.4606 _x_89 | 1.782627 .674223 2.64 0.008 .4611743 3.10408 _x_90 | -7.879837 1.065862 -7.39 0.000 -9.968888 -5.790785 _x_91 | 9.145964 1.193116 7.67 0.000 6.807498 11.48443 _x_92 | .7280907 1.125654 0.65 0.518 -1.47815 2.934331 _x_93 | .8277494 1.150959 0.72 0.472 -1.428088 3.083587 _x_94 | 1.693843 1.285098 1.32 0.187 -.8249039 4.212589 _x_95 | -10.71582 1.477589 -7.25 0.000 -13.61184 -7.819795 _x_96 | 18.75229 1.628913 11.51 0.000 15.55968 21.9449 _x_97 | 23.65309 1.591576 14.86 0.000 20.53366 26.77252 _x_98 | 10.17861 1.727256 5.89 0.000 6.793252 13.56397 _x_99 | 40.29683 1.58944 25.35 0.000 37.18159 43.41208 _x_100 | -.7624906 .8714983 -0.87 0.382 -2.470596 .9456147 _x_101 | 9.14853 1.187104 7.71 0.000 6.821849 11.47521 _x_102 | 9.031144 .8273889 10.92 0.000 7.409491 10.6528 _x_103 | -3.162639 1.072743 -2.95 0.003 -5.265177 -1.060102 _x_104 | 7.172519 1.364144 5.26 0.000 4.498846 9.846193 _x_105 | -10.50227 1.488284 -7.06 0.000 -13.41925 -7.585285 _x_106 | -12.53757 1.260575 -9.95 0.000 -15.00825 -10.06688 _x_107 | -1.442206 1.528584 -0.94 0.345 -4.438176 1.553763 _x_108 | -19.63173 1.620074 -12.12 0.000 -22.80702 -16.45645 _x_109 | -.5261659 .9031978 -0.58 0.560 -2.296401 1.244069 _x_110 | 9.325671 1.260847 7.40 0.000 6.854457 11.79689 _x_111 | 9.426078 . . . . . _x_112 | -2.900777 1.116117 -2.60 0.009 -5.088326 -.7132287 _x_113 | 7.608556 1.435956 5.30 0.000 4.794134 10.42298 _x_114 | -9.64214 1.615813 -5.97 0.000 -12.80908 -6.475205 _x_115 | -15.27554 1.425499 -10.72 0.000 -18.06947 -12.48162 _x_116 | -3.4075 1.676293 -2.03 0.042 -6.692973 -.1220263 _x_117 | -21.28315 1.820673 -11.69 0.000 -24.8516 -17.71469 _x_118 | -.5566388 .8512805 -0.65 0.513 -2.225118 1.11184 _x_119 | 9.101695 1.152219 7.90 0.000 6.843387 11.36 _x_120 | 9.526773 .7465309 12.76 0.000 8.0636 10.98995 _x_121 | -3.119473 1.052615 -2.96 0.003 -5.18256 -1.056386 _x_122 | 6.891409 1.335554 5.16 0.000 4.273772 9.509046 _x_123 | -10.33805 1.445713 -7.15 0.000 -13.17159 -7.504501 _x_124 | -13.13617 1.257853 -10.44 0.000 -15.60151 -10.67082 _x_125 | -2.360339 1.490779 -1.58 0.113 -5.282211 .5615329 _x_126 | -19.87927 1.590516 -12.50 0.000 -22.99662 -16.76192 _x_127 | 3.959304 .3302733 11.99 0.000 3.311981 4.606628 _x_128 | 1.456769 .332813 4.38 0.000 .8044676 2.109071 _x_129 | 4.899826 .3123665 15.69 0.000 4.287599 5.512053 _x_130 | .5718325 .3745648 1.53 0.127 -.162301 1.305966 _x_131 | .5303223 .42652 1.24 0.214 -.3056416 1.366286 _x_132 | -.7152556 .8001038 -0.89 0.371 -2.28343 .8529192 _x_133 | .1980946 .398236 0.50 0.619 -.5824337 .9786229 _x_134 | -.0487518 .4535623 -0.11 0.914 -.9377176 .8402139 _x_135 | .283714 .9820869 0.29 0.773 -1.641141 2.208569 _x_136 | .4663141 .3596539 1.30 0.195 -.2385945 1.171223 _x_137 | .4660651 .4124523 1.13 0.258 -.3423265 1.274457 _x_138 | -.489542 .8041433 -0.61 0.543 -2.065634 1.08655 _x_139 | -.2440144 .536583 -0.45 0.649 -1.295698 .807669 _x_140 | .0240741 .6289911 0.04 0.969 -1.208726 1.256874 _x_141 | -2.560806 .4570441 -5.60 0.000 -3.456596 -1.665016 _x_142 | .1067904 .5364518 0.20 0.842 -.9446359 1.158217 _x_143 | .4193681 .6544614 0.64 0.522 -.8633527 1.702089 _x_144 | -2.132661 .7088003 -3.01 0.003 -3.521884 -.7434375 _x_145 | .0785768 .5238476 0.15 0.881 -.9481455 1.105299 _x_146 | .3047036 .6106144 0.50 0.618 -.8920786 1.501486 _x_147 | -2.649255 .4095502 -6.47 0.000 -3.451958 -1.846551 _x_148 | -1.051589 .5719442 -1.84 0.066 -2.172579 .0694011 _x_149 | -1.831863 .6569372 -2.79 0.005 -3.119436 -.5442899 _x_150 | .3060227 .563763 0.54 0.587 -.7989325 1.410978 _x_151 | -1.212731 .6165287 -1.97 0.049 -2.421105 -.0043571 _x_152 | -1.966039 .6934703 -2.84 0.005 -3.325216 -.6068626 _x_153 | -.2436184 .5489853 -0.44 0.657 -1.31961 .832373 _x_154 | .6134067 .9208935 0.67 0.505 -1.191511 2.418325 _x_155 | -1.19476 .9674549 -1.23 0.217 -3.090937 .7014165 _x_156 | .4700296 .8681658 0.54 0.588 -1.231544 2.171603 _x_157 | -.92481 .5822631 -1.59 0.112 -2.066025 .2164047 _x_158 | -1.624166 .6836967 -2.38 0.018 -2.964187 -.2841451 _x_159 | .9061073 .7905668 1.15 0.252 -.6433753 2.45559 _x_160 | -1.022575 .6237072 -1.64 0.101 -2.245019 .1998686 _x_161 | -1.605097 .7261332 -2.21 0.027 -3.028292 -.1819022 _x_162 | .3113242 .7961472 0.39 0.696 -1.249096 1.871744 _x_163 | -1.500079 1.093294 -1.37 0.170 -3.642895 .642737 _x_164 | -2.815004 1.148031 -2.45 0.014 -5.065104 -.5649045 _x_165 | -.4978953 1.193943 -0.42 0.677 -2.837981 1.842191 _x_166 | -.9537088 .5569363 -1.71 0.087 -2.045284 .1378664 _x_167 | -1.677419 .6359975 -2.64 0.008 -2.923951 -.4308872 _x_168 | 1.259329 .5204106 2.42 0.016 .2393426 2.279315 _x_169 | -.9708053 .5942877 -1.63 0.102 -2.135588 .1939772 _x_170 | -1.711011 .6705415 -2.55 0.011 -3.025248 -.3967736 _x_171 | .691901 .4922562 1.41 0.160 -.2729034 1.656705 _x_172 | .7042728 .9101139 0.77 0.439 -1.079518 2.488063 _x_173 | -.898134 .9539829 -0.94 0.346 -2.767906 .971638 _x_174 | 1.626341 .8373609 1.94 0.052 -.0148561 3.267538 _x_175 | -.0154506 .4027727 -0.04 0.969 -.8048706 .7739693 _x_176 | -.3346116 .4136384 -0.81 0.419 -1.145328 .4761047 _x_177 | .0111698 .3883768 0.03 0.977 -.7500347 .7723744 _x_178 | -.230751 .5292495 -0.44 0.663 -1.268061 .8065591 _x_179 | -.9130638 .7494634 -1.22 0.223 -2.381985 .5558574 _x_180 | 11.1023 .7682422 14.45 0.000 9.596572 12.60803 _x_181 | -.2167575 .5666379 -0.38 0.702 -1.327347 .8938324 _x_182 | -.7587914 .7908686 -0.96 0.337 -2.308865 .7912826 _x_183 | 9.095666 .9858736 9.23 0.000 7.163389 11.02794 _x_184 | -.1852757 .513953 -0.36 0.718 -1.192605 .8220536 _x_185 | -.9289297 .7367049 -1.26 0.207 -2.372845 .5149853 _x_186 | 10.41795 .7360569 14.15 0.000 8.975307 11.8606 _x_187 | .8137075 .7659983 1.06 0.288 -.6876217 2.315037 _x_188 | -1.45506 .7505823 -1.94 0.053 -2.926174 .0160548 _x_189 | 9.380122 .8701857 10.78 0.000 7.674589 11.08565 _x_190 | .5762417 .7703504 0.75 0.454 -.9336172 2.086101 _x_191 | -1.328758 .814433 -1.63 0.103 -2.925018 .2675011 _x_192 | 10.52912 1.008025 10.45 0.000 8.553429 12.50481 _x_193 | .8513578 .7478464 1.14 0.255 -.6143943 2.31711 _x_194 | -1.370513 .7079426 -1.94 0.053 -2.758055 .0170286 _x_195 | 10.42207 .7965376 13.08 0.000 8.860888 11.98326 _x_196 | -11.39402 1.308816 -8.71 0.000 -13.95925 -8.82879 _x_197 | .0127039 .8951754 0.01 0.989 -1.741808 1.767215 _x_198 | 2.241304 .9159431 2.45 0.014 .4460884 4.036519 _x_199 | -9.238998 .9844588 -9.38 0.000 -11.1685 -7.309494 _x_200 | 1.128579 1.022113 1.10 0.270 -.8747255 3.131883 _x_201 | 3.313688 1.000606 3.31 0.001 1.352537 5.274839 _x_202 | -8.011051 1.121697 -7.14 0.000 -10.20954 -5.812565 _x_203 | -11.76265 1.111201 -10.59 0.000 -13.94056 -9.584735 _x_204 | -8.954822 1.009172 -8.87 0.000 -10.93276 -6.976881 _x_205 | -20.02553 .5050445 -39.65 0.000 -21.01539 -19.03566 _x_206 | .1673043 .9247682 0.18 0.856 -1.645208 1.979817 _x_207 | 2.25167 .9978316 2.26 0.024 .2959565 4.207385 _x_208 | -10.23207 1.149574 -8.90 0.000 -12.48519 -7.978943 _x_209 | 1.188277 1.063045 1.12 0.264 -.8952536 3.271807 _x_210 | 3.03612 1.085819 2.80 0.005 .9079528 5.164287 _x_211 | -9.245682 1.265388 -7.31 0.000 -11.7258 -6.765567 _x_212 | -8.801493 1.294623 -6.80 0.000 -11.33891 -6.264079 _x_213 | -7.425881 1.230899 -6.03 0.000 -9.838399 -5.013363 _x_214 | -19.56417 .9628916 -20.32 0.000 -21.4514 -17.67694 _x_215 | -.1794002 .8770816 -0.20 0.838 -1.898449 1.539648 _x_216 | 2.032896 .8669243 2.34 0.019 .3337558 3.732036 _x_217 | -10.54757 .9107438 -11.58 0.000 -12.33259 -8.762541 _x_218 | 1.009786 1.000874 1.01 0.313 -.9518919 2.971464 _x_219 | 3.106209 .9606365 3.23 0.001 1.223396 4.989022 _x_220 | -9.022275 1.047998 -8.61 0.000 -11.07631 -6.968236 _x_221 | -11.15032 1.074293 -10.38 0.000 -13.25589 -9.044742 _x_222 | -8.774322 .9551413 -9.19 0.000 -10.64636 -6.902279 _x_223 | -20.84089 . . . . . _x_224 | 3.215039 .4472806 7.19 0.000 2.338385 4.091692 _x_225 | 2.509815 .1236763 20.29 0.000 2.267414 2.752216 _x_226 | 6.947558 .161714 42.96 0.000 6.630604 7.264511 _x_227 | 3.158353 .1325708 23.82 0.000 2.898519 3.418187 _x_228 | 1.303314 .4772338 2.73 0.006 .3679525 2.238675 _x_229 | 1.149326 .4651787 2.47 0.013 .2375921 2.061059 _x_230 | .8775182 .4909077 1.79 0.074 -.0846433 1.83968 _x_231 | -.5488871 .1575656 -3.48 0.000 -.8577101 -.2400641 _x_232 | -.7724765 .1611205 -4.79 0.000 -1.088267 -.4566861 _x_233 | -.9292498 .2354906 -3.95 0.000 -1.390803 -.4676966 _x_234 | -.0293088 .226222 -0.13 0.897 -.4726958 .4140782 _x_235 | -.2473995 .2340363 -1.06 0.290 -.7061022 .2113033 _x_236 | .2961753 .2967643 1.00 0.318 -.2854722 .8778227 _x_237 | -.112677 .1637782 -0.69 0.491 -.4336763 .2083224 _x_238 | -.5022029 .1649297 -3.04 0.002 -.825459 -.1789467 _x_239 | -.5561234 .2216602 -2.51 0.012 -.9905695 -.1216774 _cons | -4.275907 .4391489 -9.74 0.000 -5.136623 -3.415191 ------------------------------------------------------------------------------ . poisgof Goodness-of-fit chi2 = 738.8914 Prob > chi2(348) = 0.0000 . *The robust standard error model has exactly the same coefficients, and therefore exactly the same goodn > ess of fit as the original. Also, the standard errors are different, and smaller in many cases but not > necessarily always smaller. . *In this case, since the model fits reasonably well, the robust SE are not overwhelmingly different from > the ordinarly SE. . exit, clear