--------------------------------------------------------------------------------------------
log: C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2003\class 6.log
log type: text
opened on: 15 Oct 2003, 11:01:46
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 133,710
vars: 41 14 May 2003 11:59
size: 10,563,090 (59.7% 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
edlvl float %16.0g edlabel 4 categories ed attainment
immigrant float %9.0g immigrant_lbl
-------------------------------------------------------------------------------
Sorted by: immigrant
*This is the individual level dataset from the current population survey, which is available on my website.
. set mem 30m
no; data in memory would be lost
r(4);
. *The command for increasing the memory is set mem.
. sort sex
. by sex: tabulate race clswkr
_______________________________________________________________________________
-> sex = male
| sector of worker p109
p25 | not in un private federal g state gov local gov | Total
------------+-------------------------------------------------------+----------
White | 23,700 23,948 758 1,027 1,676 | 55,457
Black | 3,323 2,169 133 104 219 | 6,145
Amer Indian | 490 308 21 12 64 | 930
Asian | 983 965 46 49 46 | 2,259
------------+-------------------------------------------------------+----------
Total | 28,496 27,390 958 1,192 2,005 | 64,791
| sector of worker p109
p25 | self empl self empl unpaid never wor | Total
------------+--------------------------------------------+----------
White | 1,413 2,841 24 70 | 55,457
Black | 42 138 0 17 | 6,145
Amer Indian | 2 31 0 2 | 930
Asian | 59 104 0 7 | 2,259
------------+--------------------------------------------+----------
Total | 1,516 3,114 24 96 | 64,791
_______________________________________________________________________________
-> sex = female
| sector of worker p109
p25 | not in un private federal g state gov local gov | Total
------------+-------------------------------------------------------+----------
White | 30,392 20,541 587 1,357 2,720 | 58,018
Black | 4,022 2,542 158 243 373 | 7,481
Amer Indian | 564 241 28 25 86 | 964
Asian | 1,299 904 32 67 51 | 2,456
------------+-------------------------------------------------------+----------
Total | 36,277 24,228 805 1,692 3,230 | 68,919
| sector of worker p109
p25 | self empl self empl unpaid never wor | Total
------------+--------------------------------------------+----------
White | 544 1,752 40 85 | 58,018
Black | 23 104 2 14 | 7,481
Amer Indian | 4 14 0 2 | 964
Asian | 35 58 5 5 | 2,456
------------+--------------------------------------------+----------
Total | 606 1,928 47 106 | 68,919
. by sex: tabulate race clswkr if age>16 & age<65
_______________________________________________________________________________
-> sex = male
| sector of worker p109
p25 | not in un private federal g state gov local gov | Total
------------+-------------------------------------------------------+----------
White | 4,821 22,974 720 997 1,606 | 34,954
Black | 871 2,096 128 103 211 | 3,586
Amer Indian | 124 297 21 11 60 | 544
Asian | 259 930 44 46 42 | 1,479
------------+-------------------------------------------------------+----------
Total | 6,075 26,297 913 1,157 1,919 | 40,563
| sector of worker p109
p25 | self empl self empl unpaid never wor | Total
------------+--------------------------------------------+----------
White | 1,297 2,498 15 26 | 34,954
Black | 38 129 0 10 | 3,586
Amer Indian | 2 27 0 2 | 544
Asian | 54 98 0 6 | 1,479
------------+--------------------------------------------+----------
Total | 1,391 2,752 15 44 | 40,563
_______________________________________________________________________________
-> sex = female
| sector of worker p109
p25 | not in un private federal g state gov local gov | Total
------------+-------------------------------------------------------+----------
White | 9,608 19,646 569 1,315 2,635 | 35,977
Black | 1,218 2,455 158 239 359 | 4,557
Amer Indian | 173 235 28 23 84 | 563
Asian | 519 880 31 66 51 | 1,647
------------+-------------------------------------------------------+----------
Total | 11,518 23,216 786 1,643 3,129 | 42,744
| sector of worker p109
p25 | self empl self empl unpaid never wor | Total
------------+--------------------------------------------+----------
White | 506 1,613 31 54 | 35,977
Black | 21 95 2 10 | 4,557
Amer Indian | 4 14 0 2 | 563
Asian | 34 57 5 4 | 1,647
------------+--------------------------------------------+----------
Total | 565 1,779 38 70 | 42,744
. contract sex race clswkr if age>16 & age<65, zero
. *contract is the command that moves from individual level data to cross tabulated data
. *The zero option ensures that cells with sampling zeroes are represented in the final data set.
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 72
vars: 4 14 May 2003 11:59
size: 648 (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
clswkr byte %32.0g cwrknm sector of worker p109
_freq int %12.0g Frequency
-------------------------------------------------------------------------------
Sorted by: sex race clswkr
Note: dataset has changed since last saved
. *the new cross tabulated dataset has 72=2*4*9 cells
. rename _freq count
. tabulate count
Frequency | Freq. Percent Cum.
------------+-----------------------------------
0 | 4 5.56 5.56
2 | 4 5.56 11.11
4 | 2 2.78 13.89
5 | 1 1.39 15.28
6 | 1 1.39 16.67
10 | 2 2.78 19.44
11 | 1 1.39 20.83
14 | 1 1.39 22.22
15 | 1 1.39 23.61
21 | 2 2.78 26.39
23 | 1 1.39 27.78
26 | 1 1.39 29.17
27 | 1 1.39 30.56
28 | 1 1.39 31.94
31 | 2 2.78 34.72
34 | 1 1.39 36.11
38 | 1 1.39 37.50
42 | 1 1.39 38.89
44 | 1 1.39 40.28
46 | 1 1.39 41.67
51 | 1 1.39 43.06
54 | 2 2.78 45.83
57 | 1 1.39 47.22
60 | 1 1.39 48.61
66 | 1 1.39 50.00
84 | 1 1.39 51.39
95 | 1 1.39 52.78
98 | 1 1.39 54.17
103 | 1 1.39 55.56
124 | 1 1.39 56.94
128 | 1 1.39 58.33
129 | 1 1.39 59.72
158 | 1 1.39 61.11
173 | 1 1.39 62.50
211 | 1 1.39 63.89
235 | 1 1.39 65.28
239 | 1 1.39 66.67
259 | 1 1.39 68.06
297 | 1 1.39 69.44
359 | 1 1.39 70.83
506 | 1 1.39 72.22
519 | 1 1.39 73.61
569 | 1 1.39 75.00
720 | 1 1.39 76.39
871 | 1 1.39 77.78
880 | 1 1.39 79.17
930 | 1 1.39 80.56
997 | 1 1.39 81.94
1218 | 1 1.39 83.33
1297 | 1 1.39 84.72
1315 | 1 1.39 86.11
1606 | 1 1.39 87.50
1613 | 1 1.39 88.89
2096 | 1 1.39 90.28
2455 | 1 1.39 91.67
2498 | 1 1.39 93.06
2635 | 1 1.39 94.44
4821 | 1 1.39 95.83
9608 | 1 1.39 97.22
19646 | 1 1.39 98.61
22974 | 1 1.39 100.00
------------+-----------------------------------
Total | 72 100.00
. *Some number of zeroes in the dataset is nothing to worry about.
. clear all
. *now let's take a brief look at the homework dataset
. use "C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2002\HW3 dataset with best f
> it vars.dta", clear
. *This is the dataset for homework 3
. drop _x_*
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2002\HW3 dataset
> with best fit vars.dta
obs: 225
vars: 14 24 Oct 2002 11:00
size: 7,650 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
meth str6 %9s
feth str6 %9s
mgen byte %9.0g gen
fgen byte %9.0g gen
year byte %8.0g
count long %12.0g Frequency
ethintdm byte %9.0g
ethintct byte %9.0g
BW byte %9.0g
MOh byte %9.0g
QS byte %9.0g QS
PrettyGood float %9.0g predicted number of events
BOhS byte %9.0g
BWS byte %9.0g
-------------------------------------------------------------------------------
Sorted by:
. tabulate meth [fweight=count]
meth | Freq. Percent Cum.
------------+-----------------------------------
Blk_NH | 45,681 7.03 7.03
Mex_Am | 25,294 3.89 10.92
Oth_H | 11,609 1.79 12.71
Oth_NH | 8,100 1.25 13.96
Wht_NH | 559,137 86.04 100.00
------------+-----------------------------------
Total | 649,821 100.00
. tabulate year [fweight=count]
year | Freq. Percent Cum.
------------+-----------------------------------
70 | 64,903 9.99 9.99
80 | 348,247 53.59 63.58
90 | 236,671 36.42 100.00
------------+-----------------------------------
Total | 649,821 100.00
. tabulate fgen mgen [fweight=count]
| mgen
fgen | foreign US native | Total
-----------+----------------------+----------
foreign | 0 19,166 | 19,166
US native | 19,825 610,830 | 630,655
-----------+----------------------+----------
Total | 19,825 629,996 | 649,821
. *
. *That's a quick look at the HW3 dataset, which has 5 dimensions and one sort of structural zero that I have imposed (foreign born married to foreign born)
. clear all
. *Now I'm going to load a different multidimensional dataset to work with in class.
. use "C:\AAA Miker Files\newer web pages\soc_388_notes\Qian 80-90 intermar.dta", clear
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\Qian 80-90 intermar.dta
obs: 512
vars: 6 16 Oct 2001 11:12
size: 10,752 (99.9% 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
-------------------------------------------------------------------------------
Sorted by: year med4 fed4
. tabulate mfulleth ffulleth [fweight=count]
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 372 43 7 320 | 742
Hisp | 40 15,469 227 7,189 | 22,925
black | 11 459 34,334 1,625 | 36,429
white | 447 6,744 458 455,797 | 463,446
-----------+--------------------------------------------+----------
Total | 870 22,715 35,026 464,931 | 523,542
. tabulate med4 fed4 [fweight=count]
| fed4
med4 | 1 2 3 4 | Total
-----------+--------------------------------------------+----------
1 | 32,016 33,374 8,407 988 | 74,785
2 | 28,370 137,876 43,783 8,446 | 218,475
3 | 7,051 48,766 61,633 18,195 | 135,645
4 | 984 13,794 28,635 51,224 | 94,637
-----------+--------------------------------------------+----------
Total | 68,421 233,810 142,458 78,853 | 523,542
. tabulate year [fweight=count]
year | Freq. Percent Cum.
------------+-----------------------------------
80 | 315,266 60.22 60.22
90 | 208,276 39.78 100.00
------------+-----------------------------------
Total | 523,542 100.00
. gen BW=0
. *The question is, how many cells are there in the dataset with one black spouse and one white spouse?
. replace BW=1 if mfulleth=="white" & ffulleth=="black"
(32 real changes made)
. replace BW=1 if ffulleth=="white" & mfulleth=="black"
(32 real changes made)
*The answer is 64, two in each of the 32 hed4 by wed4 by year tables (4x4x2=32)
. *I'm saying that there are in this dataset 32 different tables,
. *Each with husband's race by wife's race.
. sort year med4 fed4
. by year med4 fed4: tabulate mfulleth ffulleth [fweight=count]
_______________________________________________________________________________
-> year = 80, med4 = 1, fed4 = 1
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 1 0 0 2 | 3
Hisp | 1 2,180 13 341 | 2,535
black | 1 24 2,039 50 | 2,114
white | 6 165 12 15,801 | 15,984
-----------+--------------------------------------------+----------
Total | 9 2,369 2,064 16,194 | 20,636
_______________________________________________________________________________
-> year = 80, med4 = 1, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 1 2 1 6 | 10
Hisp | 0 1,119 10 351 | 1,480
black | 0 15 2,173 59 | 2,247
white | 8 235 17 17,604 | 17,864
-----------+--------------------------------------------+----------
Total | 9 1,371 2,201 18,020 | 21,601
_______________________________________________________________________________
-> year = 80, med4 = 1, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Hisp | 0 204 6 65 | 275
black | 0 3 598 21 | 622
white | 1 43 3 2,509 | 2,556
-----------+--------------------------------------------+----------
Total | 1 250 607 2,595 | 3,453
_______________________________________________________________________________
-> year = 80, med4 = 1, fed4 = 4
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 1 0 0 0 | 1
Hisp | 0 29 0 6 | 35
black | 0 2 78 0 | 80
white | 2 5 1 367 | 375
-----------+--------------------------------------------+----------
Total | 3 36 79 373 | 491
_______________________________________________________________________________
-> year = 80, med4 = 2, fed4 = 1
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 2 0 0 5 | 7
Hisp | 1 1,129 4 311 | 1,445
black | 0 23 1,716 73 | 1,812
white | 4 283 19 15,539 | 15,845
-----------+--------------------------------------------+----------
Total | 7 1,435 1,739 15,928 | 19,109
_______________________________________________________________________________
-> year = 80, med4 = 2, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 30 4 1 26 | 61
Hisp | 7 2,383 31 1,132 | 3,553
black | 3 62 6,734 227 | 7,026
white | 46 1,082 59 81,301 | 82,488
-----------+--------------------------------------------+----------
Total | 86 3,531 6,825 82,686 | 93,128
_______________________________________________________________________________
-> year = 80, med4 = 2, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 16 2 0 9 | 27
Hisp | 3 477 13 257 | 750
black | 1 21 2,054 71 | 2,147
white | 17 257 21 18,173 | 18,468
-----------+--------------------------------------------+----------
Total | 37 757 2,088 18,510 | 21,392
_______________________________________________________________________________
-> year = 80, med4 = 2, fed4 = 4
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 3 1 0 0 | 4
Hisp | 0 45 3 37 | 85
black | 1 3 405 14 | 423
white | 3 37 5 4,161 | 4,206
-----------+--------------------------------------------+----------
Total | 7 86 413 4,212 | 4,718
_______________________________________________________________________________
-> year = 80, med4 = 3, fed4 = 1
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 1 1 0 5 | 7
Hisp | 1 264 3 87 | 355
black | 0 10 374 22 | 406
white | 1 71 1 2,982 | 3,055
-----------+--------------------------------------------+----------
Total | 3 346 378 3,096 | 3,823
_______________________________________________________________________________
-> year = 80, med4 = 3, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 18 2 1 19 | 40
Hisp | 3 782 12 473 | 1,270
black | 0 32 1,911 108 | 2,051
white | 25 390 17 27,314 | 27,746
-----------+--------------------------------------------+----------
Total | 46 1,206 1,941 27,914 | 31,107
_______________________________________________________________________________
-> year = 80, med4 = 3, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 48 4 0 32 | 84
Hisp | 2 607 12 404 | 1,025
black | 2 31 2,162 93 | 2,288
white | 36 390 25 24,167 | 24,618
-----------+--------------------------------------------+----------
Total | 88 1,032 2,199 24,696 | 28,015
_______________________________________________________________________________
-> year = 80, med4 = 3, fed4 = 4
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 23 1 0 8 | 32
Hisp | 1 128 3 97 | 229
black | 1 7 660 27 | 695
white | 21 88 3 8,301 | 8,413
-----------+--------------------------------------------+----------
Total | 46 224 666 8,433 | 9,369
_______________________________________________________________________________
-> year = 80, med4 = 4, fed4 = 1
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 0 0 0 1 | 1
Hisp | 0 24 3 12 | 39
black | 0 0 53 1 | 54
white | 1 11 1 519 | 532
-----------+--------------------------------------------+----------
Total | 1 35 57 533 | 626
_______________________________________________________________________________
-> year = 80, med4 = 4, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 5 2 0 12 | 19
Hisp | 1 141 0 112 | 254
black | 0 10 354 27 | 391
white | 3 132 5 9,821 | 9,961
-----------+--------------------------------------------+----------
Total | 9 285 359 9,972 | 10,625
_______________________________________________________________________________
-> year = 80, med4 = 4, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 39 0 1 24 | 64
Hisp | 2 157 5 161 | 325
black | 0 15 667 37 | 719
white | 24 198 12 16,340 | 16,574
-----------+--------------------------------------------+----------
Total | 65 370 685 16,562 | 17,682
_______________________________________________________________________________
-> year = 80, med4 = 4, fed4 = 4
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 89 0 0 50 | 139
Hisp | 5 164 4 234 | 407
black | 0 8 1,045 38 | 1,091
white | 75 192 14 27,573 | 27,854
-----------+--------------------------------------------+----------
Total | 169 364 1,063 27,895 | 29,491
_______________________________________________________________________________
-> year = 90, med4 = 1, fed4 = 1
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 0 0 0 4 | 4
Hisp | 1 870 7 187 | 1,065
black | 0 11 713 36 | 760
white | 4 156 11 9,380 | 9,551
-----------+--------------------------------------------+----------
Total | 5 1,037 731 9,607 | 11,380
_______________________________________________________________________________
-> year = 90, med4 = 1, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 0 0 0 2 | 2
Hisp | 0 565 8 217 | 790
black | 0 12 633 36 | 681
white | 2 149 7 10,142 | 10,300
-----------+--------------------------------------------+----------
Total | 2 726 648 10,397 | 11,773
_______________________________________________________________________________
-> year = 90, med4 = 1, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 0 0 0 1 | 1
Hisp | 1 222 3 100 | 326
black | 0 4 394 21 | 419
white | 5 71 10 4,122 | 4,208
-----------+--------------------------------------------+----------
Total | 6 297 407 4,244 | 4,954
_______________________________________________________________________________
-> year = 90, med4 = 1, fed4 = 4
| ffulleth
mfulleth | Hisp black white | Total
-----------+---------------------------------+----------
Hisp | 13 0 11 | 24
black | 0 43 1 | 44
white | 5 1 423 | 429
-----------+---------------------------------+----------
Total | 18 44 435 | 497
_______________________________________________________________________________
-> year = 90, med4 = 2, fed4 = 1
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 2 2 0 3 | 7
Hisp | 0 468 6 146 | 620
black | 0 13 557 42 | 612
white | 3 140 11 7,868 | 8,022
-----------+--------------------------------------------+----------
Total | 5 623 574 8,059 | 9,261
_______________________________________________________________________________
-> year = 90, med4 = 2, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 1 2 0 11 | 14
Hisp | 3 1,227 17 547 | 1,794
black | 0 48 2,643 148 | 2,839
white | 18 572 44 39,467 | 40,101
-----------+--------------------------------------------+----------
Total | 22 1,849 2,704 40,173 | 44,748
_______________________________________________________________________________
-> year = 90, med4 = 2, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 4 3 1 5 | 13
Hisp | 0 497 19 334 | 850
black | 0 21 1,513 77 | 1,611
white | 16 343 32 19,526 | 19,917
-----------+--------------------------------------------+----------
Total | 20 864 1,565 19,942 | 22,391
_______________________________________________________________________________
-> year = 90, med4 = 2, fed4 = 4
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Hisp | 1 42 1 34 | 78
black | 0 1 216 5 | 222
white | 4 41 2 3,381 | 3,428
-----------+--------------------------------------------+----------
Total | 5 84 219 3,420 | 3,728
_______________________________________________________________________________
-> year = 90, med4 = 3, fed4 = 1
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 0 0 0 2 | 2
Hisp | 0 163 1 76 | 240
black | 0 3 210 22 | 235
white | 4 85 5 2,657 | 2,751
-----------+--------------------------------------------+----------
Total | 4 251 216 2,757 | 3,228
_______________________________________________________________________________
-> year = 90, med4 = 3, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 2 3 1 10 | 16
Hisp | 2 419 7 313 | 741
black | 0 18 870 89 | 977
white | 6 295 23 15,601 | 15,925
-----------+--------------------------------------------+----------
Total | 10 735 901 16,013 | 17,659
_______________________________________________________________________________
-> year = 90, med4 = 3, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 14 6 1 26 | 47
Hisp | 3 783 23 624 | 1,433
black | 1 39 2,119 160 | 2,319
white | 21 636 53 29,109 | 29,819
-----------+--------------------------------------------+----------
Total | 39 1,464 2,196 29,919 | 33,618
_______________________________________________________________________________
-> year = 90, med4 = 3, fed4 = 4
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 10 2 0 6 | 18
Hisp | 0 102 4 145 | 251
black | 0 5 501 43 | 549
white | 15 115 10 7,868 | 8,008
-----------+--------------------------------------------+----------
Total | 25 224 515 8,062 | 8,826
_______________________________________________________________________________
-> year = 90, med4 = 4, fed4 = 1
| ffulleth
mfulleth | Hisp black white | Total
-----------+---------------------------------+----------
Hisp | 10 0 3 | 13
black | 1 27 1 | 29
white | 13 0 303 | 316
-----------+---------------------------------+----------
Total | 24 27 307 | 358
_______________________________________________________________________________
-> year = 90, med4 = 4, fed4 = 2
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 1 3 0 5 | 9
Hisp | 0 35 2 34 | 71
black | 0 3 94 6 | 103
white | 2 42 3 2,939 | 2,986
-----------+--------------------------------------------+----------
Total | 3 83 99 2,984 | 3,169
_______________________________________________________________________________
-> year = 90, med4 = 4, fed4 = 3
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 10 0 0 9 | 19
Hisp | 1 130 1 118 | 250
black | 0 6 324 32 | 362
white | 12 205 10 10,095 | 10,322
-----------+--------------------------------------------+----------
Total | 23 341 335 10,254 | 10,953
_______________________________________________________________________________
-> year = 90, med4 = 4, fed4 = 4
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 51 3 0 37 | 91
Hisp | 1 90 6 220 | 317
black | 1 8 454 38 | 501
white | 62 297 21 20,444 | 20,824
-----------+--------------------------------------------+----------
Total | 115 398 481 20,739 | 21,733
. *If we want to fit a model to marginal values of all 32
. *of the racial intermarriage tables, we would need 7x32 terms.
.
. desmat mfulleth*med4*fed4*year ffulleth*med4*fed4*year
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)
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*med4*fed4*year
Hessian has become unstable or asymmetric
--Break--
r(1);
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*med4*fed4*year, verbose
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)
Iteration 0: log likelihood = -10182718 (not concave)
Iteration 1: log likelihood = -9775409 (not concave)
Iteration 2: log likelihood = -8993376.6 (not concave)
Iteration 3: log likelihood = -8091654.2 (not concave)
Iteration 4: log likelihood = -7444357.8 (not concave)
Iteration 5: log likelihood = -6491615.2 (not concave)
Iteration 6: log likelihood = -5455862.1 (not concave)
Iteration 7: log likelihood = -4592233.3 (not concave)
Iteration 8: log likelihood = -3909283.7 (not concave)
Iteration 9: log likelihood = -3417118.4 (not concave)
Iteration 10: log likelihood = -2973957 (not concave)
Iteration 11: log likelihood = -2783195 (not concave)
Iteration 12: log likelihood = -2592428.7 (not concave)
Iteration 13: log likelihood = -2221113.9 (not concave)
Iteration 14: log likelihood = -2052404.7 (not concave)
Iteration 15: log likelihood = -1754366.1 (not concave)
Iteration 16: log likelihood = -1604776.6 (not concave)
Iteration 17: log likelihood = -1483631.8 (not concave)
Iteration 18: log likelihood = -1343375 (not concave)
Iteration 19: log likelihood = -1247741.1 (not concave)
Iteration 20: log likelihood = -1161997 (not concave)
Iteration 21: log likelihood = -1072588.2 (not concave)
Iteration 22: log likelihood = -970211.93 (not concave)
Iteration 23: log likelihood = -924405.15 (not concave)
Iteration 24: log likelihood = -870858.9 (not concave)
Iteration 25: log likelihood = -808624.74 (not concave)
Iteration 26: log likelihood = -777813.6 (not concave)
Iteration 27: log likelihood = -736661.37
Iteration 28: log likelihood = -714424.98 (backed up)
Iteration 29: log likelihood = -585481.63 (backed up)
Iteration 30: log likelihood = -271160.25 (backed up)
Iteration 31: log likelihood = -191025.55
Iteration 32: log likelihood = -160267.66
Iteration 33: log likelihood = -155976.88
Iteration 34: log likelihood = -155783.44
Iteration 35: log likelihood = -155780.43 (not concave)
Iteration 36: log likelihood = -155780.43 (not concave)
Poisson regression Number of obs = 512
LR chi2(223) = 2493255.72
Prob > chi2 = 0.0000
Log likelihood = -155780.43 Pseudo R2 = 0.8889
------------------------------------------------------------------------------
count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_x_1 | 6.739331 .5777007 11.67 0.000 5.607058 7.871603
_x_2 | 6.55768 .5777687 11.35 0.000 5.425274 7.690086
_x_3 | 8.580719 .5774133 14.86 0.000 7.44901 9.712428
_x_4 | .672868 .8544451 0.79 0.431 -1.001814 2.34755
_x_5 | 1.434689 .9593436 1.50 0.135 -.4455894 3.314968
_x_6 | .1996414 1.562928 0.13 0.898 -2.86364 3.262923
_x_7 | -1.409378 .6908616 -2.04 0.041 -2.763442 -.0553147
_x_8 | -2.813116 .6923976 -4.06 0.000 -4.17019 -1.456041
_x_9 | -3.075743 1.165912 -2.64 0.008 -5.360888 -.7905981
_x_10 | -1.001429 .690817 -1.45 0.147 -2.355406 .3525473
_x_11 | -2.497165 .6921987 -3.61 0.000 -3.853849 -1.140481
_x_12 | -2.568621 1.162887 -2.21 0.027 -4.847837 -.2894045
_x_13 | -.856026 .6901658 -1.24 0.215 -2.208726 .4966741
_x_14 | -2.50209 .6903571 -3.62 0.000 -3.855165 -1.149015
_x_15 | -2.304059 1.155534 -1.99 0.046 -4.568863 -.0392543
_x_16 | 1.158274 .8096167 1.43 0.153 -.4285456 2.745094
_x_17 | -10.71366 99.77274 -0.11 0.914 -206.2646 184.8373
_x_18 | 1.542873 1.331642 1.16 0.247 -1.067098 4.152843
_x_19 | -1.742121 .6591025 -2.64 0.008 -3.033938 -.4503038
_x_20 | 8.083052 99.7672 0.08 0.935 -187.4571 203.6232
_x_21 | -3.183266 1.166868 -2.73 0.006 -5.470285 -.8962481
_x_22 | -1.142946 .6589871 -1.73 0.083 -2.434537 .148645
_x_23 | 9.08084 99.76719 0.09 0.927 -186.4593 204.6209
_x_24 | -2.172137 1.159989 -1.87 0.061 -4.445674 .1013998
_x_25 | -1.09277 .6583802 -1.66 0.097 -2.383171 .1976319
_x_26 | 8.471085 99.76718 0.08 0.932 -187.069 204.0112
_x_27 | -2.649984 1.155583 -2.29 0.022 -4.914884 -.3850834
_x_28 | 1.931318 .9844577 1.96 0.050 .0018165 3.86082
_x_29 | 13.61574 99.7745 0.14 0.891 -181.9387 209.1702
_x_30 | -.7037542 1.565766 -0.45 0.653 -3.772598 2.36509
_x_31 | 1.218335 1.085411 1.12 0.262 -.9090317 3.345701
_x_32 | 14.58558 99.77525 0.15 0.884 -180.9703 210.1415
_x_33 | 1.81057 1.517262 1.19 0.233 -1.16321 4.78435
_x_34 | 1.15176 1.678529 0.69 0.493 -2.138096 4.441616
_x_35 | 15.70596 99.78291 0.16 0.875 -179.865 211.2769
_x_36 | 4.668947 1.945426 2.40 0.016 .8559824 8.481912
_x_37 | .4768449 .7711308 0.62 0.536 -1.034544 1.988233
_x_38 | -10.08876 99.76811 -0.10 0.919 -205.6307 185.4531
_x_39 | .9095668 1.329256 0.68 0.494 -1.695727 3.51486
_x_40 | 1.27381 .778383 1.64 0.102 -.2517928 2.799413
_x_41 | -9.507611 99.76799 -0.10 0.924 -205.0493 186.0341
_x_42 | 1.22494 1.242126 0.99 0.324 -1.209581 3.659462
_x_43 | .6714407 1.231502 0.55 0.586 -1.742258 3.085139
_x_44 | -10.12168 99.77243 -0.10 0.919 -205.6721 185.4287
_x_45 | .5939326 1.548174 0.38 0.701 -2.440433 3.628299
_x_46 | .3331877 .7708511 0.43 0.666 -1.177653 1.844028
_x_47 | -10.26111 99.7681 -0.10 0.918 -205.803 185.2808
_x_48 | 1.276668 1.319611 0.97 0.333 -1.309723 3.863058
_x_49 | 1.019629 .7778652 1.31 0.190 -.5049589 2.544217
_x_50 | -9.836734 99.76798 -0.10 0.921 -205.3784 185.7049
_x_51 | 1.18949 1.234337 0.96 0.335 -1.229767 3.608747
_x_52 | .1781836 1.227983 0.15 0.885 -2.228618 2.584985
_x_53 | -10.65089 99.77238 -0.11 0.915 -206.2012 184.8994
_x_54 | .2432021 1.540187 0.16 0.875 -2.775508 3.261913
_x_55 | .5776109 .7699304 0.75 0.453 -.9314249 2.086647
_x_56 | -9.667817 99.76808 -0.10 0.923 -205.2097 185.874
_x_57 | 1.883223 1.314745 1.43 0.152 -.6936295 4.460076
_x_58 | 1.556118 .7756852 2.01 0.045 .0358025 3.076433
_x_59 | -8.869275 99.76796 -0.09 0.929 -204.4109 186.6723
_x_60 | 2.143121 1.228792 1.74 0.081 -.2652664 4.551508
_x_61 | 1.07811 1.219856 0.88 0.377 -1.312763 3.468983
_x_62 | -9.191027 99.77228 -0.09 0.927 -204.7411 186.359
_x_63 | 1.673555 1.531161 1.09 0.274 -1.327465 4.674575
_x_64 | .3160822 .9431945 0.34 0.738 -1.532545 2.164709
_x_65 | -1.165671 .7628921 -1.53 0.127 -2.660913 .3295697
_x_66 | -1.321461 .7631906 -1.73 0.083 -2.817287 .1743655
_x_67 | -.8133933 .7621274 -1.07 0.286 -2.307136 .6803489
_x_68 | .0794173 1.231475 0.06 0.949 -2.334229 2.493064
_x_69 | -1.077844 1.448612 -0.74 0.457 -3.917072 1.761383
_x_70 | -18.12627 141.287 -0.13 0.898 -295.0436 258.7911
_x_71 | .3192939 .932735 0.34 0.732 -1.508833 2.147421
_x_72 | 1.992906 1.102325 1.81 0.071 -.1676103 4.153423
_x_73 | 9.032338 88.4755 0.10 0.919 -164.3765 182.4411
_x_74 | .2357881 .9329153 0.25 0.800 -1.592692 2.064268
_x_75 | 1.993327 1.102411 1.81 0.071 -.1673581 4.154013
_x_76 | 9.665046 88.47522 0.11 0.913 -163.7432 183.0733
_x_77 | .1324963 .9309732 0.14 0.887 -1.692178 1.95717
_x_78 | 1.927291 1.098936 1.75 0.079 -.2265837 4.081166
_x_79 | 9.257778 88.47494 0.10 0.917 -164.1499 182.6655
_x_80 | -2.820341 1.44895 -1.95 0.052 -5.660232 .0195495
_x_81 | 10.32609 99.78082 0.10 0.918 -185.2407 205.8929
_x_82 | -25.38635 1.164989 -21.79 0.000 -27.66968 -23.10301
_x_83 | 2.145802 1.087743 1.97 0.049 .0138644 4.27774
_x_84 | -7.869803 99.77347 -0.08 0.937 -203.4222 187.6826
_x_85 | 13.21445 268.9059 0.05 0.961 -513.8315 540.2604
_x_86 | 1.735577 1.08794 1.60 0.111 -.3967459 3.8679
_x_87 | -8.279198 99.77346 -0.08 0.934 -203.8316 187.2732
_x_88 | 13.14661 268.9059 0.05 0.961 -513.8992 540.1924
_x_89 | 1.870645 1.086384 1.72 0.085 -.258629 3.999919
_x_90 | -7.893662 99.77343 -0.08 0.937 -203.446 187.6587
_x_91 | 13.37098 268.9058 0.05 0.960 -513.6747 540.4167
_x_92 | .3226433 1.694475 0.19 0.849 -2.998466 3.643752
_x_93 | 11.33068 439.8728 0.03 0.979 -850.8042 873.4656
_x_94 | 1.705933 1.875492 0.91 0.363 -1.969963 5.381829
_x_95 | -11.13787 99.78723 -0.11 0.911 -206.7172 184.4415
_x_96 | 25.02271 . . . . .
_x_97 | 19.99466 141.2934 0.14 0.887 -256.9353 296.9246
_x_98 | 5.709681 172.9666 0.03 0.974 -333.2987 344.7181
_x_99 | 42.69323 141.2887 0.30 0.763 -234.2276 319.6141
_x_100 | -.510949 1.248934 -0.41 0.682 -2.958815 1.936917
_x_101 | 9.572242 99.7755 0.10 0.924 -185.9841 205.1286
_x_102 | 1.105299 515.5545 0.00 0.998 -1009.363 1011.574
_x_103 | -2.595498 1.380558 -1.88 0.060 -5.301341 .1103457
_x_104 | 7.955102 99.77682 0.08 0.936 -187.6039 203.5141
_x_105 | -13.3745 268.9054 -0.05 0.960 -540.4194 513.6704
_x_106 | -10.54008 88.47992 -0.12 0.905 -183.9575 162.8774
_x_107 | .9552243 133.3497 0.01 0.994 -260.4055 262.3159
_x_108 | -20.90734 283.0861 -0.07 0.941 -575.7459 533.9312
_x_109 | -.0842382 1.24892 -0.07 0.946 -2.532076 2.3636
_x_110 | 9.808565 99.77548 0.10 0.922 -185.7478 205.3649
_x_111 | .8538156 515.5545 0.00 0.999 -1009.614 1011.322
_x_112 | -2.232711 1.38039 -1.62 0.106 -4.938225 .4728037
_x_113 | 8.198267 99.7768 0.08 0.935 -187.3607 203.7572
_x_114 | -13.47869 268.9053 -0.05 0.960 -540.5234 513.5661
_x_115 | -10.66609 88.47961 -0.12 0.904 -184.083 162.7508
_x_116 | .4638525 133.3495 0.00 0.997 -260.8964 261.8241
_x_117 | -21.84462 283.0859 -0.08 0.938 -576.6829 532.9936
_x_118 | -.4391783 1.246581 -0.35 0.725 -2.882431 2.004075
_x_119 | 9.380995 99.77544 0.09 0.925 -186.1753 204.9373
_x_120 | .6646558 515.5544 0.00 0.999 -1009.803 1011.133
_x_121 | -2.623434 1.376464 -1.91 0.057 -5.321254 .0743869
_x_122 | 7.54889 99.77674 0.08 0.940 -188.0099 203.1077
_x_123 | -13.9588 268.9052 -0.05 0.959 -541.0034 513.0858
_x_124 | -10.77273 88.47926 -0.12 0.903 -184.1889 162.6434
_x_125 | .1901741 133.3493 0.00 0.999 -261.1697 261.55
_x_126 | -21.68259 283.0858 -0.08 0.939 -576.5206 533.1554
_x_127 | 5.573001 .3339711 16.69 0.000 4.918429 6.227572
_x_128 | 5.435176 .3340645 16.27 0.000 4.780421 6.08993
_x_129 | 7.495153 .3334312 22.48 0.000 6.84164 8.148666
_x_130 | -.2499864 .5050676 -0.49 0.621 -1.239901 .7399279
_x_131 | -.8251795 .6691492 -1.23 0.218 -2.136688 .4863289
_x_132 | -2.017654 1.067738 -1.89 0.059 -4.110383 .0750744
_x_133 | .0799817 .5050087 0.16 0.874 -.9098172 1.069781
_x_134 | -.598838 .6690129 -0.90 0.371 -1.910079 .7124031
_x_135 | -1.392115 1.062591 -1.31 0.190 -3.474755 .6905257
_x_136 | .2347619 .5040821 0.47 0.641 -.7532209 1.222745
_x_137 | -.5559094 .6669581 -0.83 0.405 -1.863123 .7513045
_x_138 | -1.216622 1.054993 -1.15 0.249 -3.284371 .8511265
_x_139 | -.546925 .4726308 -1.16 0.247 -1.473264 .3794143
_x_140 | -.0515376 1.056204 -0.05 0.961 -2.12166 2.018585
_x_141 | -3.093543 .6862228 -4.51 0.000 -4.438515 -1.748571
_x_142 | .06427 .4724057 0.14 0.892 -.8616282 .9901682
_x_143 | .9733549 1.055119 0.92 0.356 -1.094641 3.041351
_x_144 | -2.169863 .6751613 -3.21 0.001 -3.493154 -.8465708
_x_145 | .1068473 .4715353 0.23 0.821 -.8173449 1.03104
_x_146 | .3661711 1.054321 0.35 0.728 -1.70026 2.432602
_x_147 | -2.671873 .6674014 -4.00 0.000 -3.979956 -1.36379
_x_148 | -1.061095 .6155059 -1.72 0.085 -2.267465 .145274
_x_149 | -2.253026 1.134668 -1.99 0.047 -4.476935 -.0291176
_x_150 | .2790064 .876904 0.32 0.750 -1.439694 1.997707
_x_151 | -.9344752 .763002 -1.22 0.221 -2.429932 .5609813
_x_152 | -2.234377 1.21001 -1.85 0.065 -4.605953 .1371987
_x_153 | -.0712397 .9128697 -0.08 0.938 -1.860431 1.717952
_x_154 | .4468413 1.168985 0.38 0.702 -1.844328 2.73801
_x_155 | -1.764694 1.470436 -1.20 0.230 -4.646696 1.117308
_x_156 | .3054792 1.228045 0.25 0.804 -2.101445 2.712404
_x_157 | -1.205423 .615123 -1.96 0.050 -2.411042 .0001961
_x_158 | -2.455464 1.133233 -2.17 0.030 -4.67656 -.234367
_x_159 | .7322985 .8628861 0.85 0.396 -.9589272 2.423524
_x_160 | -1.158301 .7624963 -1.52 0.129 -2.652766 .3361645
_x_161 | -2.59129 1.208749 -2.14 0.032 -4.960394 -.2221847
_x_162 | .0062109 .9028091 0.01 0.995 -1.763262 1.775684
_x_163 | -.4212352 1.163857 -0.36 0.717 -2.702353 1.859883
_x_164 | -2.661386 1.465477 -1.82 0.069 -5.533668 .2108959
_x_165 | -.0342035 1.216636 -0.03 0.978 -2.418765 2.350358
_x_166 | -.9683054 .6139284 -1.58 0.115 -2.171583 .2349721
_x_167 | -1.880941 1.132076 -1.66 0.097 -4.099769 .3378868
_x_168 | 1.341689 .8552532 1.57 0.117 -.3345765 3.017954
_x_169 | -.6378529 .7601166 -0.84 0.401 -2.127654 .8519482
_x_170 | -1.66837 1.206919 -1.38 0.167 -4.033887 .6971476
_x_171 | .9438385 .8949529 1.05 0.292 -.8102369 2.697914
_x_172 | .624939 1.155591 0.54 0.589 -1.639977 2.889855
_x_173 | -1.104219 1.459065 -0.76 0.449 -3.963935 1.755496
_x_174 | 1.499596 1.205493 1.24 0.214 -.8631258 3.862318
_x_175 | -.2485882 .5571973 -0.45 0.655 -1.340675 .8434985
_x_176 | -.4604388 .5576154 -0.83 0.409 -1.553345 .6324672
_x_177 | .0554054 .5561009 0.10 0.921 -1.034532 1.145343
_x_178 | -.2567084 .8088019 -0.32 0.751 -1.841931 1.328514
_x_179 | -.3601883 .9490252 -0.38 0.704 -2.220243 1.499867
_x_180 | 9.275012 110.1557 0.08 0.933 -206.6262 225.1762
_x_181 | -.3189199 .8090994 -0.39 0.693 -1.904726 1.266886
_x_182 | -.3870374 .9494817 -0.41 0.684 -2.247987 1.473913
_x_183 | 9.116928 110.1556 0.08 0.934 -206.7841 225.018
_x_184 | -.4076109 .8067371 -0.51 0.613 -1.988787 1.173565
_x_185 | -.4591519 .9451135 -0.49 0.627 -2.31154 1.393237
_x_186 | 8.796663 110.1554 0.08 0.936 -207.1039 224.6972
_x_187 | 1.116141 .9608896 1.16 0.245 -.7671682 2.99945
_x_188 | -1.376665 1.218023 -1.13 0.258 -3.763946 1.010616
_x_189 | 12.19019 268.9049 0.05 0.964 -514.8537 539.2341
_x_190 | .7409619 .9610753 0.77 0.441 -1.142711 2.624635
_x_191 | -1.7368 1.216874 -1.43 0.154 -4.12183 .6482306
_x_192 | 12.51002 268.9048 0.05 0.963 -514.5337 539.5538
_x_193 | .8979331 .9592357 0.94 0.349 -.9821343 2.778
_x_194 | -1.361006 1.214748 -1.12 0.263 -3.741869 1.019857
_x_195 | 12.72721 268.9047 0.05 0.962 -514.3164 539.7708
_x_196 | -12.11331 99.78491 -0.12 0.903 -207.6881 183.4615
_x_197 | .1055231 1.151046 0.09 0.927 -2.150486 2.361532
_x_198 | 2.629352 1.380861 1.90 0.057 -.0770864 5.33579
_x_199 | -11.37196 268.9062 -0.04 0.966 -538.4185 515.6745
_x_200 | .5234837 1.279616 0.41 0.682 -1.984517 3.031484
_x_201 | 3.148858 1.453415 2.17 0.030 .3002164 5.9975
_x_202 | -10.97167 268.9054 -0.04 0.967 -538.0166 516.0733
_x_203 | -10.27761 110.1606 -0.09 0.926 -226.1883 205.6331
_x_204 | -6.692497 110.1613 -0.06 0.952 -222.6047 209.2197
_x_205 | -20.74236 290.5922 -0.07 0.943 -590.2927 548.8079
_x_206 | .4758409 1.151074 0.41 0.679 -1.780223 2.731905
_x_207 | 2.843018 1.379361 2.06 0.039 .1395212 5.546515
_x_208 | -12.02858 268.9061 -0.04 0.964 -539.0748 515.0177
_x_209 | .86511 1.279691 0.68 0.499 -1.643038 3.373258
_x_210 | 3.39666 1.452335 2.34 0.019 .550135 6.243185
_x_211 | -11.30992 268.9053 -0.04 0.966 -538.3547 515.7348
_x_212 | -9.58705 110.1605 -0.09 0.931 -225.4976 206.3235
_x_213 | -6.596096 110.1612 -0.06 0.952 -222.5081 209.3159
_x_214 | -21.57455 290.5921 -0.07 0.941 -591.1246 547.9755
_x_215 | .0957266 1.148403 0.08 0.934 -2.155102 2.346555
_x_216 | 2.402916 1.376343 1.75 0.081 -.2946673 5.1005
_x_217 | -12.24678 268.906 -0.05 0.964 -539.2929 514.7993
_x_218 | .4761351 1.275132 0.37 0.709 -2.023077 2.975347
_x_219 | 2.770356 1.447993 1.91 0.056 -.0676574 5.608369
_x_220 | -11.75865 268.9053 -0.04 0.965 -538.8033 515.286
_x_221 | -9.85791 110.1602 -0.09 0.929 -225.7679 206.0521
_x_222 | -6.931618 110.1609 -0.06 0.950 -222.8431 208.9799
_x_223 | -21.49071 290.592 -0.07 0.941 -591.0405 548.0591
_cons | -6.638972 .6666404 -9.96 0.000 -7.945564 -5.332381
------------------------------------------------------------------------------
------------------------------------------------------------------------------------------
Poisson regression
------------------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -155780.426
LR chi square: 2493255.720
Model degrees of freedom: 223
Pseudo R-squared: 0.889
Prob: 0.000
------------------------------------------------------------------------------------------
nr Effect Coeff s.e.
------------------------------------------------------------------------------------------
count
mfulleth
1 Hisp 6.739** 0.578
2 black 6.558** 0.578
3 white 8.581** 0.577
med4
4 2 0.673 0.854
5 3 1.435 0.959
6 4 0.200 1.563
mfulleth.med4
7 Hisp.2 -1.409* 0.691
8 Hisp.3 -2.813** 0.692
9 Hisp.4 -3.076** 1.166
10 black.2 -1.001 0.691
11 black.3 -2.497** 0.692
12 black.4 -2.569* 1.163
13 white.2 -0.856 0.690
14 white.3 -2.502** 0.690
15 white.4 -2.304* 1.156
fed4
16 2 1.158 0.810
17 3 -10.714 99.773
18 4 1.543 1.332
mfulleth.fed4
19 Hisp.2 -1.742** 0.659
20 Hisp.3 8.083 99.767
21 Hisp.4 -3.183** 1.167
22 black.2 -1.143 0.659
23 black.3 9.081 99.767
24 black.4 -2.172 1.160
25 white.2 -1.093 0.658
26 white.3 8.471 99.767
27 white.4 -2.650* 1.156
med4.fed4
28 2.2 1.931* 0.984
29 2.3 13.616 99.774
30 2.4 -0.704 1.566
31 3.2 1.218 1.085
32 3.3 14.586 99.775
33 3.4 1.811 1.517
34 4.2 1.152 1.679
35 4.3 15.706 99.783
36 4.4 4.669* 1.945
mfulleth.med4.fed4
37 Hisp.2.2 0.477 0.771
38 Hisp.2.3 -10.089 99.768
39 Hisp.2.4 0.910 1.329
40 Hisp.3.2 1.274 0.778
41 Hisp.3.3 -9.508 99.768
42 Hisp.3.4 1.225 1.242
43 Hisp.4.2 0.671 1.232
44 Hisp.4.3 -10.122 99.772
45 Hisp.4.4 0.594 1.548
46 black.2.2 0.333 0.771
47 black.2.3 -10.261 99.768
48 black.2.4 1.277 1.320
49 black.3.2 1.020 0.778
50 black.3.3 -9.837 99.768
51 black.3.4 1.189 1.234
52 black.4.2 0.178 1.228
53 black.4.3 -10.651 99.772
54 black.4.4 0.243 1.540
55 white.2.2 0.578 0.770
56 white.2.3 -9.668 99.768
57 white.2.4 1.883 1.315
58 white.3.2 1.556* 0.776
59 white.3.3 -8.869 99.768
60 white.3.4 2.143 1.229
61 white.4.2 1.078 1.220
62 white.4.3 -9.191 99.772
63 white.4.4 1.674 1.531
year
64 90 0.316 0.943
mfulleth.year
65 Hisp.90 -1.166 0.763
66 black.90 -1.321 0.763
67 white.90 -0.813 0.762
med4.year
68 2.90 0.079 1.231
69 3.90 -1.078 1.449
70 4.90 -18.126 141.287
mfulleth.med4.year
71 Hisp.2.90 0.319 0.933
72 Hisp.3.90 1.993 1.102
73 Hisp.4.90 9.032 88.476
74 black.2.90 0.236 0.933
75 black.3.90 1.993 1.102
76 black.4.90 9.665 88.475
77 white.2.90 0.132 0.931
78 white.3.90 1.927 1.099
79 white.4.90 9.258 88.475
fed4.year
80 2.90 -2.820 1.449
81 3.90 10.326 99.781
82 4.90 -25.386** 1.165
mfulleth.fed4.year
83 Hisp.2.90 2.146* 1.088
84 Hisp.3.90 -7.870 99.773
85 Hisp.4.90 13.214 268.906
86 black.2.90 1.736 1.088
87 black.3.90 -8.279 99.773
88 black.4.90 13.147 268.906
89 white.2.90 1.871 1.086
90 white.3.90 -7.894 99.773
91 white.4.90 13.371 268.906
med4.fed4.year
92 2.2.90 0.323 1.694
93 2.4.90 11.331 439.873
94 3.2.90 1.706 1.875
95 3.3.90 -11.138 99.787
96 3.4.90 25.023 .
97 4.2.90 19.995 141.293
98 4.3.90 5.710 172.967
99 4.4.90 42.693 141.289
mfulleth.med4.fed4.year
100 Hisp.2.2.90 -0.511 1.249
101 Hisp.2.3.90 9.572 99.775
102 Hisp.2.4.90 1.105 515.555
103 Hisp.3.2.90 -2.595 1.381
104 Hisp.3.3.90 7.955 99.777
105 Hisp.3.4.90 -13.374 268.905
106 Hisp.4.2.90 -10.540 88.480
107 Hisp.4.3.90 0.955 133.350
108 Hisp.4.4.90 -20.907 283.086
109 black.2.2.90 -0.084 1.249
110 black.2.3.90 9.809 99.775
111 black.2.4.90 0.854 515.554
112 black.3.2.90 -2.233 1.380
113 black.3.3.90 8.198 99.777
114 black.3.4.90 -13.479 268.905
115 black.4.2.90 -10.666 88.480
116 black.4.3.90 0.464 133.350
117 black.4.4.90 -21.845 283.086
118 white.2.2.90 -0.439 1.247
119 white.2.3.90 9.381 99.775
120 white.2.4.90 0.665 515.554
121 white.3.2.90 -2.623 1.376
122 white.3.3.90 7.549 99.777
123 white.3.4.90 -13.959 268.905
124 white.4.2.90 -10.773 88.479
125 white.4.3.90 0.190 133.349
126 white.4.4.90 -21.683 283.086
ffulleth
127 Hisp 5.573** 0.334
128 black 5.435** 0.334
129 white 7.495** 0.333
ffulleth.med4
130 Hisp.2 -0.250 0.505
131 Hisp.3 -0.825 0.669
132 Hisp.4 -2.018 1.068
133 black.2 0.080 0.505
134 black.3 -0.599 0.669
135 black.4 -1.392 1.063
136 white.2 0.235 0.504
137 white.3 -0.556 0.667
138 white.4 -1.217 1.055
ffulleth.fed4
139 Hisp.2 -0.547 0.473
140 Hisp.3 -0.052 1.056
141 Hisp.4 -3.094** 0.686
142 black.2 0.064 0.472
143 black.3 0.973 1.055
144 black.4 -2.170** 0.675
145 white.2 0.107 0.472
146 white.3 0.366 1.054
147 white.4 -2.672** 0.667
ffulleth.med4.fed4
148 Hisp.2.2 -1.061 0.616
149 Hisp.2.3 -2.253* 1.135
150 Hisp.2.4 0.279 0.877
151 Hisp.3.2 -0.934 0.763
152 Hisp.3.3 -2.234 1.210
153 Hisp.3.4 -0.071 0.913
154 Hisp.4.2 0.447 1.169
155 Hisp.4.3 -1.765 1.470
156 Hisp.4.4 0.305 1.228
157 black.2.2 -1.205 0.615
158 black.2.3 -2.455* 1.133
159 black.2.4 0.732 0.863
160 black.3.2 -1.158 0.762
161 black.3.3 -2.591* 1.209
162 black.3.4 0.006 0.903
163 black.4.2 -0.421 1.164
164 black.4.3 -2.661 1.465
165 black.4.4 -0.034 1.217
166 white.2.2 -0.968 0.614
167 white.2.3 -1.881 1.132
168 white.2.4 1.342 0.855
169 white.3.2 -0.638 0.760
170 white.3.3 -1.668 1.207
171 white.3.4 0.944 0.895
172 white.4.2 0.625 1.156
173 white.4.3 -1.104 1.459
174 white.4.4 1.500 1.205
ffulleth.year
175 Hisp.90 -0.249 0.557
176 black.90 -0.460 0.558
177 white.90 0.055 0.556
ffulleth.med4.year
178 Hisp.2.90 -0.257 0.809
179 Hisp.3.90 -0.360 0.949
180 Hisp.4.90 9.275 110.156
181 black.2.90 -0.319 0.809
182 black.3.90 -0.387 0.949
183 black.4.90 9.117 110.156
184 white.2.90 -0.408 0.807
185 white.3.90 -0.459 0.945
186 white.4.90 8.797 110.155
ffulleth.fed4.year
187 Hisp.2.90 1.116 0.961
188 Hisp.3.90 -1.377 1.218
189 Hisp.4.90 12.190 268.905
190 black.2.90 0.741 0.961
191 black.3.90 -1.737 1.217
192 black.4.90 12.510 268.905
193 white.2.90 0.898 0.959
194 white.3.90 -1.361 1.215
195 white.4.90 12.727 268.905
med4.fed4.year
196 2.3.90 -12.113 99.785
ffulleth.med4.fed4.year
197 Hisp.2.2.90 0.106 1.151
198 Hisp.2.3.90 2.629 1.381
199 Hisp.2.4.90 -11.372 268.906
200 Hisp.3.2.90 0.523 1.280
201 Hisp.3.3.90 3.149* 1.453
202 Hisp.3.4.90 -10.972 268.905
203 Hisp.4.2.90 -10.278 110.161
204 Hisp.4.3.90 -6.692 110.161
205 Hisp.4.4.90 -20.742 290.592
206 black.2.2.90 0.476 1.151
207 black.2.3.90 2.843* 1.379
208 black.2.4.90 -12.029 268.906
209 black.3.2.90 0.865 1.280
210 black.3.3.90 3.397* 1.452
211 black.3.4.90 -11.310 268.905
212 black.4.2.90 -9.587 110.160
213 black.4.3.90 -6.596 110.161
214 black.4.4.90 -21.575 290.592
215 white.2.2.90 0.096 1.148
216 white.2.3.90 2.403 1.376
217 white.2.4.90 -12.247 268.906
218 white.3.2.90 0.476 1.275
219 white.3.3.90 2.770 1.448
220 white.3.4.90 -11.759 268.905
221 white.4.2.90 -9.858 110.160
222 white.4.3.90 -6.932 110.161
223 white.4.4.90 -21.491 290.592
224 _cons -6.639** 0.667
------------------------------------------------------------------------------------------
* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = 309363.9
Prob > chi2(288) = 0.0000
. predict P1_indep_edbyed
(option n assumed; predicted number of events)
. *Note that the standard errors of some of these coefficients are alarmingly large, in the
> hundreds.
. *That can mean that there is insufficient data...
. 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
. *102 of our 512 cells are zero.
. *That means that models with many terms can run into problems fitting the data, because there is sparse data.
. *So let me demonstrate that this model actually fits the marginals of each of the 32 racial intermarriage tables.
I'll show just one.
.
. table mfulleth ffulleth if me4==2 & fed4==3 & year==90, contents (sum count)
me4 not found
r(111);
. table mfulleth ffulleth if med4==2 & fed4==3 & year==90, contents (sum count)
--------------------------------------
| ffulleth
mfulleth | Asian Hisp black white
----------+---------------------------
Asian | 4 3 1 5
Hisp | 0 497 19 334
black | 0 21 1513 77
white | 16 343 32 19526
--------------------------------------
. table mfulleth ffulleth if med4==2 & fed4==3 & year==90, contents (sum count) row col
---------------------------------------------
| ffulleth
mfulleth | Asian Hisp black white Total
----------+----------------------------------
Asian | 4 3 1 5 13
Hisp | 0 497 19 334 850
black | 0 21 1513 77 1611
white | 16 343 32 19526 19917
|
Total | 20 864 1565 19942 22391
---------------------------------------------
. table mfulleth ffulleth if med4==2 & fed4==3 & year==90, contents (sum P1_indep_edbyed) row col
------------------------------------------------------------
| ffulleth
mfulleth | Asian Hisp black white Total
----------+-------------------------------------------------
Asian | .0116116 .5016211 .9086075 11.5779 12.99974
Hisp | .7592337 32.79889 59.41002 757.0303 849.9984
black | 1.438973 62.16362 112.5996 1434.797 1610.999
white | 17.79018 768.5356 1392.081 17738.55 19916.96
|
Total | 20 863.9998 1564.999 19941.96 22390.96
------------------------------------------------------------
. *This is the independence model for this one of the 32 separate ed by ed by year tables.
. gen race_endogamy=0
. replace race_endogamy=1 if mfulleth== ffulleth
(128 real changes made)
. gen ed_endogamy=0
. replace ed_endogamy=1 if med4== fed4
(128 real changes made)
. desmat: poisson count mffulleth*med4*year ffulleth*fed4*year race_endogamy ed_endogamy
variable mffulleth not found
r(111);
. desmat: poisson count mfulleth*med4*year ffulleth*fed4*year race_endogamy ed_endogamy
------------------------------------------------------------------------------------------
Poisson regression
------------------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -53071.595
LR chi square: 2698673.383
Model degrees of freedom: 63
Pseudo R-squared: 0.962
Prob: 0.000
------------------------------------------------------------------------------------------
nr Effect Coeff s.e.
------------------------------------------------------------------------------------------
count
mfulleth
1 Hisp 4.448** 0.268
2 black 4.509** 0.268
3 white 5.272** 0.268
med4
4 2 1.626** 0.286
5 3 2.257** 0.279
6 4 2.627** 0.276
mfulleth.med4
7 Hisp.2 -1.512** 0.286
8 Hisp.3 -2.559** 0.280
9 Hisp.4 -3.778** 0.278
10 black.2 -1.110** 0.286
11 black.3 -2.269** 0.279
12 black.4 -3.331** 0.277
13 white.2 -0.814** 0.286
14 white.3 -1.825** 0.279
15 white.4 -2.230** 0.276
year
16 90 -0.448 0.572
mfulleth.year
17 Hisp.90 -0.054 0.465
18 black.90 -0.170 0.465
19 white.90 0.223 0.464
med4.year
20 2.90 -0.265 0.504
21 3.90 -0.080 0.482
22 4.90 -0.025 0.477
mfulleth.med4.year
23 Hisp.2.90 0.390 0.505
24 Hisp.3.90 0.407 0.484
25 Hisp.4.90 0.090 0.481
26 black.2.90 0.529 0.505
27 black.3.90 0.558 0.484
28 black.4.90 0.085 0.479
29 white.2.90 0.241 0.504
30 white.3.90 0.193 0.483
31 white.4.90 -0.123 0.477
ffulleth
32 Hisp 3.909** 0.225
33 black 3.725** 0.225
34 white 4.901** 0.224
fed4
35 2 1.794** 0.238
36 3 2.086** 0.235
37 4 2.236** 0.234
ffulleth.fed4
38 Hisp.2 -1.449** 0.239
39 Hisp.3 -2.566** 0.237
40 Hisp.4 -3.766** 0.237
41 black.2 -1.013** 0.239
42 black.3 -1.818** 0.236
43 black.4 -2.731** 0.235
44 white.2 -0.702** 0.238
45 white.3 -1.650** 0.235
46 white.4 -2.207** 0.234
ffulleth.year
47 Hisp.90 -0.375 0.351
48 black.90 -0.581 0.351
49 white.90 -0.307 0.349
fed4.year
50 2.90 -1.049** 0.394
51 3.90 -0.457 0.372
52 4.90 -0.059 0.365
ffulleth.fed4.year
53 Hisp.2.90 1.143** 0.396
54 Hisp.3.90 1.327** 0.374
55 Hisp.4.90 0.817* 0.370
56 black.2.90 1.027** 0.396
57 black.3.90 1.107** 0.374
58 black.4.90 0.463 0.368
59 white.2.90 0.911* 0.394
60 white.3.90 0.992** 0.372
61 white.4.90 0.421 0.365
race_endogamy
62 1 3.341** 0.008
ed_endogamy
63 1 1.102** 0.003
64 _cons -5.167** 0.344
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* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = 103946.2
Prob > chi2(448) = 0.0000
. desmat: poisson count mfulleth*med4*year ffulleth*fed4*year race_endogamy*year ed_endogam
> y*year
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Poisson regression
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Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -52840.288
LR chi square: 2699135.997
Model degrees of freedom: 65
Pseudo R-squared: 0.962
Prob: 0.000
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nr Effect Coeff s.e.
------------------------------------------------------------------------------------------
count
mfulleth
1 Hisp 4.397** 0.269
2 black 4.463** 0.268
3 white 5.197** 0.268
med4
4 2 1.626** 0.286
5 3 2.241** 0.279
6 4 2.602** 0.276
mfulleth.med4
7 Hisp.2 -1.509** 0.287
8 Hisp.3 -2.538** 0.280
9 Hisp.4 -3.748** 0.278
10 black.2 -1.109** 0.286
11 black.3 -2.252** 0.280
12 black.4 -3.306** 0.277
13 white.2 -0.814** 0.286
14 white.3 -1.808** 0.279
15 white.4 -2.205** 0.276
year
16 90 -0.515 0.572
mfulleth.year
17 Hisp.90 0.105 0.465
18 black.90 -0.043 0.465
19 white.90 0.420 0.464
med4.year
20 2.90 -0.274 0.504
21 3.90 -0.060 0.482
22 4.90 0.036 0.477
mfulleth.med4.year
23 Hisp.2.90 0.393 0.505
24 Hisp.3.90 0.377 0.484
25 Hisp.4.90 0.017 0.481
26 black.2.90 0.535 0.505
27 black.3.90 0.537 0.484
28 black.4.90 0.023 0.479
29 white.2.90 0.249 0.504
30 white.3.90 0.172 0.483
31 white.4.90 -0.183 0.477
ffulleth
32 Hisp 3.825** 0.225
33 black 3.634** 0.225
34 white 4.821** 0.224
fed4
35 2 1.794** 0.238
36 3 2.072** 0.235
37 4 2.214** 0.234
ffulleth.fed4
38 Hisp.2 -1.445** 0.239
39 Hisp.3 -2.549** 0.237
40 Hisp.4 -3.738** 0.237
41 black.2 -1.012** 0.239
42 black.3 -1.803** 0.236
43 black.4 -2.706** 0.235
44 white.2 -0.701** 0.238
45 white.3 -1.636** 0.235
46 white.4 -2.185** 0.234
ffulleth.year
47 Hisp.90 -0.173 0.351
48 black.90 -0.344 0.351
49 white.90 -0.090 0.349
fed4.year
50 2.90 -1.051** 0.394
51 3.90 -0.428 0.372
52 4.90 -0.005 0.365
ffulleth.fed4.year
53 Hisp.2.90 1.137** 0.396
54 Hisp.3.90 1.291** 0.374
55 Hisp.4.90 0.749* 0.370
56 black.2.90 1.029** 0.396
57 black.3.90 1.076** 0.374
58 black.4.90 0.402 0.368
59 white.2.90 0.912* 0.394
60 white.3.90 0.962** 0.372
61 white.4.90 0.368 0.365
race_endogamy
62 1 3.486** 0.011
race_endogamy.year
63 1.90 -0.354** 0.016
ed_endogamy
64 1 1.099** 0.004
ed_endogamy.year
65 1.90 0.007 0.006
66 _cons -5.155** 0.343
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* p < .05
** p < .01
. *Racial endogamy declines sharply over time, but educational endogamy is flat over time.
. log close
log: C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2003\class 6.log
log type: text
closed on: 15 Oct 2003, 12:19:58
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