Giovanni Minchio giovanni.minchio@unitn.it
Yuxin Zhang yuxin.zhang@unitn.it
Quantitative Methods Lab, Lesson 7.2
14
Nov. 2024
Linear probability model (LPM) uses linear regression with a binary outcome with the values 0 and 1.
\[ Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \dots + \beta_k X_{ki} + u_i \]
\[ P(Y = 1 \mid X_1, X_2, ..., X_{k}) \]
Binary outcome is commonly coded as 0 (no/failure) and 1 (yes/success)
Interpretation changes! The coefficients now are probabilities of the outcome
Pro:
Simple model with intuitive interpretation (probability change)
Sometimes more efficient
Con:
Probability of an event is always between 0 and 1; however, a LPM may extrapolate and produce estimates greater than 1
Then, LPM violates two main assumptions of linear regression: linearity & homoskedasticity: use robust se
Let’s say we are interested in how age agea, gender
gndr, education edulvlb, country context
cntry, and internet use in minutes netustm
predict voting behavior (0/1).
Variable Storage Display Value
name type format label Variable label
------------------------------------------------------------------------------------------------------------------------
vote byte %3.0g vote Voted last national election
agea int %5.0g agea Age of respondent, calculated
gndr byte %3.0g gndr Gender
edulvlb int %6.0g edulvlb Highest level of education
cntry str2 %2s Country
netustm int %6.0g netustm Internet use, how much time on typical day, in minutes-> tabulation of vote
Voted last national |
election | Freq. Percent Cum.
---------------------+-----------------------------------
Yes | 26,794 72.12 72.12
No | 7,764 20.90 93.02
Not eligible to vote | 2,594 6.98 100.00
---------------------+-----------------------------------
Total | 37,152 100.00
-> tabulation of agea
Age of |
respondent, |
calculated | Freq. Percent Cum.
--------------+-----------------------------------
15 | 99 0.27 0.27
16 | 267 0.72 0.98
17 | 360 0.96 1.95
18 | 402 1.08 3.02
19 | 458 1.23 4.25
20 | 385 1.03 5.28
21 | 457 1.22 6.51
22 | 452 1.21 7.72
23 | 400 1.07 8.79
24 | 389 1.04 9.83
25 | 419 1.12 10.95
26 | 423 1.13 12.09
27 | 401 1.07 13.16
28 | 451 1.21 14.37
29 | 448 1.20 15.57
30 | 486 1.30 16.87
31 | 492 1.32 18.19
32 | 517 1.39 19.58
33 | 536 1.44 21.01
34 | 488 1.31 22.32
35 | 555 1.49 23.81
36 | 584 1.56 25.37
37 | 550 1.47 26.85
38 | 568 1.52 28.37
39 | 566 1.52 29.89
40 | 598 1.60 31.49
41 | 680 1.82 33.31
42 | 600 1.61 34.92
43 | 593 1.59 36.51
44 | 567 1.52 38.03
45 | 598 1.60 39.63
46 | 656 1.76 41.39
47 | 609 1.63 43.02
48 | 623 1.67 44.69
49 | 649 1.74 46.43
50 | 653 1.75 48.18
51 | 687 1.84 50.02
52 | 672 1.80 51.82
53 | 662 1.77 53.59
54 | 621 1.66 55.26
55 | 653 1.75 57.01
56 | 715 1.92 58.92
57 | 664 1.78 60.70
58 | 687 1.84 62.54
59 | 660 1.77 64.31
60 | 687 1.84 66.15
61 | 728 1.95 68.10
62 | 673 1.80 69.91
63 | 634 1.70 71.60
64 | 598 1.60 73.21
65 | 630 1.69 74.89
66 | 677 1.81 76.71
67 | 657 1.76 78.47
68 | 609 1.63 80.10
69 | 587 1.57 81.67
70 | 630 1.69 83.36
71 | 630 1.69 85.05
72 | 573 1.54 86.59
73 | 521 1.40 87.98
74 | 477 1.28 89.26
75 | 506 1.36 90.62
76 | 444 1.19 91.81
77 | 393 1.05 92.86
78 | 326 0.87 93.73
79 | 310 0.83 94.56
80 | 312 0.84 95.40
81 | 307 0.82 96.22
82 | 278 0.74 96.97
83 | 213 0.57 97.54
84 | 182 0.49 98.03
85 | 159 0.43 98.45
86 | 150 0.40 98.85
87 | 98 0.26 99.12
88 | 86 0.23 99.35
89 | 93 0.25 99.60
90 | 151 0.40 100.00
--------------+-----------------------------------
Total | 37,319 100.00
-> tabulation of gndr
Gender | Freq. Percent Cum.
------------+-----------------------------------
Male | 17,463 46.43 46.43
Female | 20,148 53.57 100.00
------------+-----------------------------------
Total | 37,611 100.00
-> tabulation of edulvlb
Highest level of education | Freq. Percent Cum.
----------------------------------------+-----------------------------------
Not completed ISCED level 1 | 317 0.85 0.85
ISCED 1, completed primary education | 2,252 6.01 6.86
Vocational ISCED 2C < 2 years, no acces | 8 0.02 6.88
General/pre-vocational ISCED 2A/2B, acc | 223 0.60 7.48
General ISCED 2A, access ISCED 3A gener | 4,358 11.64 19.11
Vocational ISCED 2C >= 2 years, no acce | 44 0.12 19.23
Vocational ISCED 2A/2B, access ISCED 3 | 341 0.91 20.14
Vocational ISCED 2, access ISCED 3 gene | 44 0.12 20.26
Vocational ISCED 3C < 2 years, no acces | 508 1.36 21.62
General ISCED 3A/3B, access ISCED 5B/lo | 93 0.25 21.86
General ISCED 3A, access upper tier ISC | 5,304 14.16 36.03
Vocational ISCED 3C >= 2 years, no acce | 4,078 10.89 46.92
Vocational ISCED 3A, access ISCED 5B/ l | 666 1.78 48.69
Vocational ISCED 3A, access upper tier | 5,386 14.38 63.08
General ISCED 4A/4B, access ISCED 5B/lo | 17 0.05 63.12
General ISCED 4A, access upper tier ISC | 19 0.05 63.17
ISCED 4 programmes without access ISCED | 836 2.23 65.40
Vocational ISCED 4A/4B, access ISCED 5B | 91 0.24 65.65
Vocational ISCED 4A, access upper tier | 996 2.66 68.31
ISCED 5A short, intermediate/academic/g | 203 0.54 68.85
ISCED 5B short, advanced vocational qua | 1,626 4.34 73.19
ISCED 5A medium, bachelor/equivalent fr | 1,665 4.45 77.64
ISCED 5A medium, bachelor/equivalent fr | 3,133 8.37 86.00
ISCED 5A long, master/equivalent from l | 792 2.11 88.12
ISCED 5A long, master/equivalent from u | 3,961 10.58 98.69
ISCED 6, doctoral degree | 408 1.09 99.78
Other | 81 0.22 100.00
----------------------------------------+-----------------------------------
Total | 37,450 100.00
-> tabulation of cntry
Country | Freq. Percent Cum.
------------+-----------------------------------
BE | 1,341 3.57 3.57
BG | 2,718 7.23 10.79
CH | 1,523 4.05 14.84
CZ | 2,476 6.58 21.42
EE | 1,542 4.10 25.52
FI | 1,577 4.19 29.72
FR | 1,977 5.26 34.97
GB | 1,149 3.05 38.03
GR | 2,799 7.44 45.47
HR | 1,592 4.23 49.70
HU | 1,849 4.92 54.62
IE | 1,770 4.71 59.33
IS | 903 2.40 61.73
IT | 2,640 7.02 68.75
LT | 1,659 4.41 73.16
ME | 1,278 3.40 76.55
MK | 1,429 3.80 80.35
NL | 1,470 3.91 84.26
NO | 1,411 3.75 88.01
PT | 1,838 4.89 92.90
SI | 1,252 3.33 96.23
SK | 1,418 3.77 100.00
------------+-----------------------------------
Total | 37,611 100.00
-> tabulation of netustm
Internet use, |
how much time |
on typical |
day, in |
minutes | Freq. Percent Cum.
---------------+-----------------------------------
0 | 43 0.16 0.16
1 | 7 0.03 0.18
2 | 7 0.03 0.21
3 | 1 0.00 0.21
5 | 21 0.08 0.29
6 | 62 0.22 0.51
7 | 24 0.09 0.60
8 | 72 0.26 0.86
9 | 28 0.10 0.96
10 | 142 0.51 1.47
14 | 1 0.00 1.48
15 | 167 0.61 2.08
18 | 1 0.00 2.09
20 | 126 0.46 2.54
25 | 10 0.04 2.58
28 | 1 0.00 2.58
30 | 1,034 3.75 6.33
31 | 1 0.00 6.33
35 | 8 0.03 6.36
38 | 2 0.01 6.37
40 | 61 0.22 6.59
45 | 229 0.83 7.42
50 | 52 0.19 7.61
55 | 8 0.03 7.64
59 | 2 0.01 7.65
60 | 3,401 12.32 19.97
61 | 6 0.02 19.99
63 | 2 0.01 20.00
64 | 2 0.01 20.01
65 | 18 0.07 20.07
68 | 4 0.01 20.08
69 | 1 0.00 20.09
70 | 55 0.20 20.29
71 | 2 0.01 20.29
72 | 1 0.00 20.30
74 | 1 0.00 20.30
75 | 65 0.24 20.54
78 | 1 0.00 20.54
80 | 80 0.29 20.83
85 | 10 0.04 20.87
88 | 2 0.01 20.87
90 | 1,393 5.05 25.92
95 | 7 0.03 25.95
98 | 2 0.01 25.95
99 | 1 0.00 25.96
100 | 20 0.07 26.03
105 | 49 0.18 26.21
110 | 43 0.16 26.36
115 | 4 0.01 26.38
118 | 1 0.00 26.38
119 | 5 0.02 26.40
120 | 4,432 16.06 42.46
121 | 3 0.01 42.47
122 | 6 0.02 42.49
123 | 5 0.02 42.51
125 | 5 0.02 42.53
128 | 9 0.03 42.56
130 | 34 0.12 42.68
132 | 1 0.00 42.69
133 | 1 0.00 42.69
135 | 43 0.16 42.85
138 | 1 0.00 42.85
140 | 50 0.18 43.03
143 | 1 0.00 43.04
145 | 1 0.00 43.04
150 | 1,106 4.01 47.05
155 | 3 0.01 47.06
158 | 4 0.01 47.07
160 | 24 0.09 47.16
165 | 29 0.11 47.26
168 | 1 0.00 47.27
170 | 13 0.05 47.32
175 | 1 0.00 47.32
177 | 1 0.00 47.32
180 | 3,147 11.40 58.73
181 | 3 0.01 58.74
182 | 2 0.01 58.74
183 | 5 0.02 58.76
185 | 14 0.05 58.81
188 | 2 0.01 58.82
189 | 1 0.00 58.82
190 | 24 0.09 58.91
192 | 2 0.01 58.92
195 | 17 0.06 58.98
196 | 1 0.00 58.98
198 | 1 0.00 58.99
200 | 32 0.12 59.10
202 | 1 0.00 59.11
204 | 1 0.00 59.11
205 | 5 0.02 59.13
210 | 537 1.95 61.07
215 | 3 0.01 61.08
220 | 3 0.01 61.10
225 | 12 0.04 61.14
230 | 21 0.08 61.21
240 | 2,207 8.00 69.21
242 | 1 0.00 69.22
243 | 1 0.00 69.22
244 | 3 0.01 69.23
245 | 1 0.00 69.23
246 | 1 0.00 69.24
248 | 1 0.00 69.24
250 | 9 0.03 69.27
255 | 8 0.03 69.30
256 | 1 0.00 69.31
260 | 21 0.08 69.38
264 | 1 0.00 69.39
265 | 4 0.01 69.40
270 | 353 1.28 70.68
271 | 1 0.00 70.68
275 | 2 0.01 70.69
276 | 1 0.00 70.69
278 | 1 0.00 70.70
280 | 10 0.04 70.73
285 | 6 0.02 70.76
290 | 4 0.01 70.77
299 | 1 0.00 70.77
300 | 1,948 7.06 77.83
301 | 4 0.01 77.85
302 | 1 0.00 77.85
304 | 1 0.00 77.85
305 | 3 0.01 77.86
308 | 3 0.01 77.88
310 | 11 0.04 77.92
311 | 1 0.00 77.92
315 | 6 0.02 77.94
320 | 12 0.04 77.98
325 | 2 0.01 77.99
328 | 1 0.00 77.99
330 | 234 0.85 78.84
333 | 1 0.00 78.85
338 | 1 0.00 78.85
340 | 4 0.01 78.86
345 | 3 0.01 78.88
350 | 11 0.04 78.92
359 | 1 0.00 78.92
360 | 1,205 4.37 83.29
361 | 2 0.01 83.29
362 | 1 0.00 83.30
363 | 1 0.00 83.30
365 | 2 0.01 83.31
368 | 2 0.01 83.31
370 | 5 0.02 83.33
375 | 4 0.01 83.35
377 | 1 0.00 83.35
380 | 7 0.03 83.38
390 | 126 0.46 83.83
400 | 3 0.01 83.84
405 | 6 0.02 83.86
410 | 6 0.02 83.89
420 | 482 1.75 85.63
425 | 4 0.01 85.65
430 | 4 0.01 85.66
435 | 2 0.01 85.67
440 | 2 0.01 85.68
445 | 1 0.00 85.68
450 | 62 0.22 85.90
460 | 1 0.00 85.91
470 | 2 0.01 85.92
480 | 1,315 4.76 90.68
481 | 1 0.00 90.68
485 | 1 0.00 90.69
488 | 6 0.02 90.71
489 | 1 0.00 90.71
490 | 5 0.02 90.73
492 | 1 0.00 90.73
495 | 3 0.01 90.75
500 | 5 0.02 90.76
505 | 1 0.00 90.77
510 | 95 0.34 91.11
520 | 4 0.01 91.13
525 | 2 0.01 91.13
530 | 2 0.01 91.14
533 | 1 0.00 91.14
540 | 406 1.47 92.62
545 | 1 0.00 92.62
555 | 1 0.00 92.62
560 | 1 0.00 92.63
570 | 53 0.19 92.82
580 | 1 0.00 92.82
585 | 1 0.00 92.83
590 | 4 0.01 92.84
595 | 6 0.02 92.86
599 | 2 0.01 92.87
600 | 1,126 4.08 96.95
601 | 1 0.00 96.95
602 | 1 0.00 96.96
608 | 1 0.00 96.96
609 | 1 0.00 96.96
610 | 3 0.01 96.97
615 | 2 0.01 96.98
620 | 4 0.01 97.00
630 | 29 0.11 97.10
640 | 1 0.00 97.10
650 | 2 0.01 97.11
660 | 108 0.39 97.50
665 | 1 0.00 97.51
690 | 5 0.02 97.53
720 | 428 1.55 99.08
732 | 1 0.00 99.08
735 | 1 0.00 99.08
740 | 2 0.01 99.09
745 | 1 0.00 99.09
750 | 13 0.05 99.14
765 | 1 0.00 99.14
780 | 31 0.11 99.26
810 | 1 0.00 99.26
840 | 58 0.21 99.47
870 | 1 0.00 99.47
899 | 2 0.01 99.48
900 | 60 0.22 99.70
930 | 2 0.01 99.71
940 | 1 0.00 99.71
960 | 37 0.13 99.84
990 | 1 0.00 99.85
1020 | 5 0.02 99.87
1038 | 1 0.00 99.87
1080 | 13 0.05 99.92
1140 | 1 0.00 99.92
1200 | 12 0.04 99.96
1380 | 3 0.01 99.97
1440 | 7 0.03 100.00
---------------+-----------------------------------
Total | 27,598 100.00GB: United Kingdom, NO:
Norway, FR: France, IT: Italy(30,434 observations deleted)
Country | Freq. Percent Cum.
------------+-----------------------------------
FR | 1,977 27.55 27.55
GB | 1,149 16.01 43.56
IT | 2,640 36.78 80.34
NO | 1,411 19.66 100.00
------------+-----------------------------------
Total | 7,177 100.00 Country | Freq. Percent Cum.
------------+-----------------------------------
NO | 1,411 19.66 19.66
IT | 2,640 36.78 56.44
FR | 1,977 27.55 83.99
GB | 1,149 16.01 100.00
------------+-----------------------------------
Total | 7,177 100.00
cntry_4:
0 NO
1 IT
2 FR
3 GBvote:
1 Yes
2 No
3 Not eligible to vote
.a Refusal
.b Don't know
.c No answer
(742 observations deleted)
(1,480 differences between vote and vote_bi)
RECODE of |
vote (Voted |
last |
national |
election) | Freq. Percent Cum.
------------+-----------------------------------
0 | 1,480 23.47 23.47
1 | 4,825 76.53 100.00
------------+-----------------------------------
Total | 6,305 100.00(2,725 observations deleted)gndr:
1 Male
2 Female
.a No answer
(3,710 differences between gndr and gndr_bi)
RECODE of |
gndr |
(Gender) | Freq. Percent Cum.
------------+-----------------------------------
0 | 1,797 48.44 48.44
1 | 1,913 51.56 100.00
------------+-----------------------------------
Total | 3,710 100.00edulvlb:
0 Not completed ISCED level 1
113 ISCED 1, completed primary education
129 Vocational ISCED 2C < 2 years, no access ISCED 3
212 General/pre-vocational ISCED 2A/2B, access ISCED 3 vocational
213 General ISCED 2A, access ISCED 3A general/all 3
221 Vocational ISCED 2C >= 2 years, no access ISCED 3
222 Vocational ISCED 2A/2B, access ISCED 3 vocational
223 Vocational ISCED 2, access ISCED 3 general/all
229 Vocational ISCED 3C < 2 years, no access ISCED 5
311 General ISCED 3 >=2 years, no access ISCED 5
312 General ISCED 3A/3B, access ISCED 5B/lower tier 5A
313 General ISCED 3A, access upper tier ISCED 5A/all 5
321 Vocational ISCED 3C >= 2 years, no access ISCED 5
322 Vocational ISCED 3A, access ISCED 5B/ lower tier 5A
323 Vocational ISCED 3A, access upper tier ISCED 5A/all 5
412 General ISCED 4A/4B, access ISCED 5B/lower tier 5A
413 General ISCED 4A, access upper tier ISCED 5A/all 5
421 ISCED 4 programmes without access ISCED 5
422 Vocational ISCED 4A/4B, access ISCED 5B/lower tier 5A
423 Vocational ISCED 4A, access upper tier ISCED 5A/all 5
510 ISCED 5A short, intermediate/academic/general tertiary below bachelor
520 ISCED 5B short, advanced vocational qualifications
610 ISCED 5A medium, bachelor/equivalent from lower tier tertiary
620 ISCED 5A medium, bachelor/equivalent from upper/single tier tertiary
710 ISCED 5A long, master/equivalent from lower tier tertiary
720 ISCED 5A long, master/equivalent from upper/single tier tertiary
800 ISCED 6, doctoral degree
5555 Other
.a Refusal
.b Don't know
.c No answer
(3,678 differences between edulvlb and edu_bi) RECODE of |
vote |
(Voted |
last |
national | Country
election) | NO IT FR GB | Total
-----------+--------------------------------------------+----------
0 | 72 259 433 129 | 893
1 | 718 1,049 561 417 | 2,745
-----------+--------------------------------------------+----------
Total | 790 1,308 994 546 | 3,638 
regress vote_bi agea, robust
regress vote_bi agea i.edu_bi, robust
regress vote_bi agea i.edu_bi netustm, robust
regress vote_bi agea i.edu_bi netustm i.gndr_bi, robust
regress vote_bi agea i.edu_bi netustm i.gndr_bi i.cntry_4, robust Linear regression Number of obs = 3,638
F(1, 3636) = 63.98
Prob > F = 0.0000
R-squared = 0.0180
Root MSE = .42658
------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
agea | .0056776 .0007098 8.00 0.000 .0042859 .0070693
_cons | .5022229 .033343 15.06 0.000 .43685 .5675958
------------------------------------------------------------------------------
Linear regression Number of obs = 3,611
F(2, 3608) = 103.39
Prob > F = 0.0000
R-squared = 0.0504
Root MSE = .41948
------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
agea | .0067363 .0007065 9.53 0.000 .0053511 .0081215
1.edu_bi | .1633984 .0137552 11.88 0.000 .1364296 .1903671
_cons | .3985797 .0344417 11.57 0.000 .3310525 .4661069
------------------------------------------------------------------------------
Linear regression Number of obs = 3,279
F(3, 3275) = 72.61
Prob > F = 0.0000
R-squared = 0.0624
Root MSE = .40959
------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
agea | .0084535 .0007334 11.53 0.000 .0070155 .0098914
1.edu_bi | .1423356 .0145026 9.81 0.000 .1139006 .1707706
netustm | .0000658 .0000396 1.66 0.097 -.0000118 .0001435
_cons | .3268182 .0380458 8.59 0.000 .2522222 .4014142
------------------------------------------------------------------------------
Linear regression Number of obs = 3,279
F(4, 3274) = 54.47
Prob > F = 0.0000
R-squared = 0.0624
Root MSE = .40965
------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
agea | .008453 .0007335 11.52 0.000 .0070148 .0098911
1.edu_bi | .1424021 .0145782 9.77 0.000 .1138188 .1709855
netustm | .0000657 .0000396 1.66 0.098 -.000012 .0001434
1.gndr_bi | -.0009763 .0143892 -0.07 0.946 -.029189 .0272364
_cons | .3273472 .0387782 8.44 0.000 .2513153 .4033791
------------------------------------------------------------------------------
Linear regression Number of obs = 3,279
F(7, 3271) = 77.11
Prob > F = 0.0000
R-squared = 0.1412
Root MSE = .39223
------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
agea | .0082132 .0007075 11.61 0.000 .006826 .0096003
1.edu_bi | .1274084 .0143858 8.86 0.000 .0992024 .1556144
netustm | .0000223 .0000386 0.58 0.563 -.0000534 .0000981
1.gndr_bi | -.0016086 .0137437 -0.12 0.907 -.0285558 .0253385
|
cntry_4 |
IT | -.0549815 .0162725 -3.38 0.001 -.0868868 -.0230762
FR | -.3062807 .0194974 -15.71 0.000 -.344509 -.2680524
GB | -.1251441 .0203418 -6.15 0.000 -.1650281 -.0852602
|
_cons | .4760632 .0397109 11.99 0.000 .3982024 .5539239
------------------------------------------------------------------------------Linear regression Number of obs = 3,279
F(7, 3271) = 77.11
Prob > F = 0.0000
R-squared = 0.1412
Root MSE = .39223
------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
agea | .0082132 .0007075 11.61 0.000 .006826 .0096003
1.edu_bi | .1274084 .0143858 8.86 0.000 .0992024 .1556144
netustm | .0000223 .0000386 0.58 0.563 -.0000534 .0000981
1.gndr_bi | -.0016086 .0137437 -0.12 0.907 -.0285558 .0253385
|
cntry_4 |
IT | -.0549815 .0162725 -3.38 0.001 -.0868868 -.0230762
FR | -.3062807 .0194974 -15.71 0.000 -.344509 -.2680524
GB | -.1251441 .0203418 -6.15 0.000 -.1650281 -.0852602
|
_cons | .4760632 .0397109 11.99 0.000 .3982024 .5539239
------------------------------------------------------------------------------Linear regression Number of obs = 3,279
F(7, 3271) = 77.11
Prob > F = 0.0000
R-squared = 0.1412
Root MSE = .39223
------------------------------------------------------------------------------
vote_bi | Coefficient Legend
-------------+----------------------------------------------------------------
agea | .0082132 _b[agea]
1.edu_bi | .1274084 _b[1.edu_bi]
netustm | .0000223 _b[netustm]
1.gndr_bi | -.0016086 _b[1.gndr_bi]
|
cntry_4 |
IT | -.0549815 _b[1.cntry_4]
FR | -.3062807 _b[2.cntry_4]
GB | -.1251441 _b[3.cntry_4]
|
_cons | .4760632 _b[_cons]
------------------------------------------------------------------------------P.s., as we endorse theory-informed models, you don’t drop a predictor simply because it’s not statistically significant or has a minimal effect!
We think the effect of gender on voting probability varies differs by country contexts.
Linear regression Number of obs = 3,279
F(10, 3268) = 54.35
Prob > F = 0.0000
R-squared = 0.1422
Root MSE = .39219
---------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
----------------+----------------------------------------------------------------
agea | .008184 .0007081 11.56 0.000 .0067957 .0095723
1.edu_bi | .1276086 .014386 8.87 0.000 .0994021 .1558151
netustm | .0000193 .0000386 0.50 0.618 -.0000564 .0000949
1.gndr_bi | -.0002859 .0206182 -0.01 0.989 -.0407118 .04014
|
cntry_4 |
IT | -.0660775 .0230388 -2.87 0.004 -.1112494 -.0209056
FR | -.307611 .0275381 -11.17 0.000 -.3616047 -.2536173
GB | -.0919438 .0299912 -3.07 0.002 -.1507472 -.0331404
|
gndr_bi#cntry_4 |
1#IT | .0210185 .0307938 0.68 0.495 -.0393585 .0813956
1#FR | .0021501 .03786 0.06 0.955 -.0720816 .0763818
1#GB | -.0589122 .0406298 -1.45 0.147 -.1385746 .0207503
|
_cons | .4775582 .0401806 11.89 0.000 .3987765 .5563399
---------------------------------------------------------------------------------Linear regression Number of obs = 3,279
F(10, 3268) = 54.35
Prob > F = 0.0000
R-squared = 0.1422
Root MSE = .39219
---------------------------------------------------------------------------------
| Robust
vote_bi | Coefficient std. err. t P>|t| [95% conf. interval]
----------------+----------------------------------------------------------------
agea | .008184 .0007081 11.56 0.000 .0067957 .0095723
1.edu_bi | .1276086 .014386 8.87 0.000 .0994021 .1558151
netustm | .0000193 .0000386 0.50 0.618 -.0000564 .0000949
1.gndr_bi | -.0002859 .0206182 -0.01 0.989 -.0407118 .04014
|
cntry_4 |
IT | -.0660775 .0230388 -2.87 0.004 -.1112494 -.0209056
FR | -.307611 .0275381 -11.17 0.000 -.3616047 -.2536173
GB | -.0919438 .0299912 -3.07 0.002 -.1507472 -.0331404
|
gndr_bi#cntry_4 |
1#IT | .0210185 .0307938 0.68 0.495 -.0393585 .0813956
1#FR | .0021501 .03786 0.06 0.955 -.0720816 .0763818
1#GB | -.0589122 .0406298 -1.45 0.147 -.1385746 .0207503
|
_cons | .4775582 .0401806 11.89 0.000 .3987765 .5563399
---------------------------------------------------------------------------------marginsPredictive margins Number of obs = 3,279
Model VCE: Robust
Expression: Linear prediction, predict()
------------------------------------------------------------------------------
| Delta-method
| Margin std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
cntry_4 |
NO | .8886054 .0109678 81.02 0.000 .867101 .9101099
IT | .8333289 .0114871 72.54 0.000 .8108062 .8558516
FR | .5820993 .0159138 36.58 0.000 .5508972 .6133013
GB | .7663881 .0177623 43.15 0.000 .7315616 .8012145
------------------------------------------------------------------------------margins
command
Next: Binary logistic regression
