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This chapter makes use of the chdaga.dta file.
use chdage
Figure 1.1 -- page 4
graph twoway scatter chd age, ylabel(0 1)
Table 1.2 -- page 4
/* create the age categories */
recode age 20/29=1 30/34=2 35/39=3 40/44=4 45/49=5 50/54=6 55/59=7 60/69=8, gen(agecat)
tabulate agecat chd
| chd
agecat | 0 1 | Total
-----------+----------------------+----------
29 | 9 1 | 10
34 | 13 2 | 15
39 | 9 3 | 12
44 | 10 5 | 15
49 | 7 6 | 13
54 | 3 5 | 8
59 | 4 13 | 17
69 | 2 8 | 10
-----------+----------------------+----------
Total | 57 43 | 100
table agecat, c(mean chd)
----------+-----------
agecat | mean(chd)
----------+-----------
29 | .1
34 | .1333333
39 | .25
44 | .3333333
49 | .4615385
54 | .625
59 | .7647059
69 | .8
----------+-----------
Figure 1.2 -- page 5
collapse (count) tot=chd (sum) present=chd, by(agecat) gen prop = present / tot graph twoway scatter prop agecat, ylabel(0(.2)1)
Table 1.3 -- page 11
use chdage, clear
logit chd age
Iteration 0: log likelihood = -68.331491
Iteration 1: log likelihood = -54.170558
Iteration 2: log likelihood = -53.681645
Iteration 3: log likelihood = -53.676547
Iteration 4: log likelihood = -53.676546
Logit estimates Number of obs = 100
LR chi2(1) = 29.31
Prob > chi2 = 0.0000
Log likelihood = -53.676546 Pseudo R2 = 0.2145
------------------------------------------------------------------------------
chd | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
age | .1109211 .0240598 4.610 0.000 .0637647 .1580776
_cons | -5.309453 1.133655 -4.683 0.000 -7.531376 -3.087531
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