* Class starts here. Always open a Stata log at the beginning of every work session.
. ttest yrsed if age>=25 & age<=34, by(sex)
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
Male | 9027 13.31212 .0312351 2.967666 13.25089 13.37335
Female | 9511 13.55657 .0292693 2.854472 13.49919 13.61394
---------+--------------------------------------------------------------------
combined | 18538 13.43753 .0213921 2.912627 13.3956 13.47946
---------+--------------------------------------------------------------------
diff | -.2444469 .0427623 -.3282649 -.1606289
------------------------------------------------------------------------------
diff = mean(Male) - mean(Female) t = -5.7164
Ho: diff = 0 degrees of freedom = 18536
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000
* The T-statistic for the difference between men’s education and women’s education (in the 25-34 age group) is -5.7. What probability is associated with a t-statistic of -5.7? The answer is, as is shown below, about 5 parts in a billion. If we double it, to get the probability in both tails, we end up with 1 in 100 million i.e., 1.05 x 10-8. As the course goes on, we will endeavor to explain this in more detail.
. display 1-ttail(18536,-5.7164)
5.524e-09
. display ttail(18536,5.7164)
5.524e-09
. display 2*ttail(18536,5.7164)
1.105e-08
. summarize incwelfr
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 103226 40.62242 478.8231 0 25000
. summarize incwelfr if age>=15 & incwelfr>0 & incwelfr~=.
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 1289 3253.134 2813.505 1 25000
* There are 1289 welfare recipients in the CPS.
. summarize incwelfr if age>=15 & incwelfr>0 & incwelfr~=. [fweight=perwt_rounded]
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incwelfr | 2551246 3072.095 2803.442 1 25000
*Applying the weights, we see that there are 2.5 million welfare recipients in the US in March, 2000.
. display 2551246*3072
7.837e+09
* The display command is an in-line calculator. Multiplying the number of welfare recipients by the average 1999 welfare income yields $7.8 billion in total welfare expenditures. Does that sound like a lot? It is only $40 per US adult.
* Now on to the syntax for creating new variables. Use the generate command, or gen for short:
. gen byte receives_welfare=0
. replace receives_welfare =1 if incwelfr>0 & incwelfr~=.
(1289 real changes made)
* This next command generates a label that associates the value 0 with the text “no welfare” and the value 1 with the text “receives welfare”
. label define receives_welfare_lbl 0 "no welfare" 1 "receives welfare"
* This next command associates the above defined value label with the variable receives_welfare. And at this point, if you wanted to save the newly created variable with the rest of your dataset, it would be good to
. label val receives_welfare receives_welfare_lbl
. tabulate receives_welfare [fweight=perwt_rounded]
receives_welfare | Freq. Percent Cum.
-----------------+-----------------------------------
no welfare |271,536,575 99.07 99.07
receives welfare | 2,551,246 0.93 100.00
-----------------+-----------------------------------
Total |274,087,821 100.00
. summarize perwt_rounded
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
perwt_roun~d | 133710 2049.868 1083.244 93 14281
* The average weight is 2049.
* Not only can the values get labels, but the variable itself can get a label (note label “var” here compared to label “val” above)
. label var receives_welfare "did respondent receive welfare in 1999"
. summarize perwt_rounded, detail
integer perwt, negative values recoded to 0
-------------------------------------------------------------
Percentiles Smallest
1% 284 93
5% 428 93
10% 603 93 Obs 133710
25% 1188 96 Sum of Wgt. 133710
50% 2049 Mean 2049.868
Largest Std. Dev. 1083.244
75% 2649 11824
90% 3534 12547 Variance 1173417
95% 3967 12905 Skewness .6144906
99% 4893 14281 Kurtosis 4.006292
. table receives_welfare sex [fweight= perwt_rounded] , contents(freq mean age mean yrsed mean incwage) row col
--------------------------------------------------------
did respondent |
receive welfare | Sex
in 1999 | Male Female Total
-----------------+--------------------------------------
no welfare | 1.34e+08 1.38e+08 2.72e+08
| 34.2 36.4 35.3
| 12.92792 12.90996 12.9187
| 26619.92881 14124.35177 20203.23216
|
receives welfare | 357,702 2193544 2551246
| 34.8 32.8 33.1
| 10.75763 11.14463 11.09037
| 4196.737659 3577.073717 3663.954806
|
Total | 1.34e+08 1.40e+08 2.74e+08
| 34.2 36.3 35.3
| 12.92039 12.87497 12.89688
| 26542.14272 13915.27974 20005.84709
--------------------------------------------------------
. codebook sex
--------------------------------------------------------------------------------------------------
sex Sex
--------------------------------------------------------------------------------------------------
type: numeric (byte)
label: sexlbl
range: [1,2] units: 1
unique values: 2 missing .: 0/133710
tabulation: Freq. Numeric Label
64791 1 Male
68919 2 Female
. codebook race
--------------------------------------------------------------------------------------------------
race Race
--------------------------------------------------------------------------------------------------
type: numeric (int)
label: racelbl
range: [100,650] units: 10
unique values: 4 missing .: 0/133710
tabulation: Freq. Numeric Label
1.1e+05 100 White
13626 200 Black/Negro
1894 300 American Indian/Aleut/Eskimo
4715 650 Asian or Pacific Islander
. table receives_welfare sex, contents(freq mean incwelfr) row col
--------------------------------------------
did respondent |
receive welfare | Sex
in 1999 | Male Female Total
-----------------+--------------------------
no welfare | 64,603 67,818 132,421
| 0 0 0
|
receives welfare | 188 1,101 1,289
| 2980 3300 3253
|
Total | 64,791 68,919 133,710
| 11 67 41
--------------------------------------------
* A key variable for HW 1:
. tabulate citizen
Citizenship status | Freq. Percent Cum.
--------------------------------+-----------------------------------
NIU | 117,310 87.73 87.73
Born abroad of American parents | 976 0.73 88.46
Naturalized citizen | 5,348 4.00 92.46
Not a citizen | 10,076 7.54 100.00
--------------------------------+-----------------------------------
Total | 133,710 100.00
. codebook citizen
--------------------------------------------------------------------------------------------------
citizen Citizenship status
--------------------------------------------------------------------------------------------------
type: numeric (byte)
label: citizenlbl
range: [0,3] units: 1
unique values: 4 missing .: 0/133710
tabulation: Freq. Numeric Label
1.2e+05 0 NIU
976 1 Born abroad of American parents
5348 2 Naturalized citizen
10076 3 Not a citizen
* The people who are NIU in the variable citizen, where were they born? Answer: US.
. tabulate bpl if citizen==0
Birthplace | Freq. Percent Cum.
----------------------------+-----------------------------------
United States, n.s. | 116,213 99.06 99.06
Puerto Rico | 950 0.81 99.87
U.S. outlying areas, n.s. | 140 0.12 99.99
Mexico | 4 0.00 100.00
El Salvador | 3 0.00 100.00
----------------------------+-----------------------------------
Total | 117,310 100.00
. log close
name: <unnamed>
log: C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\Soc180B_spr2019_logs\class2_l
> og.log
log type: text
closed on: 4 Apr 2019, 16:29:31
--------------------------------------------------------------------------------------------------