--------------------------------------------------------------------------------------------------
name: <unnamed>
log: C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\Soc180B_spr2019_logs\class6_l
> og.log
log type: text
opened on: 18 Apr 2019, 14:40:54
. use "C:\Users\mexmi\Desktop\cps_mar_2000_new.dta", clear
. *class starts here
. ttest yrsed if age>24 & age<35, 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
* above is our classic t-test. Below is the regression version of the same test. The “i.sex” part is just to tell stata that sex is a categorical variable, so not to take the actual values seriously, but to create a 0-1 dummy variable for sex, the way we do below by hand.
. regress yrsed i.sex if age>24 & age<35
Source | SS df MS Number of obs = 18538
-------------+------------------------------ F( 1, 18536) = 32.68
Model | 276.742433 1 276.742433 Prob > F = 0.0000
Residual | 156979.922 18536 8.46892111 R-squared = 0.0018
-------------+------------------------------ Adj R-squared = 0.0017
Total | 157256.664 18537 8.48339343 Root MSE = 2.9101
------------------------------------------------------------------------------
yrsed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sex |
Female | .2444469 .0427623 5.72 0.000 .1606289 .3282649
_cons | 13.31212 .0306297 434.62 0.000 13.25208 13.37216
------------------------------------------------------------------------------
. 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
* creating the dummy variable by hand.
. gen byte male=0 if sex==2
(64791 missing values generated)
. replace male=1 if sex==1
(64791 real changes made)
. tabulate sex male
| male
Sex | 0 1 | Total
-----------+----------------------+----------
Male | 0 64,791 | 64,791
Female | 68,919 0 | 68,919
-----------+----------------------+----------
Total | 68,919 64,791 | 133,710
. regress yrsed male if age>24 & age<35
Source | SS df MS Number of obs = 18538
-------------+------------------------------ F( 1, 18536) = 32.68
Model | 276.742433 1 276.742433 Prob > F = 0.0000
Residual | 156979.922 18536 8.46892111 R-squared = 0.0018
-------------+------------------------------ Adj R-squared = 0.0017
Total | 157256.664 18537 8.48339343 Root MSE = 2.9101
------------------------------------------------------------------------------
yrsed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
male | -.2444469 .0427623 -5.72 0.000 -.3282649 -.1606289
_cons | 13.55657 .0298401 454.31 0.000 13.49808 13.61506
------------------------------------------------------------------------------
------------------------------------------------------------------------------
* We did not quite get to it in class, but it is easy to add weights to a regression. Aweights gives you a reasonable answer.
. regress yrsed i.sex if age>24 & age<35 [aweight= perwt_rounded]
(sum of wgt is 3.7786e+07)
Source | SS df MS Number of obs = 18538
-------------+------------------------------ F( 1, 18536) = 25.52
Model | 195.741395 1 195.741395 Prob > F = 0.0000
Residual | 142186.809 18536 7.67084641 R-squared = 0.0014
-------------+------------------------------ Adj R-squared = 0.0013
Total | 142382.551 18537 7.6809921 Root MSE = 2.7696
------------------------------------------------------------------------------
yrsed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sex |
Female | .2055446 .0406899 5.05 0.000 .1257887 .2853005
_cons | 13.5574 .0290221 467.14 0.000 13.50051 13.61429
------------------------------------------------------------------------------
* Fweights gives you a very unreasonable answer (in the SE an T stats), because it unreasonably inflates the sample size by a factor of 2000.
. regress yrsed i.sex if age>24 & age<35 [fweight= perwt_rounded]
Source | SS df MS Number of obs =37785945
-------------+------------------------------ F( 1,37785943) =52018.00
Model | 398979.047 1 398979.047 Prob > F = 0.0000
Residual | 28981891037785943 7.67001924 R-squared = 0.0014
-------------+------------------------------ Adj R-squared = 0.0014
Total | 29021788937785944 7.68057796 Root MSE = 2.7695
------------------------------------------------------------------------------
yrsed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sex |
Female | .2055446 .0009012 228.07 0.000 .2037782 .2073109
_cons | 13.5574 .0006428 2.1e+04 0.000 13.55614 13.55866
------------------------------------------------------------------------------
. log close
name: <unnamed>
log: C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\Soc180B_spr2019_logs\class6_l
> og.log
log type: text
closed on: 18 Apr 2019, 16:24:35
--------------------------------------------------------------------------------------------------