class 6 notes

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log: C:\AAA Miker Files\newer web pages\soc_meth_proj3\class6_2009.log

log type: text

opened on: 12 Feb 2009, 11:15:35

. set mem 200m

Current memory allocation

current memory usage

settable value description (1M = 1024k)

--------------------------------------------------------------------

set maxvar 5000 max. variables allowed 1.909M

set memory 200M max. data space 200.000M

set matsize 400 max. RHS vars in models 1.254M

-----------

203.163M

. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_mar_2000_new.dta", clear

. table occ1990 if occ1990==178|occ1990==95, contents(mean incwage sd incwage freq)

---------------------------------------------------------------

Occupation, 1990 |

basis | mean(incwage) sd(incwage) Freq.

------------------+--------------------------------------------

Registered nurses | 37536.85197 21839.96 966

Lawyers | 74044.32653 69032.96 441

---------------------------------------------------------------

. ttest incwage if occ1990==178|occ1990==95, by(occ1990)

Two-sample t test with equal variances

------------------------------------------------------------------------------

Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

---------+--------------------------------------------------------------------

Lawyers | 441 74044.33 3287.284 69032.96 67583.6 80505.06

---------+--------------------------------------------------------------------

combined | 1407 48979.49 1223.363 45888.34 46579.68 51379.31

---------+--------------------------------------------------------------------

diff | -36507.47 2451.758 -41316.97 -31697.97

------------------------------------------------------------------------------

diff = mean(Register) - mean(Lawyers) t = -14.8903

Ho: diff = 0 degrees of freedom = 1405

Ha: diff < 0 Ha: diff != 0 Ha: diff > 0

Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000

. ttest incwage if occ1990==178|occ1990==95, by(occ1990) unequal

Two-sample t test with unequal variances

------------------------------------------------------------------------------

Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

---------+--------------------------------------------------------------------

Lawyers | 441 74044.33 3287.284 69032.96 67583.6 80505.06

---------+--------------------------------------------------------------------

combined | 1407 48979.49 1223.363 45888.34 46579.68 51379.31

---------+--------------------------------------------------------------------

diff | -36507.47 3361.548 -43112.62 -29902.33

------------------------------------------------------------------------------

diff = mean(Register) - mean(Lawyers) t = -10.8603

Ho: diff = 0 Satterthwaite's degrees of freedom = 480.671

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 unequal t-statistic is the formula we used before. The equal variance t-statistic is the default assumption, and it is also the t-statistic generated by standard regressions.

. gen byte lawyers=0

. replace lawyers=1 if occ1990==178

(441 real changes made)

. regress incwage lawyers if occ1990==178|occ1990==95

Source | SS df MS Number of obs = 1407

-------------+------------------------------ F( 1, 1405) = 221.72

Model | 4.0354e+11 1 4.0354e+11 Prob > F = 0.0000

Residual | 2.5571e+12 1405 1.8200e+09 R-squared = 0.1363

-------------+------------------------------ Adj R-squared = 0.1357

Total | 2.9607e+12 1406 2.1057e+09 Root MSE = 42662

------------------------------------------------------------------------------

incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

lawyers | 36507.47 2451.758 14.89 0.000 31697.97 41316.97

_cons | 37536.85 1372.618 27.35 0.000 34844.25 40229.45

------------------------------------------------------------------------------

. *note that standard OLS regression gives us the equal variance t-statistic, 14.89

. display ttail(25,2.49)

.00989098

. *Just as with Freedman's table, this gives us 1% tail. But we generally want two-tailed tests, so we double it to 2%

. display 2*ttail(25,2.49)

.01978195

. display 2*ttail(1400,2.49)

.01288957

. *Instead of 2% we would have a 2-tail probability of 1.3% if we had N of 1400 instead of N of 25

. display 2*ttail(1400,14.89)

1.167e-46

. display normal(1.96)

.9750021

. *the normal function gives you the cumulative normal density up to the value, leaving one tail. The one-tail probability for Z-score of 1.96 is 2.5%, the two-tail probability is 5%

. display 2*(1-normal(1.96))

.04999579

. save "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_mar_2000_new.dta", replace

file C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_mar_2000_new.dta saved

. *how does the two tail probability of the normal differ from the two tail probability of the T-statistic? It depends on N. Check Freedman’s table...

. exit, clear