So Far...
Example Using hsb2
We will look at a model that uses write as the response variable and female and prog as predictors.
use http://www.gseis.ucla.edu/courses/data/hsb2, clear
tab1 female prog
-> tabulation of female
female | Freq. Percent Cum.
------------+-----------------------------------
male | 91 45.50 45.50
female | 109 54.50 100.00
------------+-----------------------------------
Total | 200 100.00
-> tabulation of prog
type of |
program | Freq. Percent Cum.
------------+-----------------------------------
general | 45 22.50 22.50
academic | 105 52.50 75.00
vocation | 50 25.00 100.00
------------+-----------------------------------
Total | 200 100.00
table prog female, cont(mean write sd write freq)
------------------------------
type of | female
program | male female
----------+-------------------
general | 49.14286 53.25
| 10.36478 8.205248
| 21 24
|
academic | 54.61702 57.58621
| 8.656622 7.115672
| 47 58
|
vocation | 41.82609 50.96296
| 8.003705 8.341193
| 23 27
------------------------------
/* 1st model -- no interaction */
anova write female prog
Number of obs = 200 R-squared = 0.2408
Root MSE = 8.32211 Adj R-squared = 0.2291
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 4304.40272 3 1434.80091 20.72 0.0000
|
female | 1128.70487 1 1128.70487 16.30 0.0001
prog | 3128.18888 2 1564.09444 22.58 0.0000
|
Residual | 13574.4723 196 69.2575116
-----------+----------------------------------------------------
Total | 17878.875 199 89.843593
xi: regress write i.female i.prog
i.female _Ifemale_0-1 (naturally coded; _Ifemale_0 omitted)
i.prog _Iprog_1-3 (naturally coded; _Iprog_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 3, 196) = 20.72
Model | 4304.40272 3 1434.80091 Prob > F = 0.0000
Residual | 13574.4723 196 69.2575116 R-squared = 0.2408
-------------+------------------------------ Adj R-squared = 0.2291
Total | 17878.875 199 89.843593 Root MSE = 8.3221
------------------------------------------------------------------------------
write | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Ifemale_1 | 4.771211 1.181876 4.04 0.000 2.440385 7.102037
_Iprog_2 | 4.832929 1.482956 3.26 0.001 1.908331 7.757528
_Iprog_3 | -4.605141 1.710049 -2.69 0.008 -7.9776 -1.232683
_cons | 48.78869 1.391537 35.06 0.000 46.04438 51.533
------------------------------------------------------------------------------
test _Ifemale_1
( 1) _Ifemale_1 = 0
F( 1, 196) = 16.30
Prob > F = 0.0001
test _Iprog_2 _Iprog_3
( 1) _Iprog_2 = 0
( 2) _Iprog_3 = 0
F( 2, 196) = 22.58
Prob > F = 0.0000
/* 2nd model -- interaction */
anova write female prog female*prog
Number of obs = 200 R-squared = 0.2590
Root MSE = 8.26386 Adj R-squared = 0.2399
Source | Partial SS df MS F Prob > F
------------+----------------------------------------------------
Model | 4630.36091 5 926.072182 13.56 0.0000
|
female | 1261.85329 1 1261.85329 18.48 0.0000
prog | 3274.35082 2 1637.17541 23.97 0.0000
female*prog | 325.958189 2 162.979094 2.39 0.0946
|
Residual | 13248.5141 194 68.2913097
------------+----------------------------------------------------
Total | 17878.875 199 89.843593
xi: regress write i.female*i.prog
i.female _Ifemale_0-1 (naturally coded; _Ifemale_0 omitted)
i.prog _Iprog_1-3 (naturally coded; _Iprog_1 omitted)
i.fem~e*i.prog _IfemXpro_#_# (coded as above)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 5, 194) = 13.56
Model | 4630.36091 5 926.072182 Prob > F = 0.0000
Residual | 13248.5141 194 68.2913097 R-squared = 0.2590
-------------+------------------------------ Adj R-squared = 0.2399
Total | 17878.875 199 89.843593 Root MSE = 8.2639
------------------------------------------------------------------------------
write | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Ifemale_1 | 4.107143 2.469299 1.66 0.098 -.7629756 8.977261
_Iprog_2 | 5.474164 2.169095 2.52 0.012 1.196128 9.7522
_Iprog_3 | -7.31677 2.494224 -2.93 0.004 -12.23605 -2.397493
_IfemXpro_~2 | -1.137957 2.954299 -0.39 0.701 -6.964625 4.68871
_IfemXpro_~3 | 5.029733 3.40528 1.48 0.141 -1.68639 11.74586
_cons | 49.14286 1.803321 27.25 0.000 45.58623 52.69949
------------------------------------------------------------------------------
test _IfemXpro_1_2 _IfemXpro_1_3
( 1) _IfemXpro_1_2 = 0
( 2) _IfemXpro_1_3 = 0
F( 2, 194) = 2.39
Prob > F = 0.0946
test _Ifemale_1
( 1) _Ifemale_1 = 0
F( 1, 194) = 2.77
Prob > F = 0.0979
test _Iprog_2 _Iprog_3
( 1) _Iprog_2 = 0
( 2) _Iprog_3 = 0
F( 2, 194) = 18.69
Prob > F = 0.0000
/* cannot use dummy coding -- need to use effect coding */
/* xi3 will produce effect coding -- findit xi3 */
xi3: regress write e.female*e.prog
This is an experimental version of xi3
Please view results with some caution
d.female _Ifemale_0-1 (naturally coded; _Ifemale_0 omitted)
d.prog _Iprog_1-3 (naturally coded; _Iprog_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 5, 194) = 13.56
Model | 4630.36091 5 926.072182 Prob > F = 0.0000
Residual | 13248.5141 194 68.2913097 R-squared = 0.2590
-------------+------------------------------ Adj R-squared = 0.2399
Total | 17878.875 199 89.843593 Root MSE = 8.2639
------------------------------------------------------------------------------
write | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Ifemale_1 | 2.702201 .6286312 4.30 0.000 1.462372 3.94203
_Iprog_2 | 4.870758 .7838244 6.21 0.000 3.324847 6.41667
_Iprog_3 | -4.836331 .9237884 -5.24 0.000 -6.658289 -3.014373
_Ife1Xpr2 | -1.217608 .7838244 -1.55 0.122 -2.763519 .3283035
_Ife1Xpr3 | 1.866237 .9237884 2.02 0.045 .0442794 3.688195
_cons | 51.23086 .6286312 81.50 0.000 49.99103 52.47068
------------------------------------------------------------------------------
tablist prog _Iprog_2 _Iprog_3 /* findit tablist */
+---------------------------------------+
| prog _Iprog_2 _Iprog_3 Freq |
|---------------------------------------|
| academic 1 0 105 |
| vocation 0 1 50 |
| general -1 -1 45 |
+---------------------------------------+
test _Ife1Xpr2 _Ife1Xpr3
( 1) _Ife1Xpr2 = 0
( 2) _Ife1Xpr3 = 0
F( 2, 194) = 2.39
Prob > F = 0.0946
test _Ifemale_1
( 1) _Ifemale_1 = 0
F( 1, 194) = 18.48
Prob > F = 0.0000
test _Iprog_2 _Iprog_3
( 1) _Iprog_2 = 0
( 2) _Iprog_3 = 0
F( 2, 194) = 23.97
Prob > F = 0.0000
Phil Ender, 18dec99