| Thanks for that, Timothy. I have two
further questions.
|
| 2. One of the chief complaints about SPSS is that the output
| for many procedures is unwieldy. It often contains a lot of
| stuff that (a lot of people think) ought to be optional
| rather than automatic (e.g., multivariate results for
| repeated measures ANOVA). And on the flip side, there are
| things that perhaps ought to be automatic that are optional
| (e.g., cell means for ANOVA models obtained via GLM). Can
| anyone comment on how Stata's output compares to that of SPSS?
|
|
| 2. One of the chief complaints about SPSS is that the output
| for many procedures is unwieldy. It often contains a lot of
| stuff that (a lot of people think) ought to be optional
| rather than automatic (e.g., multivariate results for
| repeated measures ANOVA). And on the flip side, there are
| things that perhaps ought to be automatic that are optional
| (e.g., cell means for ANOVA models obtained via GLM). Can
| anyone comment on how Stata's output compares to that of SPSS?
|
Stata has a different approach than SPSS to output.
here is a command for regression and all of the output:
regress
mpg foreign displacement weight, beta
Source
| SS
df
MS
Number of obs =
74
-------------+------------------------------ F( 3, 70) = 34.70
Model | 1600.12629 3 533.375429 Prob > F = 0.0000
Residual | 1076.03588 70 15.3719411 R-squared = 0.5979
-------------+------------------------------ Adj R-squared = 0.5807
Total | 2676.16216 73 36.6597556 Root MSE = 3.9207
-------------+------------------------------ F( 3, 70) = 34.70
Model | 1600.12629 3 533.375429 Prob > F = 0.0000
Residual | 1076.03588 70 15.3719411 R-squared = 0.5979
-------------+------------------------------ Adj R-squared = 0.5807
Total | 2676.16216 73 36.6597556 Root MSE = 3.9207
------------------------------------------------------------------------------
mpg | Coef. Std. Err. t P>|t| Beta
-------------+----------------------------------------------------------------
foreign | -1.972259 1.272818 -1.55 0.126 -.1499012
displacement | -.0042919 .0115094 -0.37 0.710 -.0650984
weight | -.0061926 .0013333 -4.64 0.000 -.7948907
_cons | 41.56386 2.686682 15.47 0.000 .
------------------------------------------------------------------------------
At this point there are any number of specialized things you might want to do. Soooo, Stata gives you basic output in a condensed way and leaves it to you if you want more. Here
mpg | Coef. Std. Err. t P>|t| Beta
-------------+----------------------------------------------------------------
foreign | -1.972259 1.272818 -1.55 0.126 -.1499012
displacement | -.0042919 .0115094 -0.37 0.710 -.0650984
weight | -.0061926 .0013333 -4.64 0.000 -.7948907
_cons | 41.56386 2.686682 15.47 0.000 .
------------------------------------------------------------------------------
At this point there are any number of specialized things you might want to do. Soooo, Stata gives you basic output in a condensed way and leaves it to you if you want more. Here
is an example using logistic regression
with additional features. You can get the additional features by entering the
command "findit listcoef
. logit foreign mpg weight length
displacement
Iteration 0: log likelihood
= -45.03321
Iteration 1: log likelihood = -27.876913
Iteration 2: log likelihood = -23.541419
Iteration 3: log likelihood = -21.694712
Iteration 4: log likelihood = -20.779084
Iteration 5: log likelihood = -20.504477
Iteration 6: log likelihood = -20.478582
Iteration 7: log likelihood = -20.478309
Iteration 8: log likelihood = -20.478308
Iteration 1: log likelihood = -27.876913
Iteration 2: log likelihood = -23.541419
Iteration 3: log likelihood = -21.694712
Iteration 4: log likelihood = -20.779084
Iteration 5: log likelihood = -20.504477
Iteration 6: log likelihood = -20.478582
Iteration 7: log likelihood = -20.478309
Iteration 8: log likelihood = -20.478308
Logit
estimates
Number of obs =
74
LR chi2(4) = 49.11
Prob > chi2 = 0.0000
Log likelihood = -20.478308 Pseudo R2 = 0.5453
LR chi2(4) = 49.11
Prob > chi2 = 0.0000
Log likelihood = -20.478308 Pseudo R2 = 0.5453
------------------------------------------------------------------------------
foreign | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -.1728742 .0939972 -1.84 0.066 -.3571053 .0113568
weight | .0012032 .0029597 0.41 0.684 -.0045977 .007004
length | .0071799 .0706211 0.10 0.919 -.1312349 .1455946
displacement | -.0796186 .0363671 -2.19 0.029 -.1508968 -.0083404
_cons | 10.05333 9.018975 1.11 0.265 -7.623534 27.7302
------------------------------------------------------------------------------
foreign | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -.1728742 .0939972 -1.84 0.066 -.3571053 .0113568
weight | .0012032 .0029597 0.41 0.684 -.0045977 .007004
length | .0071799 .0706211 0.10 0.919 -.1312349 .1455946
displacement | -.0796186 .0363671 -2.19 0.029 -.1508968 -.0083404
_cons | 10.05333 9.018975 1.11 0.265 -7.623534 27.7302
------------------------------------------------------------------------------
note: 1 failure and 0 successes completely
determined.
. listcoef
logit (N=74): Factor Change in Odds
Odds of: Foreign vs
Domestic
----------------------------------------------------------------------
foreign | b z P>|z| e^b e^bStdX SDofX
-------------+--------------------------------------------------------
mpg | -0.17287 -1.839 0.066 0.8412 0.3511 6.0547
weight | 0.00120 0.407 0.684 1.0012 2.5474 777.1936
length | 0.00718 0.102 0.919 1.0072 1.1734 22.2663
displacement | -0.07962 -2.189 0.029 0.9235 0.0007 91.8372
----------------------------------------------------------------------
foreign | b z P>|z| e^b e^bStdX SDofX
-------------+--------------------------------------------------------
mpg | -0.17287 -1.839 0.066 0.8412 0.3511 6.0547
weight | 0.00120 0.407 0.684 1.0012 2.5474 777.1936
length | 0.00718 0.102 0.919 1.0072 1.1734 22.2663
displacement | -0.07962 -2.189 0.029 0.9235 0.0007 91.8372
----------------------------------------------------------------------
. listcoef, percent
logit (N=74): Percentage Change in Odds
Odds of: Foreign vs
Domestic
----------------------------------------------------------------------
foreign | b z P>|z| % %StdX SDofX
-------------+--------------------------------------------------------
mpg | -0.17287 -1.839 0.066 -15.9 -64.9 6.0547
weight | 0.00120 0.407 0.684 0.1 154.7 777.1936
length | 0.00718 0.102 0.919 0.7 17.3 22.2663
displacement | -0.07962 -2.189 0.029 -7.7 -99.9 91.8372
----------------------------------------------------------------------
foreign | b z P>|z| % %StdX SDofX
-------------+--------------------------------------------------------
mpg | -0.17287 -1.839 0.066 -15.9 -64.9 6.0547
weight | 0.00120 0.407 0.684 0.1 154.7 777.1936
length | 0.00718 0.102 0.919 0.7 17.3 22.2663
displacement | -0.07962 -2.189 0.029 -7.7 -99.9 91.8372
----------------------------------------------------------------------
.
The listcoef gives you the exponentiated B's and what they are for a one standard deviation change in the predictor. A one standard deviation shift in weight more than doubles the odds it is a domestic car. The listcoef, percent shows you that their is a 155% increase in the odds it is a domestic car for a one standard deviation shift in weight. The listcoef is just one example of an extension on basic logistic regression to help you interpret it.
The listcoef gives you the exponentiated B's and what they are for a one standard deviation change in the predictor. A one standard deviation shift in weight more than doubles the odds it is a domestic car. The listcoef, percent shows you that their is a 155% increase in the odds it is a domestic car for a one standard deviation shift in weight. The listcoef is just one example of an extension on basic logistic regression to help you interpret it.
Alan Acock
