I wrote Dap to use in my statistical consulting practice because the utterly famous, industry standard statistics system (you know the one I mean) is not available on GNU/Linux and costs a bundle every year under a lease arrangement. I was generally happy with that system, except for the graphics, which are all but impossible to use, but there were a number of clumsy constructs left over from its ancient origins. Thus, I decided to mimic the core of the functionality of that system in the context of the C language, which allows much more programming flexibility.
Dap is a GNU program and is free software distributed under a GNU-style copyleft.
The following instructions tell you how to install Dap.
GENERALITIES
This document concerns building and installing Dap from sources.
Dap will configure and build under a number of common Unix-like
platforms.The
directions here are for GNU/Linux; the configure and build for other
platforms are similar.
Dap is a GNU program and is free software distributed under a GNU-style copyleft. See the file COPYING for details.
GETTING AND UNPACKING THE SOURCES
The simplest way is to download the most recent `dap-x.y.tar.gz' package and untar it with the command:
gunzip -c dap-x.y.tar.gz | tar -xvf -
COMPILATION
Choose a place to install dap and its documentation. Let's
call
this place `DAP_HOME'. Untar the source code. This should
create
directories
`src', `doc', and `examples'.
If you want the executables, includes, library, and info files
installed
in subdirectories `bin', `include', `lib', and `info', respectively,
of
`/usr/local', then simply issue the following commands:
./configure
make install
(Note the dot `.' before the slash `/' in the first command.)
Otherwise,
edit `Makefile.in' to select a different place to install and then
type
the above
commands. Note: when DAP_HOME/src/dap.c compiles, you will get a
warning:
implicit declaration of function `strcat'
and when DAP_HOME/src/dap0.c compiles, you will get a warning:
implicit declaration of function `dap_main'
Ignore these warnings. Now rehash.
ENVIRONMENT
The following environment variables are used by Dap:
DAPEDITOR for editing programs to run under
Dap
DAPEDOPTS options for that editor
DAPPAGER for viewing tabular output
from
Dap
DAPPAGEOPTS options for that pager
DAPCOMPILER for compiling programs to run under Dap
DAPCOMPOPTS options for that compiler
DAPVIEWER for viewing graphical output
from
Dap
DAPVIEWOPTS options for that viewer
DAPPP path name
for
the Dap preprocessor
(default: /usr/local/bin/dappp)
INCDIR directory for
compiler
to find <dap.h>
(default: /usr/local/include)
LIBDIR directory for
compiler
to find libdap.a
(default: /usr/local/lib)
All but the last three are further documented in the manual.
If your editor for programs to run under Dap does not run in its own window, you should uncomment out the call to `waitpid' at the end of the function `edrun' in `dap.c'. Alternatively, you can comment out the call to `edrun' entirely and just run the editor on your own.
READING THE MANUAL
To read the manual in info, you will need to have `/usr/local/info'
(or whatever directory you installed the info file in) in your
`INFOPATH'.
The
following command (which you will probably want to put in
yourshell's
`rc' file) will do that if your shell is `csh':
setenv INFOPATH ".:/usr/info:/usr/local/info"
Then issue the command:
info dap
If you prefer dvi or html manuals, they can be made from the file:
DAP_HOME/doc/dap.texi
(See the documentation for texi2dvi, dvips, and texinfo.)
The manual will tell you how to run and use dap. The program and data files for the examples in the manual are in the directory DAP_HOME/doc/examples.
MACHINE DEPENDENCY
Dap assumes that you have a machine with 64-bit double precision floating point numbers conforming to the IEEE floating point standard. If that is not the case, then you may have to modify `machdep.c'; good luck.
BUG REPORTS AND COMMENTS
Send bug reports to <bug-dap@gnu.org>
If you use dap, please let me know about your experience using it,
and
suggestions, by mailing
to <dap-users@gnu.org>. Thanks.
Sample output [Back to Table of Contents]
The following are samples of tabular output from Dap. They, the programs that produced them, and graphical output (not shown here) are all provided with the distribution. These examples are from:
[AMD] Milliken, G.A. and Johnson, D.E. 1984. Analysis of
Messy
Data. Van Nostrand Reinhold: New York. 473pp.
[ED] Cochran, W.G. and Cox, G.M. 1957. Experimental
Designs.
John Wiley & Sons: New York. 611pp.
[MS] Bickel, P.J. and Doksum, K.A. 1977. Mathematical Statistics:
Basic
Ideas and Selected Topics. Holden-Day: Oakland. 493 pp.
[LM] Rao, C.R. and Toutenberg, H. 1995. Linear Models: Least
Squares
and Alternatives. Springer-Verlag: New York. 352 pp.
[CDA] Agresti, A. 1990. Categorical Data
Analysis.
John Wiley & Sons: New York. 558pp.
Unbalanced ANOVA Crossed, nested ANOVA Random model, unbalanced Mixed model, balanced Mixed model, unbalanced Split plot Latin square Missing treatment combinations Linear regression Linear regression, model building Ordinal cross-classification Stratified 2x2 tables Loglinear models Logistic regression
Unbalanced ANOVA [Back
to Sample output]
AMD: pp. 128 - 134
=================================
Dap 1. Sun Dec 29 19:55:55 2002
Response variable: y
Treatment Levels
-------- ------
treat
treat1 treat2
block
block1 block2 block3
=================================
Dap 2. Sun Dec 29 19:55:55 2002
Testing Ho: treat block treat*block
Number of observations = 16
H0 SS = 238.937, df = 5, MS = 47.7875
Error SS = 20, df = 10, MS = 2
R-sq = 0.922761
F0 = 23.8937
Prob[F > F0] = 0.00003
=================================
Dap 3. Sun Dec 29 19:55:55 2002
Testing interaction
Testing Ho: treat*block
Number of observations = 16
H0 SS = 71.6308, df = 2, MS = 35.8154
Error SS = 20, df = 10, MS = 2
F0 = 17.9077
Prob[F > F0] = 0.00050
=================================
Dap 4. Sun Dec 29 19:55:55 2002
Testing treatment
Testing Ho: treat
Number of observations = 16
H0 SS = 61.7143, df = 1, MS = 61.7143
Error SS = 20, df = 10, MS = 2
F0 = 30.8571
Prob[F > F0] = 0.00025
=================================
Dap 5. Sun Dec 29 19:55:55 2002
Testing treatment
Least-squares means for: treat
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 6. Sun Dec 29 19:55:55 2002
Testing treatment
_stat_ for _lsm_ / treat
==============================================
|
|23 |27
|
|--------+--------+--------|--------+--------|
|_type_ |_LSMEAN_|_treat |treat1
|treat2
|
|========|========|========|========|========|
|EFFN |0
| | 7.7143|
7.7143|
|--------+--------+--------|--------+--------|
|LSMDIFF |23 |treat1
|
0.0000| 4.0000|
|
|--------+--------|--------+--------|
|
|27
|treat2 | -4.0000| 0.0000|
|--------+--------+--------|--------+--------|
|MINDIFF |23 |treat1
|
1.6044| 1.6044|
|
|--------+--------|--------+--------|
|
|27
|treat2 | 1.6044| 1.6044|
|--------+--------+--------|--------+--------|
|PROB |23
|treat1
| 1.0000| 0.0002|
|
|--------+--------|--------+--------|
|
|27
|treat2 | 0.0002| 1.0000|
----------------------------------------------
=================================
Dap 7. Sun Dec 29 19:55:55 2002
Testing block
Testing Ho: block
Number of observations = 16
H0 SS = 77.1692, df = 2, MS = 38.5846
Error SS = 20, df = 10, MS = 2
F0 = 19.2923
Prob[F > F0] = 0.00037
=================================
Dap 8. Sun Dec 29 19:55:55 2002
Testing block
Least-squares means for: block
Tukey method
Minimum significant differences are for level 0.05000
=================================
Dap 9. Sun Dec 29 19:55:58 2002
Testing block
_stat_ for _lsm_ / block
=======================================================
|
|23 |24
|28
|
|--------+--------+--------|--------+--------+--------|
|_type_ |_LSMEAN_|_block |block1
|block2
|block3 |
|========|========|========|========|========|========|
|EFFN |0
| | 4.8000|
4.8000|
6.0000|
|--------+--------+--------|--------+--------+--------|
|LSMDIFF |23 |block1
|
0.0000| 1.0000| 5.0000|
|
|--------+--------|--------+--------+--------|
|
|24
|block2 | -1.0000| 0.0000| 4.0000|
|
|--------+--------|--------+--------+--------|
|
|28
|block3 | -5.0000| -4.0000| 0.0000|
|--------+--------+--------|--------+--------+--------|
|MINDIFF |23 |block1
|
2.5024| 2.5024| 2.3740|
|
|--------+--------|--------+--------+--------|
|
|24
|block2 | 2.5024| 2.5024| 2.3740|
|
|--------+--------|--------+--------+--------|
|
|28
|block3 | 2.3740| 2.3740| 2.2382|
|--------+--------+--------|--------+--------+--------|
|PROB |23
|block1
| 1.0000| 0.5381| 0.0005|
|
|--------+--------|--------+--------+--------|
|
|24
|block2 | 0.5381| 1.0000| 0.0025|
|
|--------+--------|--------+--------+--------|
|
|28
|block3 | 0.0005| 0.0025| 1.0000|
-------------------------------------------------------
Crossed, nested ANOVA [Back to Sample output]
AMD: pp. 249 - 251
=================================
Dap 1. Sun Dec 29 19:56:21 2002
Response variable: y
Treatment Levels
-------- ------
a
1 2
b
1 2
c
1 2
=================================
Dap 2. Sun Dec 29 19:56:21 2002
Testing Ho: a b a*b b*c a*b*c
Number of observations = 16
H0 SS = 845.438, df = 7, MS = 120.777
Error SS = 6.5, df = 8, MS = 0.8125
R-sq = 0.99237
F0 = 148.648
Prob[F > F0] = 0.00001
=================================
Dap 3. Sun Dec 29 19:56:21 2002
Testing Ho: a
Residual: a*b*c
EMS(a*b*c) =
2 Var(a*b*c)
1 Var(Error)
EMS(a) =
8 Var(a)
2 Var(a*b*c)
1 Var(Error)
Error for a =
1 MS(a*b*c)
Number of observations = 16
H0 SS = 39.0625, df = 1, MS = 39.0625
Residual df = 2, MS = 0.8125
F0 = 48.0769
Prob[F > F0] = 0.02018
=================================
Dap 4. Sun Dec 29 19:56:21 2002
Least-squares means for: a
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 5. Sun Dec 29 19:56:21 2002
_stat_ for _lsm_ / a
============================================
|
|29 |32.125 |
|--------+--------+--------|-------+-------|
|_type_ |_LSMEAN_|_a
|1
|2 |
|========|========|========|=======|=======|
|EFFN |0
| | 8.000|
8.000|
|--------+--------+--------|-------+-------|
|LSMDIFF |29
|1
| 0.000| 3.125|
|
|--------+--------|-------+-------|
| |32.125
|2
| -3.125| 0.000|
|--------+--------+--------|-------+-------|
|MINDIFF |29
|1
| 1.939| 1.939|
|
|--------+--------|-------+-------|
| |32.125
|2
| 1.939| 1.939|
|--------+--------+--------|-------+-------|
|PROB |29
|1
| 1.000| 0.020|
|
|--------+--------|-------+-------|
| |32.125
|2
| 0.020| 1.000|
--------------------------------------------
=================================
Dap 6. Sun Dec 29 19:56:21 2002
Testing Ho: b
Residual: b*c a*b*c
EMS(b*c) =
4 Var(b*c)
2 Var(a*b*c)
1 Var(Error)
EMS(a*b*c) =
2 Var(a*b*c)
1 Var(Error)
EMS(b) =
8 Var(b)
4 Var(b*c)
2 Var(a*b*c)
1 Var(Error)
Error for b =
1 MS(b*c)
Number of observations = 16
H0 SS = 770.062, df = 1, MS = 770.062
Residual df = 2, MS = 12.0625
F0 = 63.8394
Prob[F > F0] = 0.01531
=================================
Dap 7. Sun Dec 29 19:56:21 2002
Least-squares means for: b
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 8. Sun Dec 29 19:56:21 2002
_stat_ for _lsm_ / b
============================================
|
|23.625 |37.5 |
|--------+--------+--------|-------+-------|
|_type_ |_LSMEAN_|_b
|1
|2 |
|========|========|========|=======|=======|
|EFFN |0
| | 8.000|
8.000|
|--------+--------+--------|-------+-------|
|LSMDIFF |23.625 |1
|
0.000| 13.875|
|
|--------+--------|-------+-------|
|
|37.5
|2 |-13.875| 0.000|
|--------+--------+--------|-------+-------|
|MINDIFF |23.625 |1
|
7.472| 7.472|
|
|--------+--------|-------+-------|
|
|37.5
|2 | 7.472|
7.472|
|--------+--------+--------|-------+-------|
|PROB |23.625
|1
| 1.000| 0.015|
|
|--------+--------|-------+-------|
|
|37.5
|2 | 0.015|
1.000|
--------------------------------------------
=================================
Dap 9. Sun Dec 29 19:56:21 2002
Testing Ho: a*b
Residual: a*b*c
EMS(a*b*c) =
2 Var(a*b*c)
1 Var(Error)
EMS(a*b) =
4 Var(a*b)
2 Var(a*b*c)
1 Var(Error)
Error for a*b =
1 MS(a*b*c)
Number of observations = 16
H0 SS = 10.5625, df = 1, MS = 10.5625
Residual df = 2, MS = 0.8125
F0 = 13
Prob[F > F0] = 0.06906
=================================
Dap 10. Sun Dec 29 19:56:21 2002
Testing Ho: c*b
Residual: a*b*c
EMS(a*b*c) =
2 Var(a*b*c)
1 Var(Error)
EMS(b*c) =
4 Var(b*c)
2 Var(a*b*c)
1 Var(Error)
Error for b*c =
1 MS(a*b*c)
Number of observations = 16
H0 SS = 24.125, df = 2, MS = 12.0625
Residual df = 2, MS = 0.8125
F0 = 14.8462
Prob[F > F0] = 0.06311
=================================
Dap 11. Sun Dec 29 19:56:21 2002
Testing Ho: a*c*b
Number of observations = 16
H0 SS = 1.625, df = 2, MS = 0.8125
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 1
Prob[F > F0] = 0.40960
Random model, unbalanced [Back to Sample output]
AMD: pp. 265 - 273
=================================
Dap 1. Sun Dec 29 19:56:39 2002
Response variable: efficiency
Treatment Levels
-------- ------
plant
1 2 3
site
1 2 3 4
worker 1
2 3
=================================
Dap 2. Sun Dec 29 19:56:39 2002
Testing Ho: plant plant*site plant*worker
plant*site*worker
Number of observations = 118
H0 SS = 10046.9, df = 35, MS = 287.054
Error SS = 408.617, df = 82, MS = 4.98313
R-sq = 0.960919
F0 = 57.6052
Prob[F > F0] = 0.00001
=================================
Dap 3. Sun Dec 29 19:56:39 2002
Testing Ho: plant
Residual: plant*worker plant*site plant*site*worker
EMS(plant*site) =
8.0468 Var(plant*site)
2.68227 Var(plant*site*worker)
1 Var(Error)
EMS(plant*worker) =
9.45329 Var(plant*worker)
2.36332 Var(plant*site*worker)
1 Var(Error)
EMS(plant*site*worker) =
2.85695 Var(plant*site*worker)
1 Var(Error)
EMS(plant) =
27.598 Var(plant)
6.8995 Var(plant*site)
9.19933 Var(plant*worker)
2.29983 Var(plant*site*worker)
1 Var(Error)
Error for plant =
-0.0255598 MS(Error)
0.857422 MS(plant*site)
0.973135 MS(plant*worker)
-0.804997 MS(plant*site*worker)
Number of observations = 118
H0 SS = 3866.33, df = 2, MS = 1933.16
Residual df = 4.76305, MS = 288.305
F0 = 6.70528
Prob[F > F0] = 0.04184
=================================
Dap 4. Sun Dec 29 19:56:39 2002
Least-squares means for: plant
Tukey method
Minimum significant differences are for level 0.05000
=================================
Dap 5. Sun Dec 29 19:56:43 2002
_stat_ for _lsm_ / plant
====================================================
|
|84.1979|96.2412|97.5919|
|--------+--------+--------|-------+-------+-------|
|_type_ |_LSMEAN_|_plant
|2
|3 |1
|
|========|========|========|=======|=======|=======|
|EFFN |0
| | 47.000| 34.000|
37.000|
|--------+--------+--------|-------+-------+-------|
|LSMDIFF |84.1979 |2
|
0.000| 12.043| 13.394|
|
|--------+--------|-------+-------+-------|
| |96.2412
|3
|-12.043| 0.000| 1.351|
|
|--------+--------|-------+-------+-------|
| |97.5919
|1
|-13.394| -1.351| 0.000|
|--------+--------+--------|-------+-------+-------|
|MINDIFF |84.1979 |2 |
11.655|
12.720| 12.417|
|
|--------+--------|-------+-------+-------|
| |96.2412
|3
| 12.720| 13.703| 13.422|
|
|--------+--------|-------+-------+-------|
| |97.5919
|1
| 12.417| 13.422| 13.135|
|--------+--------+--------|-------+-------+-------|
|PROB |84.1979
|2
| 1.000| 0.060| 0.038|
|
|--------+--------|-------+-------+-------|
| |96.2412
|3
| 0.060| 1.000| 0.941|
|
|--------+--------|-------+-------+-------|
| |97.5919
|1
| 0.038| 0.941| 1.000|
----------------------------------------------------
=================================
Dap 6. Sun Dec 29 19:56:43 2002
Testing Ho: site*worker*plant
Number of observations = 118
H0 SS = 1921.29, df = 18, MS = 106.738
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 21.4199
Prob[F > F0] = 0.00001
=================================
Dap 7. Sun Dec 29 19:56:43 2002
Testing Ho: site*plant
Residual: site*worker*plant
EMS(plant*site*worker) =
2.85695 Var(plant*site*worker)
1 Var(Error)
EMS(plant*site) =
8.0468 Var(plant*site)
2.68227 Var(plant*site*worker)
1 Var(Error)
Error for plant*site =
0.0611422 MS(Error)
0.938858 MS(plant*site*worker)
Number of observations = 118
H0 SS = 610.302, df = 9, MS = 67.8114
Residual df = 18.1096, MS = 100.517
F0 = 0.674628
Prob[F > F0] = 0.72173
=================================
Dap 8. Sun Dec 29 19:56:43 2002
Testing Ho: worker*plant
Residual: site*worker*plant
EMS(plant*site*worker) =
2.85695 Var(plant*site*worker)
1 Var(Error)
EMS(plant*worker) =
9.45329 Var(plant*worker)
2.36332 Var(plant*site*worker)
1 Var(Error)
Error for plant*worker =
0.17278 MS(Error)
0.82722 MS(plant*site*worker)
Number of observations = 118
H0 SS = 1949.66, df = 6, MS = 324.943
Residual df = 18.3524, MS = 89.157
F0 = 3.64461
Prob[F > F0] = 0.01480
Mixed model, balanced [Back to Sample output]
AMD: pp. 285-289
=================================
Dap 1. Sun Dec 29 19:56:56 2002
Response variable: productivity
Treatment Levels
-------- ------
machine 1 2
3
person 1
2 3 4 5 6
=================================
Dap 2. Sun Dec 29 19:56:56 2002
Testing Ho: machine person machine*person
Number of observations = 54
H0 SS = 3423.69, df = 17, MS = 201.393
Error SS = 33.2867, df = 36, MS = 0.92463
R-sq = 0.990371
F0 = 217.81
Prob[F > F0] = 0.00001
=================================
Dap 3. Sun Dec 29 19:56:56 2002
Testing Ho: machine*person
Number of observations = 54
H0 SS = 426.53, df = 10, MS = 42.653
Error SS = 33.2867, df = 36, MS = 0.92463
F0 = 46.1298
Prob[F > F0] = 0.00001
=================================
Dap 4. Sun Dec 29 19:56:56 2002
Testing Ho: person
Residual: machine*person
EMS(machine*person) =
3 Var(machine*person)
1 Var(Error)
EMS(person) =
9 Var(person)
3 Var(machine*person)
1 Var(Error)
Error for person =
1 MS(machine*person)
Number of observations = 54
H0 SS = 1241.89, df = 5, MS = 248.379
Residual df = 10, MS = 42.653
F0 = 5.82325
Prob[F > F0] = 0.00895
=================================
Dap 5. Sun Dec 29 19:56:56 2002
Least-squares means for: person
Tukey method
Minimum significant differences are for level 0.05000
=================================
Dap 6. Sun Dec 29 19:57:14 2002
_stat_ for _lsm_ / person
==================================================================================
|
|50.5778 |57.9889 |59.5778 |60.9111 |62.7222 |66.1222 |
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|_type_ |_LSMEAN_|_person
|6
|2
|4
|1
|5
|3 |
|========|========|========|========|========|========|========|========|========|
|EFFN |0
| | 9.0000|
9.0000|
9.0000| 9.0000| 9.0000| 9.0000|
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|LSMDIFF |50.5778 |6
|
0.0000| 7.4111| 9.0000| 10.3333| 12.1444| 15.5444|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |57.9889
|2
| -7.4111| 0.0000| 1.5889| 2.9222|
4.7333|
8.1333|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |59.5778
|4
| -9.0000| -1.5889| 0.0000| 1.3333| 3.1444|
6.5444|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |60.9111
|1
|-10.3333| -2.9222| -1.3333| 0.0000| 1.8111|
5.2111|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |62.7222
|5
|-12.1444| -4.7333| -3.1444| -1.8111| 0.0000| 3.4000|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |66.1222
|3
|-15.5444| -8.1333| -6.5444| -5.2111| -3.4000| 0.0000|
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|MINDIFF |50.5778 |6 |
10.6933|
10.6933| 10.6933| 10.6933| 10.6933| 10.6933|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |57.9889
|2
| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |59.5778
|4
| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |60.9111
|1
| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |62.7222
|5
| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |66.1222
|3
| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933| 10.6933|
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|PROB |50.5778
|6
| 1.0000| 0.2400| 0.1146| 0.0597|
0.0244|
0.0049|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |57.9889
|2
| 0.2400| 1.0000| 0.9942| 0.9240|
0.6512|
0.1728|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |59.5778
|4
| 0.1146| 0.9942| 1.0000| 0.9974|
0.9004|
0.3469|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |60.9111
|1
| 0.0597| 0.9240| 0.9974| 1.0000|
0.9896|
0.5644|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |62.7222
|5
| 0.0244| 0.6512| 0.9004| 0.9896|
1.0000|
0.8690|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |66.1222
|3
| 0.0049| 0.1728| 0.3469| 0.5644|
0.8690|
1.0000|
----------------------------------------------------------------------------------
=================================
Dap 7. Sun Dec 29 19:57:14 2002
Testing Ho: machine
Residual: machine*person
EMS(machine*person) =
3 Var(machine*person)
1 Var(Error)
EMS(machine) =
18 Var(machine)
3 Var(machine*person)
1 Var(Error)
Error for machine =
1 MS(machine*person)
Number of observations = 54
H0 SS = 1755.26, df = 2, MS = 877.632
Residual df = 10, MS = 42.653
F0 = 20.5761
Prob[F > F0] = 0.00029
=================================
Dap 8. Sun Dec 29 19:57:14 2002
Least-squares means for: machine
Tukey method
Minimum significant differences are for level 0.05000
=================================
Dap 9. Sun Dec 29 19:57:16 2002
_stat_ for _lsm_ / machine
=======================================================
|
|52.3556 |60.3222 |66.2722 |
|--------+--------+--------|--------+--------+--------|
|_type_
|_LSMEAN_|_machine|1
|2
|3
|
|========|========|========|========|========|========|
|EFFN |0
| | 18.0000| 18.0000|
18.0000|
|--------+--------+--------|--------+--------+--------|
|LSMDIFF |52.3556 |1
|
0.0000| 7.9667| 13.9167|
|
|--------+--------|--------+--------+--------|
| |60.3222
|2
| -7.9667| 0.0000| 5.9500|
|
|--------+--------|--------+--------+--------|
| |66.2722
|3
|-13.9167| -5.9500| 0.0000|
|--------+--------+--------|--------+--------+--------|
|MINDIFF |52.3556 |1
|
5.9677| 5.9677| 5.9677|
|
|--------+--------|--------+--------+--------|
| |60.3222
|2
| 5.9677| 5.9677| 5.9677|
|
|--------+--------|--------+--------+--------|
| |66.2722
|3
| 5.9677| 5.9677| 5.9677|
|--------+--------+--------|--------+--------+--------|
|PROB |52.3556
|1
| 1.0000| 0.0111| 0.0002|
|
|--------+--------|--------+--------+--------|
| |60.3222
|2
| 0.0111| 1.0000| 0.0507|
|
|--------+--------|--------+--------+--------|
| |66.2722
|3
| 0.0002| 0.0507| 1.0000|
-------------------------------------------------------
Mixed model, unbalanced[Back to Sample output]
AMD: pp. 290 - 295
=================================
Dap 1. Sun Dec 29 19:58:15 2002
Response variable: productivity
Treatment Levels
-------- ------
machine 1 2
3
person 1
2 3 4 5 6
=================================
Dap 2. Sun Dec 29 19:58:15 2002
Testing Ho: machine person machine*person
Number of observations = 44
H0 SS = 3061.74, df = 17, MS = 180.103
Error SS = 22.6867, df = 26, MS = 0.872564
R-sq = 0.992645
F0 = 206.406
Prob[F > F0] = 0.00001
=================================
Dap 3. Sun Dec 29 19:58:15 2002
Testing Ho: machine*person
Number of observations = 44
H0 SS = 404.315, df = 10, MS = 40.4315
Error SS = 22.6867, df = 26, MS = 0.872564
F0 = 46.3364
Prob[F > F0] = 0.00001
=================================
Dap 4. Sun Dec 29 19:58:15 2002
Testing Ho: machine
Residual: machine*person
EMS(machine*person) =
2.31622 Var(machine*person)
1 Var(Error)
EMS(machine) =
12.8219 Var(machine)
2.13699 Var(machine*person)
1 Var(Error)
Error for machine =
0.0773812 MS(Error)
0.922619 MS(machine*person)
Number of observations = 44
H0 SS = 1238.2, df = 2, MS = 619.099
Residual df = 10.0362, MS = 37.3704
F0 = 16.5666
Prob[F > F0] = 0.00067
=================================
Dap 5. Sun Dec 29 19:58:15 2002
Least-squares means for: machine
Tukey method
Minimum significant differences are for level 0.05000
=================================
Dap 6. Sun Dec 29 19:58:18 2002
_stat_ for _lsm_ / machine
=======================================================
|
|52.3136 |60.022 |66.2722 |
|--------+--------+--------|--------+--------+--------|
|_type_
|_LSMEAN_|_machine|1
|2
|3
|
|========|========|========|========|========|========|
|EFFN |0
| | 11.4088| 12.8061|
18.0000|
|--------+--------+--------|--------+--------+--------|
|LSMDIFF |52.3136 |1
|
0.0000| 7.7084| 13.9586|
|
|--------+--------|--------+--------+--------|
| |60.022
|2
| -7.7084| 0.0000| 6.2502|
|
|--------+--------|--------+--------+--------|
| |66.2722
|3
|-13.9586| -6.2502| 0.0000|
|--------+--------+--------|--------+--------+--------|
|MINDIFF |52.3136 |1
|
7.0126| 6.8186| 6.3382|
|
|--------+--------|--------+--------+--------|
| |60.022
|2
| 6.8186| 6.6189| 6.1229|
|
|--------+--------|--------+--------+--------|
| |66.2722
|3
| 6.3382| 6.1229| 5.5829|
|--------+--------+--------|--------+--------+--------|
|PROB |52.3136
|1
| 1.0000| 0.0278| 0.0003|
|
|--------+--------|--------+--------+--------|
| |60.022
|2
| 0.0278| 1.0000| 0.0455|
|
|--------+--------|--------+--------+--------|
| |66.2722
|3
| 0.0003| 0.0455| 1.0000|
-------------------------------------------------------
=================================
Dap 7. Sun Dec 29 19:58:18 2002
Testing Ho: person
Residual: machine*person
EMS(machine*person) =
2.31622 Var(machine*person)
1 Var(Error)
EMS(person) =
6.72245 Var(person)
2.24082 Var(machine*person)
1 Var(Error)
Error for person =
0.0325538 MS(Error)
0.967446 MS(machine*person)
Number of observations = 44
H0 SS = 1011.05, df = 5, MS = 202.211
Residual df = 10.0145, MS = 39.1437
F0 = 5.16586
Prob[F > F0] = 0.01334
=================================
Dap 8. Sun Dec 29 19:58:18 2002
Least-squares means for: person
Tukey method
Minimum significant differences are for level 0.05000
=================================
Dap 9. Sun Dec 29 19:58:35 2002
_stat_ for _lsm_ / person
==================================================================================
|
|50.5778 |57.6873 |59.7988 |60.7999 |62.4176 |65.6775 |
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|_type_ |_LSMEAN_|_person
|6
|2
|4
|1
|5
|3 |
|========|========|========|========|========|========|========|========|========|
|EFFN |0
| | 9.0000|
7.8572|
7.6197| 4.8331| 8.0095| 5.4729|
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|LSMDIFF |50.5778 |6
|
0.0000| 7.1095| 9.2210| 10.2221| 11.8398| 15.0997|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |57.6873
|2
| -7.1095| 0.0000| 2.1115| 3.1126|
4.7303|
7.9902|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |59.7988
|4
| -9.2210| -2.1115| 0.0000| 1.0011| 2.6188|
5.8787|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |60.7999
|1
|-10.2221| -3.1126| -1.0011| 0.0000| 1.6177|
4.8776|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |62.4176
|5
|-11.8398| -4.7303| -2.6188| -1.6177| 0.0000| 3.2599|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |65.6775
|3
|-15.0997| -7.9902| -5.8787| -4.8776| -3.2599| 0.0000|
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|MINDIFF |50.5778 |6 |
10.2412|
10.6071| 10.6950| 12.2514| 10.5531| 11.7762|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |57.6873
|2
| 10.6071| 10.9608| 11.0458| 12.5588| 10.9085| 12.0957|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |59.7988
|4
| 10.6950| 11.0458| 11.1302| 12.6331| 10.9940| 12.1729|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |60.7999
|1
| 12.2514| 12.5588| 12.6331| 13.9753| 12.5132| 13.5607|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |62.4176
|5
| 10.5531| 10.9085| 10.9940| 12.5132| 10.8560| 12.0484|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |65.6775
|3
| 11.7762| 12.0957| 12.1729| 13.5607| 12.0484| 13.1330|
|--------+--------+--------|--------+--------+--------+--------+--------+--------|
|PROB |50.5778
|6
| 1.0000| 0.2671| 0.1031| 0.1190|
0.0263|
0.0114|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |57.6873
|2
| 0.2671| 1.0000| 0.9823| 0.9479|
0.6689|
0.2794|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |59.7988
|4
| 0.1031| 0.9823| 1.0000| 0.9997|
0.9555|
0.5731|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |60.7999
|1
| 0.1190| 0.9479| 0.9997| 1.0000|
0.9970|
0.8045|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |62.4176
|5
| 0.0263| 0.6689| 0.9555| 0.9970|
1.0000|
0.9268|
|
|--------+--------|--------+--------+--------+--------+--------+--------|
| |65.6775
|3
| 0.0114| 0.2794| 0.5731| 0.8045|
0.9268|
1.0000|
----------------------------------------------------------------------------------
Split plot [Back to Sample output]
AMD: pp. 297 - 308
=================================
Dap 1. Sun Dec 29 19:58:49 2002
Whole plot (block, fertilizer) analysis
Response variable: yield
Treatment Levels
-------- ------
fertilizer 1 2 3 4
block
1 2
variety 1
2
=================================
Dap 2. Sun Dec 29 19:58:49 2002
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer block
Residual: fertilizer*block variety fertilizer*variety
block*variety
fertilizer*block*variety
Number of observations = 16
H0 SS = 171.293, df = 4, MS = 42.8231
Residual SS = 19.1575, df = 11, MS = 1.74159
R-sq = 0.899409
F0 = 24.5885
Prob[F > F0] = 0.00002
=================================
Dap 3. Sun Dec 29 19:58:49 2002
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer
Residual: fertilizer*block
EMS(fertilizer*block) =
2 Var(fertilizer*block)
1 Var(Error)
EMS(fertilizer) =
4 Var(fertilizer)
2 Var(fertilizer*block)
1 Var(Error)
Error for fertilizer =
1 MS(fertilizer*block)
Number of observations = 16
H0 SS = 40.19, df = 3, MS = 13.3967
Residual df = 3, MS = 2.30917
F0 = 5.80152
Prob[F > F0] = 0.09137
=================================
Dap 4. Sun Dec 29 19:58:49 2002
Whole plot (block, fertilizer) analysis
Least-squares means for: fertilizer
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 5. Sun Dec 29 19:58:49 2002
Whole plot (block, fertilizer) analysis
_stat_ for _lsm_ / fertilizer
================================================================
|
|38.8 |39.4
|39.8
|42.9 |
|--------+--------+--------|--------+--------+--------+--------|
|_type_
|_LSMEAN_|_fertili|1
|3
|2
|4 |
|========|========|========|========|========|========|========|
|EFFN |0
| | 4.0000|
4.0000|
4.0000| 4.0000|
|--------+--------+--------|--------+--------+--------+--------|
|LSMDIFF |38.8
|1
| 0.0000| 0.6000| 1.0000| 4.1000|
|
|--------+--------|--------+--------+--------+--------|
|
|39.4
|3 | -0.6000| 0.0000|
0.4000|
3.5000|
|
|--------+--------|--------+--------+--------+--------|
|
|39.8
|2 | -1.0000| -0.4000|
0.0000|
3.1000|
|
|--------+--------|--------+--------+--------+--------|
|
|42.9
|4 | -4.1000| -3.5000|
-3.1000|
0.0000|
|--------+--------+--------|--------+--------+--------+--------|
|MINDIFF |38.8
|1
| 3.4196| 3.4196| 3.4196| 3.4196|
|
|--------+--------|--------+--------+--------+--------|
|
|39.4
|3 | 3.4196|
3.4196|
3.4196| 3.4196|
|
|--------+--------|--------+--------+--------+--------|
|
|39.8
|2 | 3.4196|
3.4196|
3.4196| 3.4196|
|
|--------+--------|--------+--------+--------+--------|
|
|42.9
|4 | 3.4196|
3.4196|
3.4196| 3.4196|
|--------+--------+--------|--------+--------+--------+--------|
|PROB |38.8
|1
| 1.0000| 0.6155| 0.4207| 0.0317|
|
|--------+--------|--------+--------+--------+--------|
|
|39.4
|3 | 0.6155|
1.0000|
0.7344| 0.0472|
|
|--------+--------|--------+--------+--------+--------|
|
|39.8
|2 | 0.4207|
0.7344|
1.0000| 0.0633|
|
|--------+--------|--------+--------+--------+--------|
|
|42.9
|4 | 0.0317|
0.0472|
0.0633| 1.0000|
----------------------------------------------------------------
=================================
Dap 6. Sun Dec 29 19:58:49 2002
Subplot (variety) analysis
Response variable: yield
Treatment Levels
-------- ------
fertilizer 1 2 3 4
block
1 2
variety 1
2
=================================
Dap 7. Sun Dec 29 19:58:49 2002
Subplot (variety) analysis
Testing Ho: fertilizer block fertilizer*block variety
fertilizer*variety
Residual: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 182.02, df = 11, MS = 16.5473
Residual SS = 8.43, df = 4, MS = 2.1075
R-sq = 0.955736
F0 = 7.85161
Prob[F > F0] = 0.03064
=================================
Dap 8. Sun Dec 29 19:58:49 2002
Subplot (variety) analysis
Testing Ho: variety
Residual: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 2.25, df = 1, MS = 2.25
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 1.06762
Prob[F > F0] = 0.35987
=================================
Dap 9. Sun Dec 29 19:58:49 2002
Subplot (variety) analysis
Least-squares means for: variety
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 10. Sun Dec 29 19:58:49 2002
Subplot (variety) analysis
_stat_ for _lsm_ / variety
==============================================
|
|39.85 |40.6 |
|--------+--------+--------|--------+--------|
|_type_
|_LSMEAN_|_variety|1
|2 |
|========|========|========|========|========|
|EFFN |0
| | 8.0000|
8.0000|
|--------+--------+--------|--------+--------|
|LSMDIFF |39.85
|1
| 0.0000| 0.7500|
|
|--------+--------|--------+--------|
|
|40.6
|2 | -0.7500| 0.0000|
|--------+--------+--------|--------+--------|
|MINDIFF |39.85
|1
| 2.0153| 2.0153|
|
|--------+--------|--------+--------|
|
|40.6
|2 | 2.0153|
2.0153|
|--------+--------+--------|--------+--------|
|PROB |39.85
|1
| 1.0000| 0.3599|
|
|--------+--------|--------+--------|
|
|40.6
|2 | 0.3599|
1.0000|
----------------------------------------------
=================================
Dap 11. Sun Dec 29 19:58:49 2002
Subplot (variety) analysis
Testing Ho: variety*fertilizer
Residual: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 1.55, df = 3, MS = 0.516667
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 0.245156
Prob[F > F0] = 0.86125
Latin square [Back to Sample output]
ED: pp. 122 - 125
=================================
Dap 1. Sun Dec 29 19:59:10 2002
Response variable: error
Treatment Levels
-------- ------
sampler A B C
D
E F
area
1 2 3 4 5 6
order
6 5 1 2 4 3
=================================
Dap 2. Sun Dec 29 19:59:11 2002
Testing Ho: sampler area order
Residual: sampler*area*order
Number of observations = 36
H0 SS = 263.064, df = 15, MS = 17.5376
Residual SS = 66.5633, df = 20, MS = 3.32817
R-sq = 0.798065
F0 = 5.26945
Prob[F > F0] = 0.00039
=================================
Dap 3. Sun Dec 29 19:59:12 2002
Testing Ho: sampler
Residual: sampler*area*order
Number of observations = 36
H0 SS = 155.596, df = 5, MS = 31.1192
Residual SS = 66.5633, df = 20, MS = 3.32817
F0 = 9.35024
Prob[F > F0] = 0.00011
=================================
Dap 4. Sun Dec 29 19:59:12 2002
Least-squares means for: sampler
Tukey method
Minimum significant differences are for level 0.05000
=================================
Dap 5. Sun Dec 29 19:59:17 2002
_stat_ for _lsm_ / sampler
============================================================================
|
|1.2 |2.66667|5.58333|6.06667|6.11667|6.91667|
|--------+--------+--------|-------+-------+-------+-------+-------+-------|
|_type_
|_LSMEAN_|_sampler|F
|E |B
|A
|C |D
|
|========|========|========|=======|=======|=======|=======|=======|=======|
|EFFN |0
| | 6.000|
6.000|
6.000| 6.000| 6.000| 6.000|
|--------+--------+--------|-------+-------+-------+-------+-------+-------|
|LSMDIFF |1.2
|F
| 0.000| 1.467| 4.383| 4.867|
4.917|
5.717|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |2.66667
|E
| -1.467| 0.000| 2.917| 3.400| 3.450|
4.250|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |5.58333
|B
| -4.383| -2.917| 0.000| 0.483| 0.533|
1.333|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.06667
|A
| -4.867| -3.400| -0.483| 0.000| 0.050| 0.850|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.11667
|C
| -4.917| -3.450| -0.533| -0.050| 0.000| 0.800|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.91667
|D
| -5.717| -4.250| -1.333| -0.850| -0.800| 0.000|
|--------+--------+--------|-------+-------+-------+-------+-------+-------|
|MINDIFF |1.2
|F
| 3.311| 3.311| 3.311| 3.311|
3.311|
3.311|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |2.66667
|E
| 3.311| 3.311| 3.311| 3.311|
3.311|
3.311|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |5.58333
|B
| 3.311| 3.311| 3.311| 3.311|
3.311|
3.311|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.06667
|A
| 3.311| 3.311| 3.311| 3.311|
3.311|
3.311|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.11667
|C
| 3.311| 3.311| 3.311| 3.311|
3.311|
3.311|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.91667
|D
| 3.311| 3.311| 3.311| 3.311|
3.311|
3.311|
|--------+--------+--------|-------+-------+-------+-------+-------+-------|
|PROB |1.2
|F
| 1.000| 0.731| 0.006| 0.002|
0.002|
0.000|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |2.66667
|E
| 0.731| 1.000| 0.105| 0.042|
0.038|
0.007|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |5.58333
|B
| 0.006| 0.105| 1.000| 0.997|
0.995|
0.799|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.06667
|A
| 0.002| 0.042| 0.997| 1.000|
1.000|
0.963|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.11667
|C
| 0.002| 0.038| 0.995| 1.000|
1.000|
0.971|
|
|--------+--------|-------+-------+-------+-------+-------+-------|
| |6.91667
|D
| 0.000| 0.007| 0.799| 0.963|
0.971|
1.000|
----------------------------------------------------------------------------
=================================
Dap 6. Sun Dec 29 19:59:17 2002
Testing Ho: area
Residual: sampler*area*order
Number of observations = 36
H0 SS = 78.8692, df = 5, MS = 15.7738
Residual SS = 66.5633, df = 20, MS = 3.32817
F0 = 4.7395
Prob[F > F0] = 0.00512
=================================
Dap 7. Sun Dec 29 19:59:17 2002
Testing Ho: order
Residual: sampler*area*order
Number of observations = 36
H0 SS = 28.5992, df = 5, MS = 5.71983
Residual SS = 66.5633, df = 20, MS = 3.32817
F0 = 1.71861
Prob[F > F0] = 0.17635
Missing treatment combinations [Back to Sample output]
AMD: pp. 173 - 177
=================================
Dap 1. Sat Jul 28 19:54:08 2001
Testing interaction Ho:
u11 - u21 - (u13 - u23) = 0 and
u21 - u31 - (u22 - u32) = 0
=================================
Dap 2. Sat Jul 28 19:54:08 2001
Testing interaction Ho:
Testing Ho: treat*block
Number of observations = 10
H0 SS = 18.7778, df = 2, MS = 9.38889
Error SS = 14.5, df = 3, MS = 4.83333
F0 = 1.94253
Prob[F > F0] = 0.28763
=================================
Dap 3. Sat Jul 28 19:54:08 2001
Testing treatment Ho:
u11 + u13 - (u21 + u23) = 0
u21 + u22 - (u31 + u32) = 0
=================================
Dap 4. Sat Jul 28 19:54:08 2001
Testing treatment Ho:
Testing Ho: treat
Number of observations = 10
H0 SS = 2.77778, df = 2, MS = 1.38889
Error SS = 14.5, df = 3, MS = 4.83333
F0 = 0.287356
Prob[F > F0] = 0.76882
Linear regression [Back to Sample output]
MS: pp. 95 - 97
=================================
Dap 1. Sat Jul 28 19:55:34 2001
Reduced | full model regressors: _intercept_ | soilphos
Number of observations = 9
Response: plantphos
F0(1, 7) = 12.8868, Prob[F > F0] = 0.00886
R-sq = 0.648008, Adj R-sq = 0.597723
Parameter
Estimate Std Error
T0[
7] Prob[|T|>|T0|]
_intercept_
61.5804
6.24765
9.85656 0.00003
soilphos
1.41689 0.394698
3.58982 0.00886
Linear regression, model building [Back to Sample output]
LM: pp. 50 - 60
=================================
Dap 1. Sat Jul 28 20:08:03 2001
Correlations
For: _type_ = CORR
_corr_
for
_var2_
=============================================
|_var1_ |x[0] |x[1] |x[2] |x[3]
|y
|
|========|======|======|======|======|======|
|x[0] | 1.000| 0.971|-0.668| 0.652|
0.740|
|--------|------+------+------+------+------|
|x[1] | 0.971| 1.000|-0.598| 0.527|
0.628|
|--------|------+------+------+------+------|
|x[2] |-0.668|-0.598|
1.000|-0.841|-0.780|
|--------|------+------+------+------+------|
|x[3] | 0.652| 0.527|-0.841| 1.000|
0.978|
|--------|------+------+------+------+------|
|y | 0.740| 0.628|-0.780|
0.978|
1.000|
---------------------------------------------
=================================
Dap 2. Sat Jul 28 20:08:03 2001
Correlations
For: _type_ = N
_corr_
for
_var2_
=============================================
|_var1_ |x[0] |x[1] |x[2] |x[3]
|y
|
|========|======|======|======|======|======|
|x[0]
|10.000|10.000|10.000|10.000|10.000|
|--------|------+------+------+------+------|
|x[1]
|10.000|10.000|10.000|10.000|10.000|
|--------|------+------+------+------+------|
|x[2]
|10.000|10.000|10.000|10.000|10.000|
|--------|------+------+------+------+------|
|x[3]
|10.000|10.000|10.000|10.000|10.000|
|--------|------+------+------+------+------|
|y
|10.000|10.000|10.000|10.000|10.000|
---------------------------------------------
=================================
Dap 3. Sat Jul 28 20:08:03 2001
Correlations
For: _type_ = PCORR
_corr_
for
_var2_
=============================================
|_var1_ |x[0] |x[1] |x[2] |x[3]
|y
|
|========|======|======|======|======|======|
|x[0] | 0.000| 0.000| 0.035| 0.041|
0.014|
|--------|------+------+------+------+------|
|x[1] | 0.000| 0.000| 0.068| 0.118|
0.052|
|--------|------+------+------+------+------|
|x[2] | 0.035| 0.068| 0.000| 0.002|
0.008|
|--------|------+------+------+------+------|
|x[3] | 0.041| 0.118| 0.002| 0.000|
0.000|
|--------|------+------+------+------+------|
|y | 0.014| 0.052| 0.008|
0.000|
0.000|
---------------------------------------------
=================================
Dap 4. Sat Jul 28 20:08:03 2001
Model building
Reduced | full model regressors: _intercept_ | x[3]
Number of observations = 10
Response: y
F0(1, 8) = 172.619, Prob[F > F0] = 0.00001
R-sq = 0.955708, Adj R-sq = 0.950171
Parameter
Estimate Std Error
T0[
8] Prob[|T|>|T0|]
_intercept_
21.8042
2.83157
7.70041 0.00006
x[3]
1.02579 0.0780755
13.1384
0.00001
=================================
Dap 5. Sat Jul 28 20:08:03 2001
Model building
Reduced | full model regressors: _intercept_ x[3] | x[0]
Number of observations = 10
Response: y
F0(2, 7) = 131.793, Prob[F > F0] = 0.00001
R-sq = 0.97413, Adj R-sq = 0.966739
F-change(1, 7) = 4.98488, Prob[F > F-change] =
0.06073
Parameter
Estimate Std Error
T0[
7] Prob[|T|>|T0|]
_intercept_
12.9449
4.59315
2.81831 0.02584
x[3]
0.903324
0.084129
10.7374 0.00002
x[0]
1.88521 0.844369
2.23268 0.06073
=================================
Dap 6. Sat Jul 28 20:08:03 2001
Model building
Reduced | full model regressors: _intercept_ x[3] x[0] |
x[2]
Number of observations = 10
Response: y
F0(3, 6) = 183.912, Prob[F > F0] = 0.00001
R-sq = 0.989242, Adj R-sq = 0.983863
F-change(1, 6) = 8.42848, Prob[F > F-change] =
0.02723
Parameter
Estimate Std Error
T0[
6] Prob[|T|>|T0|]
_intercept_
2.55427 4.80051
0.532084
0.61379
x[3]
1.07907 0.0842512
12.8078
0.00002
x[0]
2.40786 0.615063
3.91482 0.00785
x[2]
0.936516
0.322582
2.90318 0.02723
=================================
Dap 7. Sat Jul 28 20:08:03 2001
Model building
Reduced | full model regressors: _intercept_ x[0] x[3] x[2] |
x[1]
Number of observations = 10
Response: y
F0(4, 5) = 126.46, Prob[F > F0] = 0.00004
R-sq = 0.990212, Adj R-sq = 0.982382
F-change(1, 5) = 0.495515, Prob[F > F-change] =
0.51290
Parameter
Estimate Std Error
T0[
5] Prob[|T|>|T0|]
_intercept_
2.80985 5.02914
0.558715
0.60046
x[0]
0.523737 2.75266
0.190266 0.85659
x[3]
1.12232 0.107355
10.4543 0.00014
x[2]
0.994528
0.346992
2.86614 0.03516
x[1]
0.775412 1.10155
0.703928 0.51290
Ordinal cross-classification[Back to Sample output]
CDA: pp. 49 - 50
=================================
Dap 1. Sat Jul 28 20:04:08 2001
Variable: Levels
----------------
income: 00-06 06-15 15-25 25-
jobsat: 1verydis 2littledis 3modsat 4verysat
Chisq0[9] = 11.9886, Prob[Chisq > Chisq0] = 0.21396
Statistic
Value ASE
Gamma
0.127 0.041
Kendall's Tau-b 0.088 0.028
Somers' D C|R 0.082
0.026
Somers' D R|C 0.094
0.030
=================================
Dap 2. Sat Jul 28 20:04:08 2001
For: _type_ = COUNT
_cell_
for
jobsat
======================================================
|income |1verydis |2littledis|3modsat
|4verysat
|
|========|==========|==========|==========|==========|
|00-06 |
20.0|
24.0|
80.0|
82.0|
|--------|----------+----------+----------+----------|
|06-15 |
22.0|
38.0| 104.0|
125.0|
|--------|----------+----------+----------+----------|
|15-25 |
13.0|
28.0| 81.0|
113.0|
|--------|----------+----------+----------+----------|
|25-
|
7.0| 18.0|
54.0| 92.0|
------------------------------------------------------
=================================
Dap 3. Sat Jul 28 20:04:08 2001
For: _type_ = EXPECTED
_cell_
for
jobsat
======================================================
|income |1verydis |2littledis|3modsat
|4verysat
|
|========|==========|==========|==========|==========|
|00-06 |
14.2|
24.7|
72.9|
94.2|
|--------|----------+----------+----------+----------|
|06-15 |
19.9|
34.6| 102.3|
132.2|
|--------|----------+----------+----------+----------|
|15-25 |
16.2|
28.2| 83.2|
107.5|
|--------|----------+----------+----------+----------|
|25- |
11.8|
20.5|
60.5|
78.2|
------------------------------------------------------
Stratified 2x2 tables [Back to Sample output]
CDA: pp. 232 - 233
=================================
Dap 1. Sat Jul 28 20:02:49 2001
count for response
===============================
|penicill|delay |cured|died |
|========|========|=====|=====|
|0.125 |1.5h |
0| 5|
|
|--------|-----+-----|
|
|none
| 0| 6|
|--------+--------|-----+-----|
|0.250 |1.5h |
0| 6|
|
|--------|-----+-----|
|
|none
| 3| 3|
|--------+--------|-----+-----|
|0.500 |1.5h |
2| 4|
|
|--------|-----+-----|
|
|none
| 6| 0|
|--------+--------|-----+-----|
|1.000 |1.5h |
6| 0|
|
|--------|-----+-----|
|
|none
| 5| 1|
|--------+--------|-----+-----|
|4.000 |1.5h |
5| 0|
|
|--------|-----+-----|
|
|none
| 2| 0|
-------------------------------
=================================
Dap 2. Sat Jul 28 20:02:49 2001
Cochran-Mantel-Haenszel test for delay x response, stratified
by
penicillin
M0-squared = 3.92857, Prob[M-squared > M0-squared] =
0.0475
Loglinear models [Back to Sample output]
CDA: pp. 135 - 138, 171 - 174, 176 - 177
=================================
Dap 1. Tue Nov 19 18:31:41 2002
(DV, P) vs (D, V, P)
Maximum likelihood estimation
Cell count: n
Class and aux variables: def vic pen
Statistic
df Prob
G2[Model] = 8.13
3
0.0434
G2[Reduced] = 137.93 4
0.0001
G2[Diff] = 129.80
1
0.0001
X2[Model] = 6.98
3
0.0727
Estimate
ASE Model Parameter
2.83789
0.117031
* mu
-0.39084
0.0946424
* D
0.582115
0.0946424
* V
-1.04323
0.0883545
* P
0.827962
0.0946424
? DV
0
DP
0
VP
=================================
Dap 2. Tue Nov 19 18:31:41 2002
(DV, P) vs (D, V, P)
n for _type_ / pen
===========================================================
|
|FIT
|OBS
|
|--------------+--------------|------+------+------+------|
|def
|vic
|0
|1 |0
|1
|
|==============|==============|======|======|======|======|
|0
|0
| 16.67|134.33| 19.00|132.00|
|
|--------------|------+------+------+------|
|
|1
| 0.99| 8.01| 0.00| 9.00|
|--------------+--------------|------+------+------+------|
|1
|0
| 6.96| 56.04| 11.00| 52.00|
|
|--------------|------+------+------+------|
|
|1
| 11.37| 91.63| 6.00| 97.00|
-----------------------------------------------------------
=================================
Dap 3. Tue Nov 19 18:31:41 2002
(DV, VP) vs (DV, P)
Maximum likelihood estimation
Cell count: n
Class and aux variables: def vic pen
Statistic
df Prob
G2[Model] = 1.88
2
0.3903
G2[Reduced] = 8.13
3
0.0434
G2[Diff] =
6.25
1 0.0125
X2[Model] = 1.43
2
0.4889
Estimate
ASE Model Parameter
2.72369
0.13779 * mu
-0.39084
0.0946424
* D
0.798611
0.13779
* V
-1.17135
0.115885
* P
0.827863
0.0946424
* DV
0
DP
0.264535
0.115885
? VP
=================================
Dap 4. Tue Nov 19 18:31:41 2002
(DV, VP) vs (DV, P)
n for _type_ / pen
===========================================================
|
|FIT
|OBS
|
|--------------+--------------|------+------+------+------|
|def
|vic
|0
|1 |0
|1
|
|==============|==============|======|======|======|======|
|0
|0
| 21.17|129.83| 19.00|132.00|
|
|--------------|------+------+------+------|
|
|1
| 0.48| 8.52| 0.00| 9.00|
|--------------+--------------|------+------+------+------|
|1
|0
| 8.83| 54.17| 11.00| 52.00|
|
|--------------|------+------+------+------|
|
|1
| 5.52| 97.48| 6.00| 97.00|
-----------------------------------------------------------
=================================
Dap 5. Tue Nov 19 18:31:41 2002
(DV, DP, VP) vs (DV, VP)
Maximum likelihood estimation
Cell count: n
Class and aux variables: def vic pen
Statistic
df Prob
G2[Model] = 0.70
1
0.4026
G2[Reduced] = 1.88
2
0.3903
G2[Diff] =
1.18
1 0.2772
X2[Model] = 0.38
1
0.5400
Estimate
ASE Model Parameter
2.69211
0.142941
* mu
-0.479403
0.124301
* D
0.854002
0.146841
* V
-1.20006
0.119974
* P
0.839499
0.0954928
* DV
-0.110106
0.100222
? DP
0.331104
0.129836
* VP
=================================
Dap 6. Tue Nov 19 18:31:41 2002
(DV, DP, VP) vs (DV, VP)
n for _type_ / pen
===========================================================
|
|FIT
|OBS
|
|--------------+--------------|------+------+------+------|
|def
|vic
|0
|1 |0
|1
|
|==============|==============|======|======|======|======|
|0
|0
| 18.67|132.33| 19.00|132.00|
|
|--------------|------+------+------+------|
|
|1
| 0.33| 8.67| 0.00| 9.00|
|--------------+--------------|------+------+------+------|
|1
|0
| 11.33| 51.67| 11.00| 52.00|
|
|--------------|------+------+------+------|
|
|1
| 5.67| 97.33| 6.00| 97.00|
-----------------------------------------------------------
Logistic regression [Back to Sample output]
CDA: pp. 87 - 89
=================================
Dap 1. Sat Jul 28 20:05:03 2001
Reduced | full model regressors: _intercept_ | labind
Number of observations = 14
Number of trials = 27
Events / Trials: nremiss / ncases
-2 (Lred - Lfull) = 8.2988 = ChiSq0[1]
Prob[ChiSq > ChiSq0] = 0.00397
Parameter
Estimate Std Error Wald ChiSq
Prob[ChiSq>Wald
ChiSq]
_intercept_
-3.77714
1.37863
7.5064 0.00615
labind
0.144863 0.0593411
5.95942
0.01464
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Updated: $Date: 2003/02/07 12:33:44 $ $Author: rps $