Anyone familiar with the basic syntax of C programs can learn to use the C-style features of Dap quickly and easily from the manual and the examples contained in it; advanced features of C are not necessary, although they are available. (The manual contains a brief introduction to the C syntax needed for Dap.) Because Dap processes files one line at a time, rather than reading entire files into memory, it can be, and has been, used on data sets that have very many lines and/or very many variables.
I wrote Dap to use in my statistical consulting practice because the aforementioned utterly famous, industry standard statistics system is (or at least was) not available on GNU/Linux and costs a bundle every year under a lease arrangement. And now you can run programs written for that system directly on Dap! 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.
There are two mailing lists regarding Dap, one for bugs and one for comments, which can be accessed through http://mail.gnu.org/archive/html/bug-dap and http://mail.gnu.org/archive/html/dap-users.
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 into a directory that we'll call `DAP_HOME' and untar it with:
������� gunzip -c dap-x.y.tar.gz | tar -xvf -
This should create directories `src', `doc', and `examples'.
COMPILATION
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
Otherwise, if you want to install dap in subdirectories of another
directory, say "home/dap", after the `./configure' command, instead type type:
������� make prefix=home/dap install
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�����������
path name for Emacs
DAPEDOPTS����������
options for the Emacs front-end
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)
DAPRUNS������������
path name for the Dap process that runs the
�������������������������������
preprocessor and complier
�������������
(default: /usr/local/bin/dapruns)
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 four are further documented in the manual.
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 <[email protected]>.
If you use dap, please let me know about your experience using it,
and
suggestions, by mailing
to <[email protected]>. 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 �Logit� model for linear-by-linear association �Logistic regression
Unbalanced ANOVA� [Back
to Sample output]
AMD: pp. 128 - 134
=================================
Dap�� 1. Mon Jan 12 03:20:03 2004
�
Response variable: y
Treatment������ Levels
--------������� ------
treat����������
treat1 treat2
block����������
block1 block2 block3
�
=================================
Dap�� 2. Mon Jan 12 03:20:03 2004
�
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. Mon Jan 12 03:20:03 2004
�
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�� 4. Mon Jan 12 03:20:03 2004
�
Least-squares means for: treat
LSD� method
Minimum significant differences are for level 0.05000
=================================
Dap�� 5. Mon Jan 12 03:20:04 2004
�
���������������������������
_stat_ for _lsm_ / treat
======================================================
|�������������������������
|23��������� |27���������
|
|--------+--------+--------|------------+------------|
|_type_� |_LSMEAN_|_treat� |treat1�����
|treat2����� |
|========|========|========|============|============|
|EFFN��� |0������
|������� |���� 7.71429|����
7.71429|
|--------+--------+--------|------------+------------|
|LSMDIFF |23����� |treat1� |�����������
|���������� 4|
|������� |--------+--------|------------+------------|
|������� |27�����
|treat2� |��������� -4|�����������
|
|--------+--------+--------|------------+------------|
|MINDIFF |23����� |treat1� |�����������
|���� 1.60445|
|������� |--------+--------|------------+------------|
|������� |27�����
|treat2� |���� 1.60445|�����������
|
|--------+--------+--------|------------+------------|
|PROB��� |23����� |treat1�
|����������� | 0.000242431|
|������� |--------+--------|------------+------------|
|������� |27�����
|treat2� | 0.000242431|�����������
|
------------------------------------------------------
=================================
Dap�� 6. Mon Jan 12 03:20:04 2004
�
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�� 7. Mon Jan 12 03:20:04 2004
�
Least-squares means for: block
LSD� method
Minimum significant differences are for level 0.05000
=================================
Dap�� 8. Mon Jan 12 03:20:04 2004
�
���������������������������
_stat_ for _lsm_ / block
===================================================================
|�������������������������
|23��������� |24���������
|28��������� |
|--------+--------+--------|------------+------------+------------|
|_type_� |_LSMEAN_|_block� |block1�����
|block2����� |block3�����
|
|========|========|========|============|============|============|
|EFFN��� |0������
|������� |��������
4.8|�������� 4.8|����������
6|
|--------+--------+--------|------------+------------+------------|
|LSMDIFF |23����� |block1� |�����������
|���������� 1|����������
5|
|������� |--------+--------|------------+------------+------------|
|������� |24�����
|block2� |��������� -1|�����������
|���������� 4|
|������� |--------+--------|------------+------------+------------|
|������� |28�����
|block3� |��������� -5|���������
-4|����������� |
|--------+--------+--------|------------+------------+------------|
|MINDIFF |23����� |block1� |�����������
|���� 2.03401|���� 1.92963|
|������� |--------+--------|------------+------------+------------|
|������� |24�����
|block2� |���� 2.03401|�����������
|���� 1.92963|
|������� |--------+--------|------------+------------+------------|
|������� |28�����
|block3� |���� 1.92963|����
1.92963|�����������
|
|--------+--------+--------|------------+------------+------------|
|PROB��� |23����� |block1�
|����������� |���
0.299003| 0.000179317|
|������� |--------+--------|------------+------------+------------|
|������� |24�����
|block2� |��� 0.299003|�����������
| 0.000952328|
|������� |--------+--------|------------+------------+------------|
|������� |28�����
|block3� | 0.000179317| 0.000952328|�����������
|
-------------------------------------------------------------------
=================================
Dap�� 9. Mon Jan 12 03:20:04 2004
�
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
Crossed, nested ANOVA [Back to Sample output]
AMD: pp. 249 - 251
=================================
Dap�� 1. Mon Jan 12 03:20:11 2004
�
Response variable: y
Treatment������ Levels
--------������� ------
a��������������
1 2
b��������������
1 2
c��������������
1 2
�
=================================
Dap�� 2. Mon Jan 12 03:20:11 2004
�
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. Mon Jan 12 03:20:11 2004
�
Testing Ho: a
Number of observations = 16
H0 SS = 39.0625, df = 1, MS = 39.0625
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 48.0769
Prob[F > F0] = 0.00013
=================================
Dap�� 4. Mon Jan 12 03:20:11 2004
�
Testing Ho: b
Number of observations = 16
H0 SS = 770.062, df = 1, MS = 770.062
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 947.769
Prob[F > F0] = 0.00001
=================================
Dap�� 5. Mon Jan 12 03:20:11 2004
�
Testing Ho: a*b
Number of observations = 16
H0 SS = 10.5625, df = 1, MS = 10.5625
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 13
Prob[F > F0] = 0.00693
=================================
Dap�� 6. Mon Jan 12 03:20:11 2004
�
Testing Ho: c*b
Number of observations = 16
H0 SS = 24.125, df = 2, MS = 12.0625
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 14.8462
Prob[F > F0] = 0.00203
=================================
Dap�� 7. Mon Jan 12 03:20:11 2004
�
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
=================================
Dap�� 8. Mon Jan 12 03:20:11 2004
�
Testing Ho: a
Denominator: a * b
EMS(a*b) =
��� 4 Var(a*b)
��� 1 Var(Error)
EMS(a) =
��� 8 Var(a)
��� 4 Var(a*b)
��� 1 Var(Error)
Error for a =
��� 1 MS(a*b)
Number of observations = 16
H0 SS = 39.0625, df = 1, MS = 39.0625
Residual df = 1, MS = 10.5625
F0 = 3.69822
Prob[F > F0] = 0.30528
=================================
Dap�� 9. Mon Jan 12 03:20:11 2004
�
Testing Ho: b
Denominator: a * b b * c a * b * c
EMS(a*b) =
��� 4 Var(a*b)
��� 2 Var(a*b*c)
��� 1 Var(Error)
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(a*b)
��� 4 Var(b*c)
��� 2 Var(a*b*c)
��� 1 Var(Error)
Error for b =
��� 1 MS(a*b)
��� 1 MS(b*c)
��� -1 MS(a*b*c)
Number of observations = 16
H0 SS = 770.062, df = 1, MS = 770.062
Residual df = 2.57671, MS = 21.8125
F0 = 35.3037
Prob[F > F0] = 0.01701
=================================
Dap� 10. Mon Jan 12 03:20:11 2004
�
Testing Ho: a * b
Denominator: 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� 11. Mon Jan 12 03:20:11 2004
�
Testing Ho: c * b
Denominator: a * c * b
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
Random model, unbalanced [Back to Sample output]
AMD: pp. 265 - 273
=================================
Dap�� 1. Mon Jan 12 03:20:12 2004
�
Response variable: efficiency
Treatment������ Levels
--------������� ------
plant����������
1 2 3
site�����������
1 2 3 4
worker��������� 1
2 3
�
=================================
Dap�� 2. Mon Jan 12 03:20:13 2004
�
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. Mon Jan 12 03:20:13 2004
�
Testing Ho: plant
Number of observations = 118
H0 SS = 3866.33, df = 2, MS = 1933.16
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 387.942
Prob[F > F0] = 0.00001
=================================
Dap�� 4. Mon Jan 12 03:20:13 2004
�
Testing Ho: plant*worker
Number of observations = 118
H0 SS = 1949.66, df = 6, MS = 324.943
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 65.2085
Prob[F > F0] = 0.00001
=================================
Dap�� 5. Mon Jan 12 03:20:13 2004
�
Testing Ho: plant*site
Number of observations = 118
H0 SS = 610.302, df = 9, MS = 67.8114
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 13.6082
Prob[F > F0] = 0.00001
=================================
Dap�� 6. Mon Jan 12 03:20:13 2004
�
Testing Ho: plant*site*worker
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. Mon Jan 12 03:20:13 2004
�
Testing Ho: site * plant
Denominator: 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. Mon Jan 12 03:20:13 2004
�
Response variable: efficiency
Treatment������ Levels
--------������� ------
plant����������
1 2 3
site�����������
1 2 3 4
worker��������� 1
2 3
�
=================================
Dap�� 9. Mon Jan 12 03:20:13 2004
�
Testing Ho: plant 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� 10. Mon Jan 12 03:20:14 2004
�
Testing Ho: plant
Number of observations = 118
H0 SS = 3866.33, df = 2, MS = 1933.16
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 387.942
Prob[F > F0] = 0.00001
=================================
Dap� 11. Mon Jan 12 03:20:14 2004
�
Testing Ho: plant*worker
Number of observations = 118
H0 SS = 1949.66, df = 6, MS = 324.943
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 65.2085
Prob[F > F0] = 0.00001
=================================
Dap� 12. Mon Jan 12 03:20:14 2004
�
Testing Ho: site*worker*plant
Number of observations = 118
H0 SS = 2677.73, df = 27, MS = 99.1752
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 19.9022
Prob[F > F0] = 0.00001
=================================
Dap� 13. Mon Jan 12 03:20:14 2004
�
Testing Ho: worker * plant
Denominator: site * worker * plant
EMS(plant*site*worker) =
��� 3.06428 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 =
��� 0.22875 MS(Error)
��� 0.77125 MS(plant*site*worker)
Number of observations = 118
H0 SS = 1949.66, df = 6, MS = 324.943
Residual df = 27.8087, MS = 77.6288
F0 = 4.18585
Prob[F > F0] = 0.00403
=================================
Dap� 14. Mon Jan 12 03:20:14 2004
�
Testing Ho: plant
Denominator: worker * plant site * worker * plant
EMS(plant*worker) =
��� 9.45329 Var(plant*worker)
��� 2.36332 Var(plant*site*worker)
��� 1 Var(Error)
EMS(plant*site*worker) =
��� 3.06428 Var(plant*site*worker)
��� 1 Var(Error)
EMS(plant) =
��� 27.598 Var(plant)
��� 9.19933 Var(plant*worker)
��� 2.29983 Var(plant*site*worker)
��� 1 Var(Error)
Error for plant =
��� 0.0268648 MS(Error)
��� 0.973135 MS(plant*worker)
Number of observations = 118
H0 SS = 3866.33, df = 2, MS = 1933.16
Residual df = 6.00508, MS = 316.347
F0 = 6.1109
Prob[F > F0] = 0.03567
Mixed model, balanced [Back to Sample output]
AMD:� pp. 285-289
=================================
Dap�� 1. Mon Jan 12 03:20:16 2004
�
Response variable: productivity
Treatment������ Levels
--------������� ------
machine�������� 1 2 3
person��������� 1
2 3 4 5 6
�
=================================
Dap�� 2. Mon Jan 12 03:20:16 2004
�
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. Mon Jan 12 03:20:16 2004
�
Testing Ho: machine
Number of observations = 54
H0 SS = 1755.26, df = 2, MS = 877.632
Error SS = 33.2867, df = 36, MS = 0.92463
F0 = 949.171
Prob[F > F0] = 0.00001
=================================
Dap�� 4. Mon Jan 12 03:20:16 2004
�
Testing Ho: person
Number of observations = 54
H0 SS = 1241.89, df = 5, MS = 248.379
Error SS = 33.2867, df = 36, MS = 0.92463
F0 = 268.625
Prob[F > F0] = 0.00001
=================================
Dap�� 5. Mon Jan 12 03:20:16 2004
�
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�� 6. Mon Jan 12 03:20:16 2004
�
Testing Ho: person
Denominator: 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�� 7. Mon Jan 12 03:20:16 2004
�
Testing Ho: machine
Denominator: 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. Mon Jan 12 03:20:16 2004
�
Least-squares means for: machine
LSD� method
Minimum significant differences are for level 0.05000
=================================
Dap�� 9. Mon Jan 12 03:20:17 2004
�
���������������������������
_stat_ for _lsm_ / machine
===================================================================
|�������������������������
|52.3556���� |60.3222���� |66.2722����
|
|--------+--------+--------|------------+------------+------------|
|_type_� |_LSMEAN_|_machine|1����������
|2���������� |3����������
|
|========|========|========|============|============|============|
|EFFN��� |0������
|������� |���������
18|��������� 18|���������
18|
|--------+--------+--------|------------+------------+------------|
|LSMDIFF |52.3556 |1������ |�����������
|���� 7.96667|���� 13.9167|
|������� |--------+--------|------------+------------+------------|
|������� |60.3222 |2������
|��� -7.96667|�����������
|������� 5.95|
|������� |--------+--------|------------+------------+------------|
|������� |66.2722 |3������
|��� -13.9167|������ -5.95|�����������
|
|--------+--------+--------|------------+------------+------------|
|MINDIFF |52.3556 |1������ |�����������
|���� 4.85062|���� 4.85062|
|������� |--------+--------|------------+------------+------------|
|������� |60.3222 |2������
|���� 4.85062|�����������
|���� 4.85062|
|������� |--------+--------|------------+------------+------------|
|������� |66.2722 |3������
|���� 4.85062|���� 4.85062|�����������
|
|--------+--------+--------|------------+------------+------------|
|PROB��� |52.3556 |1������
|����������� |�
0.00439263| 7.90648e-05|
|������� |--------+--------|------------+------------+------------|
|������� |60.3222 |2������
|� 0.00439263|�����������
|�� 0.0210791|
|������� |--------+--------|------------+------------+------------|
|������� |66.2722 |3������
| 7.90648e-05|�� 0.0210791|�����������
|
-------------------------------------------------------------------
�
Mixed model, unbalanced [Back to Sample output]
AMD:� pp. 290 - 295
=================================
Dap�� 1. Mon Jan 12 03:20:18 2004
�
Response variable: productivity
Treatment������ Levels
--------������� ------
machine�������� 1 2 3
person��������� 1
2 3 4 5 6
�
=================================
Dap�� 2. Mon Jan 12 03:20:18 2004
�
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. Mon Jan 12 03:20:18 2004
�
Testing Ho: machine
Number of observations = 44
H0 SS = 1238.2, df = 2, MS = 619.099
Error SS = 22.6867, df = 26, MS = 0.872564
F0 = 709.517
Prob[F > F0] = 0.00001
=================================
Dap�� 4. Mon Jan 12 03:20:18 2004
�
Testing Ho: person
Number of observations = 44
H0 SS = 1011.05, df = 5, MS = 202.211
Error SS = 22.6867, df = 26, MS = 0.872564
F0 = 231.743
Prob[F > F0] = 0.00001
=================================
Dap�� 5. Mon Jan 12 03:20:18 2004
�
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�� 6. Mon Jan 12 03:20:19 2004
�
Testing Ho: person
Denominator: 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�� 7. Mon Jan 12 03:20:19 2004
�
Testing Ho: machine
Denominator: 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�� 8. Mon Jan 12 03:20:19 2004
�
Least-squares means for: machine
LSD� method
Minimum significant differences are for level 0.05000
=================================
Dap�� 9. Mon Jan 12 03:20:19 2004
�
���������������������������
_stat_ for _lsm_ / machine
===================================================================
|�������������������������
|52.3136���� |60.022�����
|66.2722���� |
|--------+--------+--------|------------+------------+------------|
|_type_� |_LSMEAN_|_machine|1����������
|2���������� |3����������
|
|========|========|========|============|============|============|
|EFFN��� |0������
|������� |���� 11.4088|����
12.8061|��������� 18|
|--------+--------+--------|------------+------------+------------|
|LSMDIFF |52.3136 |1������ |�����������
|����� 7.7084|���� 13.9586|
|������� |--------+--------|------------+------------+------------|
|������� |60.022� |2������
|���� -7.7084|�����������
|���� 6.25021|
|������� |--------+--------|------------+------------+------------|
|������� |66.2722 |3������
|��� -13.9586|��� -6.25021|�����������
|
|--------+--------+--------|------------+------------+------------|
|MINDIFF |52.3136 |1������ |�����������
|���� 5.54278|���� 5.15225|
|������� |--------+--------|------------+------------+------------|
|������� |60.022� |2������
|���� 5.54278|�����������
|���� 4.97724|
|������� |--------+--------|------------+------------+------------|
|������� |66.2722 |3������
|���� 5.15225|���� 4.97724|�����������
|
|--------+--------+--------|------------+------------+------------|
|PROB��� |52.3136 |1������
|����������� |��
0.0112602| 0.000124789|
|������� |--------+--------|------------+------------+------------|
|������� |60.022� |2������
|�� 0.0112602|�����������
|�� 0.0188437|
|������� |--------+--------|------------+------------+------------|
|������� |66.2722 |3������
| 0.000124789|�� 0.0188437|�����������
|
-------------------------------------------------------------------
�
Split plot�� [Back to Sample output]
AMD:� pp. 297 - 308
=================================
Dap�� 1. Mon Jan 12 02:33:16 2004
Whole plot (block, fertilizer) analysis
Response variable: yield
Treatment������ Levels
--------������� ------
fertilizer����� 1 2 3 4
block����������
1 2
variety�������� 1 2
�
=================================
Dap�� 2. Mon Jan 12 02:33:16 2004
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer block
Denominator: 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. Mon Jan 12 02:33:16 2004
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer
Denominator: fertilizer*block variety fertilizer*variety block*variety
fertilizer*block*variety
Number of observations = 16
H0 SS = 40.19, df = 3, MS = 13.3967
Residual SS = 19.1575, df = 11, MS = 1.74159
F0 = 7.6922
Prob[F > F0] = 0.00479
=================================
Dap�� 4. Mon Jan 12 02:33:17 2004
Whole plot (block, fertilizer) analysis
Testing Ho: block
Denominator: fertilizer*block variety fertilizer*variety block*variety
fertilizer*block*variety
Number of observations = 16
H0 SS = 131.103, df = 1, MS = 131.103
Residual SS = 19.1575, df = 11, MS = 1.74159
F0 = 75.2774
Prob[F > F0] = 0.00001
=================================
Dap�� 5. Mon Jan 12 02:33:17 2004
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer
Denominator: 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�� 6. Mon Jan 12 02:33:17 2004
Whole plot (block, fertilizer) analysis
Least-squares means for: fertilizer
LSD� method
Minimum significant differences are for level 0.05000
=================================
Dap�� 7. Mon Jan 12 02:33:18 2004
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|���������� 4|����������
4|���������� 4|
|--------+--------+--------|------------+------------+------------+------------|
|LSMDIFF |38.8��� |1������
|����������� |��������
0.6|���������� 1|��������
4.1|
|������� |--------+--------|------------+------------+------------+------------|
|������� |39.4���
|3������ |�������
-0.6|�����������
|�������� 0.4|��������
3.5|
|������� |--------+--------|------------+------------+------------+------------|
|������� |39.8���
|2������ |���������
-1|������� -0.4|�����������
|�������� 3.1|
|������� |--------+--------|------------+------------+------------+------------|
|������� |42.9���
|4������ |�������
-4.1|������� -3.5|�������
-3.1|�����������
|
|--------+--------+--------|------------+------------+------------+------------|
|MINDIFF |38.8��� |1������
|����������� |����
3.41955|���� 3.41955|���� 3.41955|
|������� |--------+--------|------------+------------+------------+------------|
|������� |39.4���
|3������ |���� 3.41955|�����������
|���� 3.41955|���� 3.41955|
|������� |--------+--------|------------+------------+------------+------------|
|������� |39.8���
|2������ |���� 3.41955|����
3.41955|�����������
|���� 3.41955|
|������� |--------+--------|------------+------------+------------+------------|
|������� |42.9���
|4������ |���� 3.41955|����
3.41955|���� 3.41955|�����������
|
|--------+--------+--------|------------+------------+------------+------------|
|PROB��� |38.8��� |1������
|����������� |���
0.615544|��� 0.420682|��� 0.031666|
|������� |--------+--------|------------+------------+------------+------------|
|������� |39.4���
|3������ |��� 0.615544|�����������
|��� 0.734441|�� 0.0472285|
|������� |--------+--------|------------+------------+------------+------------|
|������� |39.8���
|2������ |��� 0.420682|���
0.734441|�����������
|�� 0.0632693|
|������� |--------+--------|------------+------------+------------+------------|
|������� |42.9���
|4������ |��� 0.031666|��
0.0472285|�� 0.0632693|�����������
|
--------------------------------------------------------------------------------
=================================
Dap�� 8. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Response variable: yield
Treatment������ Levels
--------������� ------
fertilizer����� 1 2 3 4
block����������
1 2
variety�������� 1 2
�
=================================
Dap�� 9. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer block fertilizer*block variety fertilizer*variety
Denominator: 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� 10. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 40.19, df = 3, MS = 13.3967
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 6.35666
Prob[F > F0] = 0.05300
=================================
Dap� 11. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: block
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 131.103, df = 1, MS = 131.103
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 62.2076
Prob[F > F0] = 0.00140
=================================
Dap� 12. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: variety
Denominator: 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� 13. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer*block
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 6.9275, df = 3, MS = 2.30917
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 1.09569
Prob[F > F0] = 0.44760
=================================
Dap� 14. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer*variety
Denominator: 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. Mon Jan 12 03:20:29 2004
�
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. Mon Jan 12 03:20:34 2004
�
Testing Ho: sampler area order
Denominator: 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. Mon Jan 12 03:20:34 2004
�
Testing Ho: sampler
Denominator: 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. Mon Jan 12 03:20:34 2004
�
Least-squares means for: sampler
LSD� method
Minimum significant differences are for level 0.05000
=================================
Dap�� 5. Mon Jan 12 03:20:35 2004
�
���������������������������
_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|���������� 6|����������
6|���������� 6|����������
6|���������� 6|
|--------+--------+--------|------------+------------+------------+------------+------------+------------|
|LSMDIFF |1.2���� |F������
|����������� |����
1.46667|���� 4.38333|���� 4.86667|����
4.91667|���� 5.71667|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |2.66667 |E������
|��� -1.46667|�����������
|���� 2.91667|��������
3.4|������� 3.45|�������
4.25|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |5.58333 |B������
|��� -4.38333|��� -2.91667|�����������
|��� 0.483333|��� 0.533333|����
1.33333|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.06667 |A������
|��� -4.86667|�������
-3.4|�� -0.483333|�����������
|������� 0.05|�������
0.85|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.11667 |C������
|��� -4.91667|������ -3.45|��
-0.533333|������ -0.05|�����������
|�������� 0.8|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.91667 |D������
|��� -5.71667|������ -4.25|���
-1.33333|������ -0.85|�������
-0.8|�����������
|
|--------+--------+--------|------------+------------+------------+------------+------------+------------|
|MINDIFF |1.2���� |F������
|����������� |����
2.19708|���� 2.19708|���� 2.19708|����
2.19708|���� 2.19708|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |2.66667 |E������
|���� 2.19708|�����������
|���� 2.19708|���� 2.19708|����
2.19708|���� 2.19708|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |5.58333 |B������
|���� 2.19708|���� 2.19708|�����������
|���� 2.19708|���� 2.19708|����
2.19708|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.06667 |A������
|���� 2.19708|���� 2.19708|����
2.19708|�����������
|���� 2.19708|���� 2.19708|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.11667 |C������
|���� 2.19708|���� 2.19708|����
2.19708|���� 2.19708|�����������
|���� 2.19708|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.91667 |D������
|���� 2.19708|���� 2.19708|����
2.19708|���� 2.19708|���� 2.19708|�����������
|
|--------+--------+--------|------------+------------+------------+------------+------------+------------|
|PROB��� |1.2���� |F������
|����������� |���
0.179066| 0.000482096| 0.000165276| 0.000148021| 2.59349e-05|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |2.66667 |E������
|��� 0.179066|�����������
|�� 0.0118367|�� 0.0042174|� 0.00378313|�
0.00064819|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |5.58333 |B������
| 0.000482096|�� 0.0118367|�����������
|��� 0.651264|��� 0.618142|���
0.220099|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.06667 |A������
| 0.000165276|�� 0.0042174|��� 0.651264|�����������
|��� 0.962609|��� 0.429156|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.11667 |C������
| 0.000148021|� 0.00378313|��� 0.618142|���
0.962609|�����������
|��� 0.456392|
|������� |--------+--------|------------+------------+------------+------------+------------+------------|
|������� |6.91667 |D������
| 2.59349e-05|� 0.00064819|��� 0.220099|���
0.429156|��� 0.456392|�����������
|
----------------------------------------------------------------------------------------------------------
=================================
Dap�� 6. Mon Jan 12 03:20:35 2004
�
Testing Ho: area
Denominator: 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. Mon Jan 12 03:20:35 2004
�
Testing Ho: order
Denominator: 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. Mon Jan 12 04:25:05 2004
Test Ho:
u11 - u21 - (u13 - u23) = 0
u21 - u31 - (u22 - u32) = 0
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�� 2. Mon Jan 12 04:25:05 2004
Test Ho:
u11 + u13 - (u21 + u23) = 0
u21 + u22 - (u31 + u32) = 0
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. Mon Jan 12 03:20:40 2004
�
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. Mon Jan 12 03:20:39 2004
Correlations
������������������
_corr_ for _var2_
====================================================================================
|_var1_� |_type_� |x1���������
|x2��������� |x3���������
|x4��������� |y����������
|
|========|========|============|============|============|============|============|
|x1����� |CORR��� |����������
1|��� 0.971236|�� -0.668403|���
0.651995|��� 0.740306|
|������� |--------|------------+------------+------------+------------+------------|
|������� |N������
|��������� 10|���������
10|��������� 10|���������
10|��������� 10|
|������� |--------|------------+------------+------------+------------+------------|
|������� |PCORR��
|���������� 0| 2.89253e-06|���
0.034618|�� 0.0410635|�� 0.0143438|
|--------+--------|------------+------------+------------+------------+------------|
|x2����� |CORR��� |���
0.971236|���������� 1|���
-0.59782|��� 0.526844|��� 0.627619|
|������� |--------|------------+------------+------------+------------+------------|
|������� |N������
|��������� 10|���������
10|��������� 10|���������
10|��������� 10|
|������� |--------|------------+------------+------------+------------+------------|
|������� |PCORR��
| 2.89253e-06|����������
0|�� 0.0679458|��� 0.117662|�� 0.0520562|
|--------+--------|------------+------------+------------+------------+------------|
|x3����� |CORR��� |��
-0.668403|��� -0.59782|����������
1|��� -0.84101|�� -0.780072|
|������� |--------|------------+------------+------------+------------+------------|
|������� |N������
|��������� 10|���������
10|��������� 10|���������
10|��������� 10|
|������� |--------|------------+------------+------------+------------+------------|
|������� |PCORR��
|��� 0.034618|�� 0.0679458|����������
0|� 0.00229667|� 0.00777379|
|--------+--------|------------+------------+------------+------------+------------|
|x4����� |CORR��� |���
0.651995|��� 0.526844|��� -0.84101|����������
1|��� 0.977603|
|������� |--------|------------+------------+------------+------------+------------|
|������� |N������
|��������� 10|���������
10|��������� 10|���������
10|��������� 10|
|������� |--------|------------+------------+------------+------------+------------|
|������� |PCORR��
|�� 0.0410635|��� 0.117662|� 0.00229667|����������
0| 1.07154e-06|
|--------+--------|------------+------------+------------+------------+------------|
|y������ |CORR���
|��� 0.740306|��� 0.627619|�� -0.780072|���
0.977603|���������� 1|
|������� |--------|------------+------------+------------+------------+------------|
|������� |N������
|��������� 10|���������
10|��������� 10|���������
10|��������� 10|
|������� |--------|------------+------------+------------+------------+------------|
|������� |PCORR��
|�� 0.0143438|�� 0.0520562|� 0.00777379| 1.07154e-06|����������
0|
------------------------------------------------------------------------------------
=================================
Dap�� 2. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ | x4
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
�� x4������������������
1.02579��� 0.0780755����� 13.1384��������
0.00001
=================================
Dap�� 3. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ x4 | x1
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
�� x4�����������������
0.903324���� 0.084129�����
10.7374�������� 0.00002
�� x1������������������
1.88521���� 0.844369�����
2.23268�������� 0.06073
=================================
Dap�� 4. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ x4 x1 | x3
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
�� x4������������������
1.07907��� 0.0842512����� 12.8078��������
0.00002
�� x1������������������
2.40786���� 0.615063�����
3.91482�������� 0.00785
�� x3�����������������
0.936516���� 0.322582�����
2.90318�������� 0.02723
=================================
Dap�� 5. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ x4 x1 x3 | x2
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
�� x4������������������
1.12232���� 0.107355�����
10.4543�������� 0.00014
�� x1�����������������
0.523737����� 2.75266����
0.190266�������� 0.85659
�� x3�����������������
0.994528���� 0.346992�����
2.86614�������� 0.03516
�� x2�����������������
0.775412����� 1.10155����
0.703928�������� 0.51290
Ordinal cross-classification [Back to Sample output]
CDA: pp. 49 - 50
=================================
Dap�� 1. Mon Jan 12 03:20:25 2004
�
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. Mon Jan 12 03:20:25 2004
�
������������������
_cell_ for jobsat
=======================================================================
|income� |_type_� |1verydis��� |2littledis�
|3modsat���� |4verysat��� |
|========|========|============|============|============|============|
|00-06�� |COUNT�� |���������
20|��������� 24|���������
80|��������� 82|
|������� |--------|------------+------------+------------+------------|
|������� |EXPECTED|����
14.1754|���� 24.6926|���� 72.9345|����
94.1976|
|--------+--------|------------+------------+------------+------------|
|06-15�� |COUNT�� |���������
22|��������� 38|��������
104|�������� 125|
|������� |--------|------------+------------+------------+------------|
|������� |EXPECTED|����
19.8868|���� 34.6415|���� 102.321|����
132.151|
|--------+--------|------------+------------+------------+------------|
|15-25�� |COUNT�� |���������
13|��������� 28|���������
81|�������� 113|
|������� |--------|------------+------------+------------+------------|
|������� |EXPECTED|����
16.1709|���� 28.1687|�����
83.202|���� 107.458|
|--------+--------|------------+------------+------------+------------|
|25-���� |COUNT�� |����������
7|��������� 18|���������
54|��������� 92|
|������� |--------|------------+------------+------------+------------|
|������� |EXPECTED|����
11.7669|���� 20.4972|���� 60.5427|����
78.1931|
-----------------------------------------------------------------------
�
Stratified 2x2 tables [Back to Sample output]
CDA: pp. 232 - 233
=================================
Dap�� 1. Mon Jan 12 03:20:24 2004
�
Cochran-Mantel-Haenszel test for delay x response, stratified by
penicillin
M0-squared = 3.92857, Prob[M-squared > M0-squared] = 0.0475
=================================
Dap�� 2. Mon Jan 12 03:20:24 2004
�
For: penicillin = 0.125
��������� _cell_ for
response
====================================
|delay�� |cured������ |died�������
|
|========|============|============|
|1.5h��� |����������
0|���������� 5|
|--------|------------+------------|
|none��� |����������
0|���������� 6|
------------------------------------
=================================
Dap�� 3. Mon Jan 12 03:20:24 2004
�
For: penicillin = 0.250
��������� _cell_ for
response
====================================
|delay�� |cured������ |died�������
|
|========|============|============|
|1.5h��� |����������
0|���������� 6|
|--------|------------+------------|
|none��� |����������
3|���������� 3|
------------------------------------
=================================
Dap�� 4. Mon Jan 12 03:20:24 2004
�
For: penicillin = 0.500
��������� _cell_ for
response
====================================
|delay�� |cured������ |died�������
|
|========|============|============|
|1.5h��� |����������
2|���������� 4|
|--------|------------+------------|
|none��� |����������
6|���������� 0|
------------------------------------
=================================
Dap�� 5. Mon Jan 12 03:20:24 2004
�
For: penicillin = 1.000
��������� _cell_ for
response
====================================
|delay�� |cured������ |died�������
|
|========|============|============|
|1.5h��� |����������
6|���������� 0|
|--------|------------+------------|
|none��� |����������
5|���������� 1|
------------------------------------
=================================
Dap�� 6. Mon Jan 12 03:20:24 2004
�
For: penicillin = 4.000
��������� _cell_ for
response
====================================
|delay�� |cured������ |died�������
|
|========|============|============|
|1.5h��� |����������
5|���������� 0|
|--------|------------+------------|
|none��� |����������
2|���������� 0|
------------------------------------
Loglinear models�� [Back to Sample output]
CDA: pp. 135 - 138, 171 - 174, 176 - 177
=================================
Dap�� 1. Mon Jan 12 03:19:54 2004
(DV, P) vs (D, V, P)
Loglinear model:
numerical indexes of classification variables
Number� defendant� victim� penalty
------� ---------� ------� -------
���� 1� black�����
black�� no
���� 2� white�����
white�� yes
�
=================================
Dap�� 2. Mon Jan 12 03:19:55 2004
(DV, P) vs (D, V, P)
Maximum likelihood estimation
Cell count: n
Class and aux variables: _defendant _victim _penalty
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��� 0.0946427���
*��� defendant:1
�� -0.582116��� 0.0946427���
*��� victim:1
��� 0.827863��� 0.0946427���
?��� defendant*victim:1:1
���� 1.04323��� 0.0883545���
*��� penalty:1
�
=================================
Dap�� 3. Mon Jan 12 03:19:55 2004
(DV, P) vs (D, V, P)
������������������������������
n for _type_ / penalty
===========================================================
|����������������������������
|FIT��������� |OBS���������
|
|--------------+--------------|------+------+------+------|
|defendant���� |victim�������
|no��� |yes�� |no��� |yes��
|
|==============|==============|======|======|======|======|
|black�������� |black��������
| 91.63| 11.37| 97.00|� 6.00|
|�������������
|--------------|------+------+------+------|
|�������������
|white�������� | 56.04|� 6.96|
52.00| 11.00|
|--------------+--------------|------+------+------+------|
|white�������� |black��������
|� 8.01|� 0.99|� 9.00|� 0.00|
|�������������
|--------------|------+------+------+------|
|�������������
|white�������� |134.33| 16.67|132.00|
19.00|
-----------------------------------------------------------
=================================
Dap�� 4. Mon Jan 12 03:19:55 2004
(DV, VP) vs (DV, P)
Loglinear model:
numerical indexes of classification variables
Number� defendant� victim� penalty
------� ---------� ------� -------
���� 1� black�����
black�� no
���� 2� white�����
white�� yes
�
=================================
Dap�� 5. Mon Jan 12 03:19:55 2004
(DV, VP) vs (DV, P)
Maximum likelihood estimation
Cell count: n
Class and aux variables: _defendant _victim _penalty
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��� 0.0946425���
*��� defendant:1
��� -0.79861����� 0.13779���
*��� victim:1
��� 0.827913��� 0.0946425���
*��� defendant*victim:1:1
����� 1.1713���� 0.115885���
*��� penalty:1
��� 0.264535���� 0.115885���
?��� victim*penalty:1:1
�
=================================
Dap�� 6. Mon Jan 12 03:19:55 2004
(DV, VP) vs (DV, P)
������������������������������
n for _type_ / penalty
===========================================================
|����������������������������
|FIT��������� |OBS���������
|
|--------------+--------------|------+------+------+------|
|defendant���� |victim�������
|no��� |yes�� |no��� |yes��
|
|==============|==============|======|======|======|======|
|black�������� |black��������
| 97.48|� 5.52| 97.00|� 6.00|
|�������������
|--------------|------+------+------+------|
|�������������
|white�������� | 54.17|� 8.83|
52.00| 11.00|
|--------------+--------------|------+------+------+------|
|white�������� |black��������
|� 8.52|� 0.48|� 9.00|� 0.00|
|�������������
|--------------|------+------+------+------|
|�������������
|white�������� |129.83| 21.17|132.00|
19.00|
-----------------------------------------------------------
=================================
Dap�� 7. Mon Jan 12 03:19:55 2004
(DV, DP, VP) vs (DV, VP)
Loglinear model:
numerical indexes of classification variables
Number� defendant� victim� penalty
------� ---------� ------� -------
���� 1� black�����
black�� no
���� 2� white�����
white�� yes
�
=================================
Dap�� 8. Mon Jan 12 03:19:55 2004
(DV, DP, VP) vs (DV, VP)
Maximum likelihood estimation
Cell count: n
Class and aux variables: _defendant _victim _penalty
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���
0.5401
��� Estimate���������
ASE� Model� Parameter
���� 2.69211���� 0.142941���
*��� _mu
��� 0.479402���� 0.124301���
*��� defendant:1
�� -0.854001���� 0.146841���
*��� victim:1
��� 0.839499��� 0.0954928���
*��� defendant*victim:1:1
���� 1.20005���� 0.119974���
*��� penalty:1
�� -0.110105���� 0.100222���
?��� defendant*penalty:1:1
��� 0.331103���� 0.129837���
*��� victim*penalty:1:1
�
=================================
Dap�� 9. Mon Jan 12 03:19:55 2004
(DV, DP, VP) vs (DV, VP)
������������������������������
n for _type_ / penalty
===========================================================
|����������������������������
|FIT��������� |OBS���������
|
|--------------+--------------|------+------+------+------|
|defendant���� |victim�������
|no��� |yes�� |no��� |yes��
|
|==============|==============|======|======|======|======|
|black�������� |black��������
| 97.33|� 5.67| 97.00|� 6.00|
|�������������
|--------------|------+------+------+------|
|�������������
|white�������� | 51.67| 11.33|
52.00| 11.00|
|--------------+--------------|------+------+------+------|
|white�������� |black��������
|� 8.67|� 0.33|� 9.00|� 0.00|
|�������������
|--------------|------+------+------+------|
|�������������
|white�������� |132.33| 18.67|132.00|
19.00|
-----------------------------------------------------------
Logit model for linear-by-linear association [Back to Sample output]
CDA: pp.� 261 -� 269
=================================
Dap�� 1. Mon Jan 12 03:19:56 2004
�
Maximum likelihood estimation
Cell count: count
Class and aux variables: income jobsat
Statistic�������������
df����� Prob
G2[Model]�� =�� 2.39��� 8���
0.9669
G2[Reduced] =� 12.04��� 9���
0.2113
G2[Diff]��� =�� 9.65���
1��� 0.0019
X2[Model]�� =�� 2.33��� 8���
0.9693
��� Estimate���������
ASE� Model� Parameter
���� 3.47292���� 0.102512���
*��� mu
��� 0.294928���� 0.132411���
*��� <6
��� 0.396336����� 0.06927���
*��� 6-15
� -0.0581172���� 0.068397���
*��� 15-25
�� -0.805298���� 0.118785���
*��� VeryDis
�� -0.385834��� 0.0853789���
*��� LittleDis
��� 0.548591��� 0.0620011���
*��� ModSat
���� 0.11199��� 0.0364075���
?��� Inc*Sat
�
=================================
Dap�� 2. Mon Jan 12 03:19:57 2004
�
��������� count for
_type_ / jobsat
==================================================================
|������� |FIT�����������������������
|OBS�����������������������
|
|--------|------+------+------+------+------+------+------+------|
|income� |0���� |1����
|2���� |3���� |0����
|1���� |2���� |3����
|
|========|======|======|======|======|======|======|======|======|
|0������ | 19.35| 29.43| 74.93| 82.30|
20.00| 24.00| 80.00| 82.00|
|--------|------+------+------+------+------+------+------+------|
|1������ | 21.41| 36.43|103.73|127.43|
22.00| 38.00|104.00|125.00|
|--------|------+------+------+------+------+------+------+------|
|2������ | 13.59| 25.86| 82.37|113.18|
13.00| 28.00| 81.00|113.00|
|--------|------+------+------+------+------+------+------+------|
|3������ |� 7.65| 16.28| 57.98|
89.10|� 7.00| 18.00| 54.00| 92.00|
------------------------------------------------------------------
Logistic regression� [Back to Sample output]
CDA: pp. 87 - 89
=================================
Dap�� 1. Mon Jan 12 03:20:27 2004
�
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: 2014/01/06 21:03:37 $ $Author: sebdiaz $