Chapters 3 and 4
Numerical Example-
0 35 59 52 24 47
35 0 17 88 50 41
59 17 0 43 63 53
52 88 43 0 37 29
24 50 63 37 0 34
47 41 53 29 34 0
Objects: (enumerated)
Dissimilarity matrix for lipread
consonants [Manning, S. K., & Shofner, E. (1991). Similarity ratings
and confusability of lipread consonants compared with similarity ratings of
auditory and orthographic stimuli. American Journal of Psychology, 104,
587-604.]:
0 141 176 308 155 118 265 296 298 331 280 69 284 318 149
182 227 355 282 308 129
141 0 118 292 149 280 306 229 194 325 265 214 312 314 292
122 182 325 255 329 49
176 118 0 251 147 288 235 273 227 324 196 149 327 321 245
90 173 335 286 337 82
308 292 251 0 298 271 282 275 249 216 243 290 331 296 204
288 265 355 255 316 311
155 149 147 298 0 273 157 269 290 324 241 275 271 300 284
169 173 312 298 331 182
118 280 288 271 273 0 267 269 233 267 200 282 306 280 249
288 292 345 178 325 249
265 306 235 282 157 267 0 182 241 322 269 275 300 324 286
298 275 349 286 286 278
296 229 273 275 269 269 182 0 184 296 204 288 247 214 271
229 292 318 198 325 275
298 194 227 249 290 233 241 184 0 175 149 269 316 202 147
271 345 361 176 320 292
331 325 324 216 324 267 322 296 175 0 243 296 312 302 276
304 296 316 271 335 292
280 265 196 243 241 200 269 204 149 243 0 243 255 243 176
210 269 347 214 308 273
69 214 149 290 275 282 275 288 269 296 243 0 308 302 286
176 200 365 312 337 180
284 312 327 331 271 306 300 247 316 312 255 308 0 192 284
265 294 298 327 284 251
318 314 321 296 300 280 324 214 202 302 243 302 192 0 300
306 302 337 227 363 271
149 292 245 204 284 249 286 271 147 276 176 286 284 300 0
269 292 363 75 331 208
182 122 90 288 169 288 298 229 271 304 210 176 265 306
269 0 131 349 220 347 147
227 182 173 265 173 292 275 292 345 296 269 200 294 302
292 131 0 329 251 310 204
355 325 335 355 312 345 349 318 361 316 347 365 298 337
363 349 329 0 334 300 345
282 255 286 255 298 178 286 198 176 271 214 312 327 227
75 220 251 334 0 298 239
308 329 337 316 331 325 286 325 320 335 308 337 284 363
331 347 310 300 298 0 296
129 49 82 311 182 249 278 275 292 292 273 180 251 271 208
147 204 345 239 296 0
Objects:
b
c
d
f
g
h
j
k
l
m
n
p
q
r
s
t
v
w
x
y
z
Chapter 5
Numerical Example (squared Euclidean distances for Figure
5.1)
0 29 8 25 17 5
29 0 9 8 10 36
8 9 0 5 13 9
25 8 5 0 26 20
17 10 13 26 0 34
5 36 9 20 34 0
Objects: (enumerated)
Matrix of squared Euclidean distances between German
towns (rounded to integral values):
0 20970 13210 11114 1017 12041 18482 17645 7362 6373
24965 410 5408 26533 16372 36297 24361 3425 9344 5098 23993 3133
20970 0 20800
33092 21789 5897 38036 5441 4176 13189 48689 18464 5162 337 1546 3681 1417
10205 3338 21412 42041 23701
13210 20800 0 2468
7453 28649 2644 6113 17104 1781 5857 8992 13210 23985 11194 22849 15769 18845
8506 2180 3737 4437
11114 33092 2468 0
5417 35869 1000 15381 23300 4525 2777 7892 18394 38069 21370 39125 28729 21625
15658 1664 2605 2465
1017 21789 7453
5417 0 16580 10829 14216 9549 3298 15938 293 6737 27112 15109 34164 22810 6416
8585 1657 15140 580
12041 5897 28649
35869 16580 0 45161 15940 1481 16490 56842 12577 2993 8468 9061 18896 12250
2628 6905 22093 51840 21320
18482 38036 2644
1000 10829 45161 0 16729 30500 7225 685 14020 24930 42505 24650 40625 31301
30125 19890 4520 377 6397
17645 5441 6113
15381 14216 15940 16729 0 8609 4330 23570 13385 7141 6416 1249 5444 2290 14152
2173 9701 18532 12932
7362 4176 17104
23300 9549 1481 30500 8609 0 8125 40265 6800 290 6865 4450 14625 7921 1325 2210
12580 35897 12517
6373 13189 1781
4525 3298 16490 7225 4330 8125 0 12356 3581 5437 16562 6485 18850 11240 9050
3349 1117 9866 2306
24965 48689 5857
2777 15938 56842 685 23570 40265 12356 0 20025 33805 53530 33245 50450 40532
39418 27925 8425 394 10474
410 18464 8992
7892 293 12577 14020 13385 6800 3581 20025 0 4570 23545 13130 31385 20417 3973
6970 2740 18769 1549
5408 5162 13210
18394 6737 2993 24930 7141 290 5437 33805 4570 0 8125 4100 15185 8033 1241 1440
9050 29921 9061
26533 337 23985
38069 27112 8468 42505 6416 6865 16562 53530 23545 8125 0 2425 2260 1258 14216
5365 25925 46196 28836
16372 1546 11194
21370 15109 9061 24650 1249 4450 6485 33245 13130 4100 2425 0 3925 801 9805 980
12890 27637 15457
36297 3681 22849
39125 34164 18896 40625 5444 14625 18850 50450 31385 15185 2260 3925 0 1186
24500 8825 29125 42512 33832
24361 1417 15769
28729 22810 12250 31301 2290 7921 11240 40532 20417 8033 1258 801 1186 0 15298
3545 19433 33850 22930
3425 10205 18845
21625 6416 2628 30125 14152 1325 9050 39418 3973 1241 14216 9805 24500 15298 0
5297 11401 36324 10004
9344 3338 8506
15658 8585 6905 19890 2173 2210 3349 27925 6970 1440 5365 980 8825 3545 5297 0
7850 23465 9325
5098 21412 2180
1664 1657 22093 4520 9701 12580 1117 8425 2740 9050 25925 12890 29125 19433
11401 7850 0 7261 401
23993 42041 3737
2605 15140 51840 377 18532 35897 9866 394 18769 29921 46196 27637 42512 33850
36324 23465 7261 0 9800
3133 23701 4437
2465 580 21320 6397 12932 12517 2306 10474 1549 9061 28836 15457 33832 22930
10004 9325 401 9800 0
Objects (coordinates for German towns): Aachen (-57, 28),
Ausburg (54, -65), Braunschweig (46, 79), Bremen (8, 111), Essen (-36 ,52), Freigburg (-22, -76), Hamburg (34,
129), Hof (74, 6), Karlsruhe (-6, -41),
Kassel (21, 45), Kiel (37, 155),
Köln (-38, 35), Mannheim (-5, -24),
München (70, -74), Nürnberg (59, -26), Passau (114, -56), Regensburg (83, -41),
Saarbrücken (-40, -28), Würzburg (31, -12), Bielefeld (0, 71), Lübeck (50,
140), Münster (-20, 70)
Example data set (20 objects measured on 4 performance
drivers and 2 performance measures:
v1 v2 v3 v4 w1 w2
3 7 4 7 4 6
6 6 5 6 6 3
4 7 7 7 7 2
1 7 3 6 3 5
5 6 6 7 1 7
7 3 7 4 2 4
6 2 7 2 6 1
7 6 7 4 7 3
7 4 6 1 7 2
6 7 7 5 5 3
5 7 6 2 2 7
2 7 7 5 7 3
3 6 7 3 7 1
7 6 7 2 3 5
1 7 6 4 5 3
7 2 5 6 6 2
6 1 3 7 7 2
5 5 2 7 4 6
7 3 6 5 7 4
6 4 4 7 2 4
Example for Partitioning of Objects Based on a Single
Dataset but Using Multiple Criterion (see lipread consonants data)
Chapter 8
Example for demonstrating the iterative process:
0 5 4 5 1
3 0 1 7 6
4 7 0 8 3
3 1 0 0 6
7 2 5 2 0
Objects: (enumerated)
A 15 x 15 paired-comparison (tournament) matrix [Hubert, L., & Schultz, J.
(1975). Maximum likelihood paired comparison ranking and quadratic assignment.
Biometrika, 62, 655-659.]:
0 1 1 1 0 0 0 1 1 0 0 0 1 0 0
0 0 1 0 0 0 0 0 1 1 1 1 1 1 0
0 0 0 1 1 0 0 1 0 0 1 0 1 0 1
0 1 0 0 0 0 0 0 0 1 0 1 1 0 0
1 1 0 1 0 1 1 1 0 0 0 1 1 0 1
1 1 1 1 0 0 1 1 0 1 1 0 1 1 1
1 1 1 1 0 0 0 1 1 0 0 0 1 1 1
0 1 0 1 0 0 0 0 1 0 0 0 1 0 1
0 0 1 1 1 1 0 0 0 1 0 0 0 1 0
1 0 1 0 1 0 1 1 0 0 0 1 0 0 1
1 0 0 1 1 0 1 1 1 1 0 1 1 1 1
1 0 1 0 0 1 1 1 1 0 0 0 1 0 0
0 0 0 0 0 0 0 0 1 1 0 0 0 0 1
1 0 1 1 1 0 0 1 0 1 0 1 1 0 1
1 1 0 1 0 0 0 0 1 0 0 1 0 0 0
Objects: (enumerated)
Proportions of people rating severity
of criminal offenses [Thurstone, L. L. (1927). The
method of paired comparisons for social values. Journal of Abnormal and
Social Psychology, 31, 384-400.]:
0 323 338 211 238 244 245 212 760 318 222 191 256 822 419
677 0 415 242 281 285 253 274 863 365 207 182 245 925 589
662 585 0 260 226 321 348 254 917 563 215 144 349 944 716
789 757 740 0 515 556 485 534 970 743 385 385 587 947 785
762 719 774 485 0 593 605 580 981 856 333 322 478 981 769
756 715 679 444 407 0 540 488 947 804 303 284 532 963 756
755 747 652 515 395 460 0 350 958 752 305 248 474 977 774
788 726 746 466 420 512 650 0 951 819 343 320 534 966 820
240 137 83 30 19 53 42 49 0 83 30 34 79 441 181
682 635 437 257 144 196 248 181 917 0 170 106 288 902 595
778 793 785 615 667 697 695 657 970 830 0 348 648 970 848
809 818 855 615 678 716 752 680 966 894 652 0 702 981 886
744 755 651 413 522 467 526 466 921 712 352 298 0 951 767
178 75 56 53 19 37 23 34 559 98 30 19 49 0 76
581 411 284 215 231 244 226 180 819 405 152 114 233 924 0
Objects: Abortion, Adultery, Arson, Assault &
Battery, Burglary, Counterfeiting, Embezzlement, Forgery, Homicide, Kidnapping,
Larceny, Libel, Perjury, Rape, Seduction
Chapter 9
Indices of (dis)agreement for the Kabah
collection with rows and columns labeled by deposit identification.
[Will Robinson, A Method for Chronological Ordering, American Antiquity, Vol.
XVI, No. 4, April, 1951]:
200 108 68 96 99 116 105 112 106 108 119 128 145 154 156
149 151
108 200 95 76 93 84 92 87 94 109 93 108 116 118 140 122
136
68 95 200 47 55 65 54 62 50 72 65 74 90 100 109 98 95
96 76 47 200 56 58 50 49 32 60 66 79 93 87 100 124 101
99 93 55 56 200 53 34 33 46 41 54 50 61 53 67 69 86
116 84 65 58 53 200 36 31 34 43 45 46 58 68 71 81 69
105 92 54 50 34 36 200 19 30 32 33 36 52 60 62 61 63
112 87 62 49 33 31 19 200 32 42 33 40 49 57 60 57 67
106 94 50 32 46 34 30 32 200 47 54 51 71 71 72 80 79
108 109 72 60 41 43 32 42 47 200 53 41 51 52 57 66 61
119 93 65 66 54 45 33 33 54 53 200 47 48 57 73 48 43
128 108 74 79 50 46 36 40 51 41 47 200 22 46 36 51 48
145 116 90 93 61 58 52 49 71 51 48 22 200 29 28 34 39
154 118 100 87 53 68 60 57 71 52 57 46 29 200 25 48 55
156 140 109 100 67 71 62 60 72 57 73 36 28 25 200 55 61
149 122 98 124 69 81 61 57 80 66 48 51 34 48 55 200 46
151 136 95 101 86 69 63 67 79 61 43 48 39 55 61 46 200
Objects (Archeological deposits of a Kabah collection
made by Brainerd): II,
Chapter 10
Example to demonstrate the iterative process:
0 15 9 25
15 0 12 20
9 12 0 16
25 20 16 0
Objects: (enumerated)
Acoustically degraded English consonants (-18 db)
(Miller, G. A., & Nicely, P. E.
(1955). Analysis of perceptual confusions among some
English consonants. Journal of the Acoustical Society of
.000 .102 .083 .087 .095 .083 .053 .057 .061 .027 .064 .042 .045 .042 .061 .045
.073 .000 .095 .068 .068 .082 .064 .032 .045 .027 .077 .041 .059 .050 .041 .059
.083 .092 .000 .063 .058 .121 .050 .017 .046 .038 .050 .042 .067 .046 .071 .058
.101 .082 .101 .000 .049 .045 .037 .071 .075 .052 .060 .060 .056 .011 .049 .067
.071 .075 .075 .054 .000 .088 .050 .058 .083 .058 .096 .025 .058 .038 .050 .058
.071 .067 .091 .044 .071 .000 .067 .044 .095 .060 .060 .063 .044 .052 .067 .020
.060 .075 .101 .063 .049 .138 .000 .037 .078 .026 .075 .067 .034 .030 .060 .056
.045 .041 .090 .056 .071 .056 .045 .000 .075 .071 .090 .045 .056 .041 .067 .063
.054 .081 .061 .044 .051 .051 .047 .074 .000 .071 .084 .057 .061 .044 .051 .084
.036 .066 .095 .030 .059 .059 .049 .086 .099 .000 .059 .046 .053 .066 .079 .072
.040 .076 .080 .049 .031 .054 .040 .112 .063 .058 .000 .067 .085 .049 .054 .076
.074 .051 .046 .032 .028 .065 .046 .093 .079 .083 .069 .000 .079 .056 .083 .083
.074 .074 .061 .037 .053 .078 .029 .090 .057 .037 .086 .049 .000 .041 .090 .049
.036 .073 .077 .064 .055 .068 .032 .100 .082 .036 .068 .050 .068 .000 .082 .059
.079 .100 .063 .058 .058 .058 .033 .058 .063 .050 .054 .033 .046 .025 .000 .117
.047 .076 .085 .025 .038 .076 .038 .059 .059 .055 .038 .034 .042 .051 .140 .000
Degraded English consonants (-12 db)
.000 .207 .254 .086 .074 .023 .043 .008 .000 .008 .012 .012 .004 .020 .031 .020
.219 .000 .253 .068 .082 .075 .048 .007 .010 .003 .003 .007 .003 .003 .017 .003
.212 .178 .000 .093 .076 .068 .047 .017 .017 .004 .004 .008 .000 .000 .017 .008
.121 .086 .109 .000 .113 .059 .043 .012 .020 .000 .031 .031 .012 .000 .012 .000
.096 .081 .092 .232 .000 .099 .044 .022 .033 .011 .040 .033 .011 .007 .026 .007
.069 .065 .069 .142 .103 .000 .207 .013 .022 .026 .013 .004 .026 .009 .000 .004
.127 .177 .111 .078 .150 .139 .000 .006 .022 .028 .017 .000 .033 .017 .022 .011
.016 .008 .008 .070 .027 .027 .004 .000 .070 .070 .172 .098 .055 .023 .078 .039
.013 .000 .004 .017 .030 .017 .047 .078 .000 .151 .069 .103 .112 .060 .039 .052
.013 .004 .004 .004 .017 .021 .029 .083 .158 .000 .067 .121 .121 .158 .042 .038
.000 .004 .004 .051 .021 .017 .021 .157 .085 .097 .000 .068 .059 .017 .059 .038
.000 .004 .015 .063 .007 .011 .007 .198 .116 .093 .187 .000 .086 .019 .049 .022
.025 .004 .008 .008 .025 .059 .034 .097 .123 .114 .102 .081 .000 .110 .013 .025
.013 .009 .009 .004 .000 .026 .030 .030 .129 .099 .039 .030 .168 .000 .022 .060
.000 .009 .000 .000 .009 .009 .000 .097 .026 .053 .070 .097 .000 .009 .000 .527
.008 .000 .000 .008 .000 .008 .000 .015 .015 .046 .054 .008 .008 .070 .649 .000
Objects: p, t, k, f, theta, s, integral, b, d, g, v, delta,
z, 3, m, n
Chapter 11
Confusion matrices—rows labeled by responses and columns
labeled by stimulus—for auditory recognition of the digits 1—9.
[Morgan, B. J. T., Chambers, S. M.,
& Morton, J. (1973). Acoustic confusion of digits
in memory and recognition. Perception & Psychophysics, 14, 375-383.]
Acoustic Recognition of Digits (Male Voice)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
(1) 188 23 33 55 21 20 31 21 56
(2) 9 117 60 16 2 15 23 30 6
(3) 12 161 143 20 5 29 31 44 5
(4) 17 33 37 300 7 21 30 36 15
(5) 150 16 20 58 445 11 41 32 113
(6) 8 83 37 10 0 346 38 56 2
(7) 21 51 71 29 5 52 274 59 7
(8) 16 32 60 18 6 27 30 219 11
(9) 119 21 62 29 51 14 29 34 324
Acoustic Recognition of Digits (Female Voice)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
(1) 770 96 121 98 128 77 122 114 218
(2) 41 385 224 48 35 94 79 91 60
(3) 56 438 480 54 35 100 85 141 49
(4) 63 89 125 1023 67 63 100 177 61
(5) 254 66 81 119 937 64 83 59 324
(6) 30 110 98 38 16 805 110 242 22
(7) 41 189 168 64 26 191 807 154 51
(8) 64 87 139 58 28 111 109 513 48
(9) 242 96 119 60 292 44 65 55 720
Chapter 13
Example data set (20 objects measured on D = 6
Likert-scale variables).
v1 v2 v3 v4 v5 v6
2 2 3 4 4 2
7 2 2 6 1 7
2 3 6 5 5 5
1 5 5 7 1 6
7 3 6 1 6 2
6 5 7 7 6 3
7 5 6 4 7 2
2 5 5 6 1 5
7 4 2 7 2 6
1 7 3 7 5 2
2 5 2 2 5 3
6 5 6 6 7 3
2 2 6 1 4 5
6 7 2 2 4 7
1 5 3 4 7 2
6 4 1 4 1 7
7 1 7 3 5 3
2 2 6 2 6 5
7 5 1 2 5 6
2 5 3 2 2 3
Objects: (enumerated)
Example data set for 20 objects measured on D = 6 Likert-scale
variables:
Independent variables (predictors) dependent variable
v1 v2 v3 v4 v5 v6 y
----------------------------------------------
4 4 1 7 5 7 22
6 5 2 7 6 6 28
2 7 2 5 6 4 17
7 3 7 3 6 4 38
5 6 6 1 3 7 36
1 3 6 2 7 4 20
2 2 3 7 7 7 21
4 4 1 4 4 3 18
5 3 6 1 4 6 35
7 6 1 3 7 5 29
4 3 3 7 7 5 23
4 4 7 7 5 1 24
6 3 7 7 6 5 38
1 3 2 2 6 7 16
6 6 4 5 2 3 30
1 7 5 3 4 5 21
4 6 3 5 5 6 26
3 5 5 3 4 6 29
6 7 5 6 1 2 27
6 4 5 4 5 1 28
6 4 3 7 7 7 33
2 1 4 7 5 5 23
4 3 3 4 7 7 29
2 6 7 6 7 3 23
6 3 1 1 6 2 22
Chapter 14
Relationships in body dimensions:
See
http://www.amstat.org/publications/jse/v11n2/datasets.heinz.html
Objects (Independent variable class label descriptions):
Skeletal Measurements
v1 Biacromial
diameter (cm.)
v2 Biiliac diameter,
or "pelvic breadth" (cm.)
v3 Bitrochanteric
diameter (cm.)
v4 Chest depth
between spine and sternum (cm.)
v5 Chest diameter at
nipple level, mid-expiration (cm.)
v6 Elbow diameter,
sum of two el-bows (cm.)
v7 Wrist diameter,
sum of two wrists (cm.)
v8 Knee diameter,
sum of two knees (cm.)
v9 Ankle diameter,
sum of two an-kles (cm.)
Girth Measurements
v10 Shoulder girth
over deltoid mus-cles (cm.)
v11 Chest girth,
mid-expiration (cm.)
v12 Waist girth,
narrowest part of torso (cm.)
v13 Navel (or
"Abdominal") girth at umbilicus (cm.)
v14 Hip girth at
level of bitrochan-teric diameter (cm.)
v15 Thigh girth below
gluteal fold, average. of two thighs (cm.)
v16 Bicep girth, flexed,
average of right and left girths (cm.)
v17 Forearm girth,
extended, palm up, avg. of two forearms (cm.)
v18 Knee girth over
patella, flexed, avg. of two knees (cm.)
v19 Calf maximum
girth, average of right and left girths (cm.)
v20 Ankle minimum girth, average of right and left girths (cm.)