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Wludyka P.S., Copeland K.A.F., Nelson P.R. — The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions (ASA-SIAM Series on Statistics and Applied Probability)
Wludyka P.S., Copeland K.A.F., Nelson P.R. — The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions (ASA-SIAM Series on Statistics and Applied Probability)



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Название: The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions (ASA-SIAM Series on Statistics and Applied Probability)

Авторы: Wludyka P.S., Copeland K.A.F., Nelson P.R.

Аннотация:

The analysis of means (ANOM) is a graphical procedure used to quantify differences among treatment groups in a variety of experimental design and observational study situations. The ANOM decision chart allows one to easily draw conclusions and interpret results with respect to both statistical and practical significance. It is an excellent choice for multiple comparisons of means, rates, or proportions and can be used with both balanced and unbalanced data. Key advances in ANOM procedures that have appeared only in technical journals during the last 20 years are included in this first comprehensive modern treatment of the ANOM containing all of the needed information for practitioners to understand and apply ANOM. The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions contains examples from a wide variety of fields adapted from real-world applications and data with easy-to-follow, step-by-step instructions. It is front loaded, so potential ANOM users can find solutions to standard problems in the first five chapters. An appendix contains several SAS® examples showing the system’s ANOM capabilities and how SAS was used to produce selected ANOM decision charts in the book. Given these features, the lack of any other book on ANOM, and the recent inclusion of ANOM in SAS, this book will be a welcome addition to practitioners’ and statisticians’ bookshelves, where it will serve both as a primer and reference. Applied statisticians, particularly consulting statisticians, will find that the graphical aspect of ANOM makes it easy to convey results to nonstatisticians. Industrial, process, and quality engineers will find that the ANOM decisions charts offer an ideal interface with management and can be instrumental in selling research conclusions. The ANOM procedures are great for comparing the rates and proportions found in managed health care settings, and for comparing outcomes in multiarm studies done by statistical researchers in medicine.


Язык: en

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2005

Количество страниц: 247

Добавлена в каталог: 27.10.2010

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
"Dot" notation      13
"Hat" (^) notation      15
Analysis of means (ANOM), ANOMV      51—55
Analysis of means (ANOM), ANOMV, large samples      59
Analysis of means (ANOM), ANOMV, unbalanced      57
Analysis of means (ANOM), assumptions      14
Analysis of means (ANOM), balanced incomplete block designs      136—142
Analysis of means (ANOM), frequencies      29—32
Analysis of means (ANOM), frequencies, unbalanced      43—45
Analysis of means (ANOM), Graeco-Latin squares      134—137
Analysis of means (ANOM), heteroscedastic, multiway      156—162
Analysis of means (ANOM), heteroscedastic, one-way      153—156
Analysis of means (ANOM), higher-order layouts      115—127
Analysis of means (ANOM), interactions I = 2      81—84
Analysis of means (ANOM), interactions I > 2      84 86
Analysis of means (ANOM), Latin squares      131—134
Analysis of means (ANOM), normal data      16—19
Analysis of means (ANOM), normal data, unbalanced      35—40
Analysis of means (ANOM), proportions      27—28
Analysis of means (ANOM), proportions, unbalanced      40—42
Analysis of means (ANOM), randomized block      95—96
Analysis of means (ANOM), sample sizes      26
Analysis of means (ANOM), two-way with interaction      88—91
Analysis of means (ANOM), two-way with no interaction      86—88
Analysis of means (ANOM), two-way, unreplicated      91—95
Analysis of means (ANOM), Youden squares      142—144
Bacterial colony data      63 65 67
Bakir, S.T.      9 171 173 239
Balanced      11
Bernard, A.J.      65 163 171 239
Bishop, T.A.      154 156 239
Blocking      95—96
Blood acid data      36
Box, G.E.P.      137 239
c-section data      41
CAT scan rate data      45
Chemical yield data      83 89 90 118
Coating thickness data      68
Coffin, M.      241
Complete layout      71
Conover, W.J.      65 66 239
Copeland, K.A.F.      241
Cornell, J.A.      147 239
Cornfield, J.      239
Craig, C.D.      239
Customer lifetime data      15
Davies, O.L.      137 239
Davis, C.S.      242
Decision lines, ANOMV      52
Decision lines, ANOMV, unequal sample sizes      57
Decision lines, axial mixture designs      150
Decision lines, balanced incomplete block design      139
Decision lines, frequencies      30
Decision lines, frequencies, unbalanced      44
Decision lines, Graeco-Latin squares      136
Decision lines, HANOM      154
Decision lines, HANOM with interaction      158
Decision lines, HANOM, no interaction      158
Decision lines, higher-order layouts      115
Decision lines, interactions, I = 2      82
Decision lines, interactions, I > 2      85
Decision lines, Latin squares      132
Decision lines, normal data, unbalanced      35
Decision lines, proportions      27
Decision lines, proportions, unbalanced      41
Decision lines, two-way, no interaction      87
Decision lines, Youden squares      144
Dedewicz, E.J.      9
Design, balanced      35
Design, completely randomized      95
Design, randomized block      95—96
Deviation from target thickness data      163 166
Disk drive data      140 144
Distribution-free techniques      163
Distribution-free techniques, randomization tests, ANOM      165
Distribution-free techniques, randomization tests, ANOMV      165
Dudewicz, E.J.      59 153 154 156 239
Dye irritation data      95
Electronic component lifetime data      62
Emergency room visits data      113
Enrick, N.L.      9 240
Epidemiological data      5
Experiment, unreplicated      91
Factor      11
Factorial design      71
Farnum, N.R.      10 240
Fiberglass data      44
Fill weight data      26
Finer, H.      240
Fisher, R.A.      8 131 135 137 240
Fligner, M.A.      240
Freund, R.J.      10 240
Giani, G.      240
Greenhouse, S.W.      239
Gunst, R.F.      241
Halperin, M.      9 239
Hamada, M.      131 243
Hemoglobin data      72 76 83
Hess, J.L.      241
Hochberg, Y.      1 240
Homogeneity of variance      51
Hsu, J.C.      1 10 240 242
Hunter, J.S.      137 239
Hunter, W.G.      137 239
Injection depth data      20 26
Injection molding data      12 31
Insulation data      159
Interaction      72
Interaction, ANOM test      81—86
Interaction, ANOVA test      75—80
Interaction, heteroscedastic ANOVA test      157
Johnson, M.E.      239
Johnson, M.M.      239
Killeen, T.J.      240
Koch, G.G.      242
Kramer, C.Y.      1 240
Laplace, P.S.      8 240
Latin squares, orthogonal      134
Latin squares, standard      131
Layard, M.W.J.      240
Length of stay data      109
Levene, H.      65 241
Levin, J.R.      6 241
Logistic regression      112
Marascuilo, L.A.      241
Marscuilo, L.A.      6
Mason, R.L.      10 241
Mean square error ($MS_{e}$), axial mixture design      149
Mean square error ($MS_{e}$), balanced incomplete block design      139
Mean square error ($MS_{e}$), Graeco-Latin square      136
Mean square error ($MS_{e}$), Latin square      132
Mean square error ($MS_{e}$), one-way layout, balanced      18
Mean square error ($MS_{e}$), one-way layout, unbalanced      35
Mean square error ($MS_{e}$), two-way layout with replication      76
Mean square error ($MS_{e}$), two-way layout, unreplicated      92
Mean square error ($MS_{e}$), Youden square      144
Mishra, S.N.      59 239
Mixture design      147
Multiple comparison procedure      1
Nelson, L.S.      9 241
Nelson, P.R.      9 10 19 26 35 51 52 63 82 85 86 132 136 138 139 144 149 153 163 171 177 241
Neubauer, D.V.      242
Ohta, H.      9 241
On-time office visit data      11 28
Ott, E.R.      9 10 242
p-value      25
Paint drying data      2
Poisson Data, ANOM, unbalanced      43
Poisson regression      113
Powder application data      124
Practical significance      19
Prostate cancer knowledge study      104
Quality of life data      116
Radio ad data      106
Ramig, P.F.      242
Randomization      79
Repair cost data      92
Replicates      13
Replication      79 91
Residuals, balanced incomplete block design      139
Residuals, Graeco-Latin square      136
Residuals, Latin squares      132
Residuals, one-way model      15
Residuals, two-way model with replicates      75
Residuals, two-way model, unreplicated      91
Residuals, Youden square      144
Retrospective power      68
Ryan, T.P.      10 242
Sa, P.      171 243
Sales training data      120
Sample size      25
Scheffe, H.      242
Schilling, E.G.      9 242
Sheesley, J.H.      9 242
Significance, practical      19
Soong, W.C.      242
SPC data      21
Spring data      53
Standard square      131
Standardized test score data      42
Stokes, M.E.      112 113 242
Stream remediation data      38
Student's t-test      1
Tamhane, A.C.      1 240
Tourism/travel coupon data      6
Transformation, absolute deviations from the median      65
Transformation, log-normal      36
Tread wear data      132 136
Treatments      11
Triglicerides data      85 98
Tube seal data      28
Tube weight data      11 16 19
Tukey — Kramer procedure      1
Tukey, J.W.      1 242
Two-way layout      71
Unbalanced experiment      36
Urgent care arrival rate data      30
Vardeman, S.B.      10 242
Velcro force data      57
Wheeler, D.J.      10 242
Wilson, W.J.      10 240
Wludyka, P.S.      9 51 52 63 65 68 163 171 239 242
Wu, C.F.J.      131 243
Yates, F.      131 135 137 240
Yogurt data      53
Zalokar, J.      239
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