Experimental design and data analysis for biologists:
Regression, analysis of variance, correlation, graphical.
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2007
|
| Ausgabe: | 6. print. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Regression, analysis of variance, correlation, graphical. |
| Beschreibung: | XVII, 537 S. graph. Darst. |
| ISBN: | 0521009766 0521811287 |
Internformat
MARC
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| 245 | 1 | 0 | |a Experimental design and data analysis for biologists |c Gerry P. Quinn ; Michael J. Keough |
| 250 | |a 6. print. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2007 | |
| 300 | |a XVII, 537 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | 3 | |a Regression, analysis of variance, correlation, graphical. | |
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Datensatz im Suchindex
| _version_ | 1804137724415836160 |
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| adam_text | ,----------,J I CONTENTS PREFACE PAGE XV ILLNTRODUCTION 1 1.1 SCIENTIFIC
METHOD 1.1.1 PATTERN DESCRIPTION 1.1.2 MODELS 1.1.3 HYPOTHESES AND TESTS
1.1.4 ALTERNATIVES TO FALSIFICATION 1.1.5 ROLE OF STATISTICAL ANALYSIS
1.2 EXPERIMENTS AND OTHER TESTS 1.3 DATA, OBSERVATIONS AND VARIABLES 1.4
PROBABILITY 1.5 PROBABILITY DISTRIBUTIONS 1.5.1 DISTRIBUTIONS FOR
VARIABLES 1.5.2 DISTRIBUTIONS FOR STATISTICS 1 2 2 3 4 5 5 7 7 9 10 12
TL ESTIMATION 14 2.1 SAMPIES AND POPULATIONS 14 2.2 COMMON PARAMETERS
AND STATISTICS 15 2.2.1 CENTER (LOCATION) OF DISTRIBUTION 15 2.2.2
SPREAD OR VARIABILITY 16 2.3 STANDARD ERRORS AND CONFIDENCE INTERVALS
FOR THE MEAN 17 2.3.1 NORMAL DISTRIBUTIONS AND THE CENTRAL LIMIT THEOREM
17 2.3.2 STANDARD ERROR OFTHE SAMPLE MEAN 18 2.3.3 CONFIDENCE INTERVALS
FOR POPULATION MEAN 19 2.3.4 INTERPRETATION OF CONFIDENCE INTERVALS FOR
POPULATION MEAN 20 2.3.5 STANDARD ERRORS FOR OTHER STATISTICS 20 2.4
METHODS FOR ESTIMATING PARAMETERS 23 2.4.1 MAXIMUM LIKELIHOOD (ML) 23
2.4.2 ORDINARY LEAST SQUARES (OLS) 24 2.4.3 MLVS OLS ESTIMATION 25 2.5
RESAMPLING METHODS FOR ESTIMATION 25 2.5.1 BOOTSTRAP 25 2.5.2 [ACKKNIFE
26 2.6 BAYESIAN INFERENCE - ESTIMATION 27 2.6.1 BAYESIAN ESTIMATION 27
2.6.2 PRIOR KNOWLEDGE AND PROBABILITY 28 2.6.3 LIKELIHOOD FUNCTION 28
2.6.4 POSTERIOR PROBABILITY 28 2.6.5 EXAMPLES 29 2.6.6 OTHER COMMENTS 29
VI I CONTENTS 31 HYPOTHESIS TESTING 3.1 STATISTICAL HYPOTHESIS TESTING
3.1.1 CLASSICAL STATISTICAL HYPOTHESIS TESTING 3.1.2 ASSOCIATED
PROBABILITY AND TYPE I ERROR 3.1.3 HYPOTHESIS TESTS FOR A SINGLE
POPULATION 3.1.4 ONE- AND TWO-TAILED TESTS 3.1.5 HYPOTHESES FOR TWO
POPULATIONS 3.1.6 PARAMETRIE TESTS AND THEIR ASSUMPTIONS 3.2 DECISION
ERRORS 3.2.1 TYPE LAND II ERRORS 3.2.2 ASYMMETRY AND SCALABLE DECISION
CRITERIA 3.3 OTHER TESTING METHODS 3.3.1 ROBUST PARAMETRIC TESTS 3.3.2
RANDOMIZATION (PERMUTATION) TESTS 3.3.3 RANK-BASED NON-PARAMETRIC TESTS
3.4 MULTIPLE TESTING 3.4.1 THE PROBLEM 3.4.2 ADJUSTING SIGNIFICANCE
LEVELS ANDJOR PVALUES 3.5 COMBINING RESULTS FROM STATISTICAL TESTS 3.5.1
COMBINING P VALUES 3.5.2 META-ANALYSIS 3.6 CRITIQUE OF STATISTICAL
HYPOTHESIS TESTING 3.6.1 DEPENDENCE ON SAMPLE SIZE AND STOPPING RULES
3.6.2 SAMPLE SPACE - RELEVANCE OF DATA NOT OBSERVED 3.6.3 P VALUES AS
MEASURE OF EVIDENCE 3.6.4 NULL HYPOTHESIS ALWAYS FALSE 3.6.5 ARBITRARY
SIGNIFICANCE LEVELS 3.6.6 ALTERNATIVES TO STATISTICAL HYPOTHESIS TESTING
3.7 BAYESIAN HYPOTHESIS TESTING 4L GRAPHICAL EXPLORATION OF DATA 4.1
EXPLORATORY DATA ANALYSIS 4.1.1 EXPLORING SAMPLES 4.2 ANALYSIS WITH
GRAPHS 4.2.1 ASSUMPTIONS OFPARAMETRIC LINEAR MODELS 4.3 TRANSFORMING
DATA 4.3.1 TRANSFORMATIONS AND DISTRIBUTIONAL ASSUMPTIONS 4.3.2
TRANSFORMATIONS AND LINEARITY 4.3.3 TRANSFORMATIONS AND ADDITIVITY 4.4
STANDARDIZATIONS 4.5 OUTLIERS 4.6 CENSORED AND MISSING DATA 4.6.1
MISSING DATA 4.6.2 CENSORED (TRUNCATED) DATA 4.7 GENERAL ISSUES AND
HINTS FOR ANALYSIS 4.7.1 GENERAL ISSUES 32 32 32 34 35 37 37 39 42 42 44
45 45 45 46 48 48 49 50 50 50 51 51 52 53 53 53 53 54 58 58 58 62 62 64
65 67 67 67 68 68 68 69 71 71 51 CORRELATION AND REGRESSION 5.1
CORRELATION ANALYSIS 5.1.1 PARAMETRIE CORRELATION MODEL 5.1.2 ROBUST
CORRELATION 5.1.3 PARAMETRIE AND NON-PARAMETRIC CONFIDENCE REGIONS 5.2
LINEAR MODELS 5.3 LINEAR REGRESSION ANALYSIS 5.3.1 SIMPLE (BIVARIATE)
LINEAR REGRESSION 5.3.2 LINEAR MODEL FOR REGRESSION 5.3.3 ESTIMATING
MODEL PARAMETERS 5.3.4 ANALYSIS OFVARIANCE 5.3.5 NULL HYPOTHESES IN
REGRESSION 5.3.6 COMPARING REGRESSION MODELS 5.3.7 VARIANCE EXPLAINED
5.3.8 ASSUMPTIONS OFREGRESSION ANALYSIS 5.3.9 REGRESSION DIAGNOSTICS
5.3.10 DIAGNOSTIC GRAPHICS 5.3.11 TRANSFORMATIONS 5.3.12 REGRESSION
THROUGH THE ORIGIN 5.3.13 WEIGHTED LEAST SQUARES 5.3.14 X RANDOM (MODEL
11 REGRESSION) 5.3.15 ROBUST REGRESSION 5.4 RELATIONSHIP BETWEEN
REGRESSION AND CORRELATION 5.5 SMOOTHING 5.5.1 RUNNING MEANS 5.5.2
LO(W)ESS 5.5.3 SPLINES 5.5.4 KERNEIS 5.5.5 OTHER ISSUES 5.6 POWER OF
TESTS IN CORRELATION AND REGRESSION 5.7 GENERAL ISSUES AND HINTS FOR
ANALYSIS 5.7.1 GENERAL ISSUES 5.7.2 HINTS FOR ANALYSIS 61 MULTIPLE AND
COMPLEX REGRESSION 6.1 MULTIPLE LINEAR REGRESSION ANALYSIS 6.1.1
MULTIPLE LINEAR REGRESSION MODEL 6.1.2 ESTIMATING MODEL PARAMETERS 6.1.3
ANALYSIS OFVARIANCE 6.1.4 NULL HYPOTHESES AND MODEL COMPARISONS 6.1.5
VARIANCE EXPLAINED 6.1.6 WHICH PREDICTORS ARE IMPORTANT? 6.1.7
ASSUMPTIONS OF MULTIPLE REGRESSION 6.1.8 REGRESSION DIAGNOSTICS 6.1.9
DIAGNOSTIC GRAPHICS 6.1.10 TRANSFORMATIONS 6.1.11 COLLINEARITY 72 72 72
76 76 77 78 78 80 85 88 89 90 91 92 94 96 98 98 99 100 104 106 107 107
107 108 108 109 109 110 110 110 111 111 114 119 119 121 122 122 124 125
125 127 127 VIII I CONTENTS 6.1.12 INTERACTIONS IN MULTIPLE REGRESSION
6.1.13 POLYNOMIAL REGRESSION 6.1.14 INDICATOR (DUMMY)VARIABLES 6.1.15
FINDING THE BEST REGRESSION MODEL 6.1.16 HIERARCHICAL PARTITIONING
6.1.17 OTHER ISSUES IN MULTIPLE LINEAR REGRESSION 6.2 REGRESSION TREES
6.3 PATH ANALYSIS AND STRUCTURAL EQUATION MODELING 6.4 NONLINEAR MODELS
6.5 SMOOTHING AND RESPONSE SURFACES 6.6 GENERAL ISSUES AND HINTS FOR
ANALYSIS 6.6.1 GENERAL ISSUES 6.6.2 HINTS FOR ANALYSIS 71 DESIGN AND
POWER ANALYSIS 7.1 SAMPLING 7.1.1 SAMPLING DESIGNS 7.1.2 SIZE OF SAMPLE
7.2 EXPERIMENTAL DESIGN 7.2.1 REPLICATION 7.2.2 CONTROLS 7.2.3
RANDOMIZATION 7.2.4 INDEPENDENCE 7.2.5 REDUCING UNEXPLAINED VARIANCE 7.3
POWER ANALYSIS 7.3.1 USING POWER TO PLAN EXPERIMENTS (A PRIORI POWER
ANALYSIS) 7.3.2 POST HOC POWER CALCULATION 7.3.3 THEEFFECT SIZE 7.3.4
USING POWER ANALYSES 7.4 GENERAL ISSUES AND HINTS FOR ANALYSIS 7.4.1
GENERAL ISSUES 7.4.2 HINTS FOR ANALYSIS 81 COMPARING GROUPS OR
TREATMENTS - ANALYSIS OF VARIANCE 8.1 SINGLE FACTOR (ONEWAY) DESIGNS
8.1.1 TYPES OFPREDICTOR VARIABLES (FAETORS) 8.12 LINEAR MODEL FOR SINGLE
FACTOR ANALYSES 8.1.3 ANALYSIS OFVARIANCE 8.1.4 NULL HYPOTHESES 8.1.5
COMPARING ANAVA MODELS 8.1.6 UNEQUAL SAMPLE SIZES (UNBALANCED DESIGNS)
8.2 FACTOR EFFECTS 8.2.1 RANDOM EFFEETS:VARIANCE COMPONENTS 8.2.2 FIXED
EFFEETS 8.3 ASSUMPTIONS 8.3.1 NORMALITY 8.3.2 VARIANCE HOMOGENEITY 8.3.3
INDEPENDENCE 130 133 135 137 141 142 143 145 150 152 153 153 154 155 155
155 157 157 158 160 161 163 164 164 166 168 168 170 171 171 172 173 173
176 178 184 186 187 187 188 188 190 191 192 193 193 8.4 ANOVA
DIAGNOSTICS 194 8.5 ROBUST ANOVA 195 8.5.1 TESTS WITH HETEROGENEOUS
VARIANCES 195 8.5.2 RANK-BASED ( NON-PARAMETRIC ) TESTS 195 8.5.3
RANDOMIZATION TESTS 196 8.6 SPECIFIC COMPARISONS OFMEANS 196 8.6.1
PLANNED COMPARISONS OR CONTRASTS 197 8.6.2 UNPLANNED PAIRWISE
COMPARISONS 199 8.6.3 SPECIFIC CONTRASTS VERSUS UNPLANNED PAIRWISE
COMPARISONS 201 8.7 TESTS FOR TRENDS 202 8.8 TESTING EQUALITY OF GROUP
VARIANCES 203 8.9 POWER OF SINGLE FACTOR ANOVA 204 8.10 GENERAL ISSUES
AND HINTS FOR ANALYSIS 206 8.10.1 GENERAL ISSUES 206 8.10.2 HINTS FOR
ANALYSIS 206 9L MULTIFACTOR ANALYSIS OF VARIANCE 208 9.1 NESTED
(HIERARCHIEAL) DESIGNS 208 9.1.1 LINEAR MODELS FOR NESTED ANALYSES 210
9.1.2 ANALYSIS OFVARIANCE 214 9.1.3 NULL HYPOTHESES 215 9.1.4 UNEQUAL
SAMPLE SIZES (UNBALANCED DESIGNS) 216 9.1.5 COMPARING ANOVA MODELS 216
9.1.6 FACTOR EFFECTS IN NESTED MODELS 216 9.1.7 ASSUMPTIONS FOR NESTED
MODELS 218 9.1.8 SPECIFIC COMPARISONS FOR NESTED DESIGNS 219 9.1.9 MORE
COMPLEX DESIGNS 219 9.1.10 DESIGN AND POWER 219 9.2 FACTORIAL DESIGNS
221 9.2.1 LINEAR MODELS FOR FACTORIAL DESIGNS 225 9.2.2 ANALYSIS
OFVARIANCE 230 9.2.3 NULL HYPOTHESES 232 9.2.4 WHAT ARE MAIN EFFECTS AND
INTERACTIONS REALLY MEASURING? 237 9.2.5 COMPARING ANOVA MODELS 241
9.2.6 UNBALANCED DESIGNS 241 9.2.7 FACTOR EFFECTS 247 9.2.8 ASSUMPTIONS
249 9.2.9 ROBUST FACTORIALANOVAS 250 9.2.10 SPECIFIC COMPARISONS ON MAIN
EFFECTS 250 9.2.11 INTERPRETING INTERACTIONS 251 9.2.12 MORE COMPLEX
DESIGNS 255 9.2.13 POWER AND DESIGN IN FACTORIAL ANOVA 259 9.3 POOLING
IN MULTIFACTOR DESIGNS 260 9.4 RELATIONSHIP BETWEEN FACTORIAL AND NESTED
DESIGNS 261 9.5 GENERAL ISSUES AND HINTS FOR ANALYSIS 261 9.5.1 GENERAL
ISSUES 261 9.5.2 HINTS FOR ANALYSIS 261 X CONTENTS 10 RANDOMIZED BLOCKS
AND SIMPLE REPEATED MEASURES: UNREPLICATED TWO FACTOR DESIGNS 262 10.1
UNREPLICATED TWO FACTOR EXPERIMENTAL DESIGNS 10.1.1 RANDORNIZED COMPLETE
BLOCK (RCB) DESIGNS 10.1.2 REPEATED MEASURES (RM) DESIGNS 10.2 ANALYZING
RCB AND RM DESIGNS 10.2.1 LINEAR MODELS FOR RCB AND RM ANALYSES 10.2.2
ANALYSIS OFVARIANCE 10.2.3 NULL HYPOTHESES 10.2.4 COMPARING ANAVA MODELS
10.3 INTERACTIONS IN RCB AND RMMODELS 10.3.1 IMPORTANCE OFTREATMENT BY
BLOCK INTERACTIONS 10.3.2 CHECKS FOR INTERACTION IN UNREPLICATED DESIGNS
10.4 ASSURNPTIONS 10.4.1 NORMALITY, INDEPENDENCE OF ERRORS 10.4.2
VARIANCES AND COVARIANCES - SPHERICITY 10.4.3 RECOMMENDED STRATEGY 10.5
ROBUST RCB AND RM ANALYSES 10.6 SPECIFIC COMPARISONS 10.7 EFFICIENCY
OFBLOCKING (TO BLOCK OR NOT TO BLOCK?) 10.8 TIME AS A BLOCKING FACTOR
10.9 ANALYSIS OFUNBALANCED RCB DESIGNS 10.10 POWER OFRCB OR SIMPLE
RMDESIGNS 10.11 MORE CORNPLEX BLOCK DESIGNS 10.11.1 FACTORIAL RANDOMIZED
BLOCK DESIGNS 10.11.2 INCOMPLETE BLOCK DESIGNS 10.11.3 LATIN SQUARE
DESIGNS 10.11.4 CROSSOVER DESIGNS 10.12 GENERALIZED RANDOMIZED BLOCK
DESIGNS 10.13 RCB AND RM DESIGNS AND STATISTICAL SOFTWARE 10.14 GENERAL
ISSUES AND HINTS FOR ANALYSIS 10.14.1 GENERAL ISSUES 10.14.2 HINTS FOR
ANALYSIS 262 262 265 268 268 272 273 274 274 274 277 280 280 280 284 284
285 285 287 287 289 290 290 292 292 296 298 298 299 299 300 I I
SPLIT-PLOT AND REPEATED MEASURES DESIGNS: PARTLY NESTED ANALYSES OF
VARIANCE 301 11.1 PARTLY NESTED DESIGNS 11.1.1 SPLIT-PLOT DESIGNS 11.1.2
REPEATED MEASURES DESIGNS 11.1.3 REASONS FOR USING THESE DESIGNS 11.2
ANALYZING PARTLY NESTED DESIGNS 11.2.1 LINEAR MODELS FOR PARTLY NESTED
ANALYSES 112.2 ANALYSIS OFVARIANCE 11.2.3 NULL HYPOTHESES 11.2.4
COMPARING ANAVAMODELS 11.3 ASSUMPTIONS 11.3.1 BETWEEN PLOTSFSUBJECTS
11.3.2 WITHIN PLOTSFSUBJECTS AND MULTISAMPLE SPHERICITY 301 301 305 309
309 310 313 315 318 318 318 318 11.4 ROBUST PARTLY NESTED ANALYSES 320
11.5 SPECIFIC COMPARISONS 320 11.5.1 MAIN EFFECTS 320 11.5.2
INTERACTIONS 321 11.5.3 PROFILE (I.E. TREND) ANALYSIS 321 11.6 ANALYSIS
OF UNBALANCED PARTLY NESTED DESIGNS 322 11.7 POWER FOR PARTLY NESTED
DESIGNS 323 11.8 MORE COMPLEX DESIGNS 323 11.8.1 ADDITIONAL
BETWEEN-PLOTSJSUBJECTS FACTORS 324 11.8.2 ADDITIONAL
WITHIN-PLOTSJSUBJECTS FACTORS 329 11.8.3 ADDITIONAL
BETWEEN-PLOTSJSUBJECTS AND WITHIN-PLOTSJ SUBJECTS FACTORS 332 11.8.4
GENERAL COMMENTS ABOUT COMPLEX DESIGNS 335 11.9 PARTLY NESTED DESIGNS
AND STATISTICAL SOFTWARE 335 11.10 GENERAL ISSUES AND HINTS FOR ANALYSIS
337 11.10.1 GENERAL ISSUES 337 11.10.2 HINTS FOR INDIVIDUAL ANALYSES 337
121 ANALYSES OF COVARIANCE 339 12.1 SINGLE FACTOR ANALYSIS OF COVARIANCE
(ANCOVA) 339 12.1.1 LINEAR MODELS FOR ANALYSIS OF COVARIANCE 342 12.1.2
ANALYSIS OF(CO)VARIANCE 347 12.1.3 NULL HYPOTHESES 347 12.1.4 COMPARING
ANCOVA MODELS 348 12.2 ASSUMPTIONS OF ANCOVA 348 12.2.1 LINEARITY 348
12.2.2 COVARIATE VALUES SIMILAR ACROSS GROUPS 349 12.2.3 FIXED COVARIATE
(X) 349 12.3 HOMOGENEOUS SLOPES 349 12.3.1 TESTING FOR HOMOGENEOUS
WITHIN-GROUP REGRESSION SLOPES 349 12.3.2 DEALING WITH HETEROGENEOUS
WITHIN-GROUP REGRESSION SLOPES 350 12.3.3 COMPARING REGRESSION LINES 352
12.4 ROBUST ANCOVA 352 12.5 UNEQUAL SAMPLE SIZES (UNBALANCED DESIGNS)
353 12.6 SPECIFIC COMPARISONS OF ADJUSTED MEANS 353 12.6.1 PLANNED
CONTRASTS 353 12.6.2 UNPLANNED COMPARISONS 353 12.7 MORE COMPLEX DESIGNS
353 12.7.1 DESIGNS WITH TWO OR MORE COVARIATES 353 12.7.2 FACTORIAL
DESIGNS 354 12.7.3 NESTED DESIGNS WITH ONE COVARIATE 355 12.7.4 PARTLY
NESTED MODELS WITH ONE COVARIATE 356 12.8 GENERAL ISSUES AND HINTS FOR
ANALYSIS 357 12.8.1 GENERAL ISSUES 357 12.8.2 HINTS FOR ANALYSIS 358 XII
I CONTENTS L3L GENERALIZED LINEAR MODELS AND LOGISTIC REGRESSION 359
13.1 GENERALIZED LINEAR MODELS 359 13.2 LOGISTIC REGRESSION 360 13.2.1
SIMPLE LOGISTIC REGRESSION 360 13.2.2 MULTIPLE LOGISTIC REGRESSION 365
13.2.3 CATEGORICAL PREDICTORS 368 13.2.4 ASSUMPTIONS OFLOGISTIC
REGRESSION 368 13.2.5 GOODNESS-OF-FIT AND RESIDUALS 368 13.2.6 MODEL
DIAGNOSTICS 370 13.2.7 MODEL SELECTION 370 13.2.8 SOFTWARE FOR LOGISTIC
REGRESSION 371 13.3 POISSON REGRESSION 371 13.4 GENERALIZED ADDITIVE
MODELS 372 13.5 MODELS FOR CORRELATED DATA 375 13.5.1 MULTI-LEVEL
(RANDOM EFFECTS) MODELS 376 13.5.2 GENERALIZED ESTIMATING EQUATIONS 377
13.6 GENERAL ISSUES AND HINTS FOR ANALYSIS 378 13.6.1 GENERAL ISSUES 378
13.6.2 HINTS FOR ANALYSIS 379 141 ANALYZING FREQUENCIES 380 14.1 SINGLE
VARIABLE GCODNESS-OF-FIT TESTS 381 14.2 CONTINGENCY TABLES 381 14.2.1
TWO WAY TABLES 381 14.2.2 THREE WAY TABLES 388 14.3 LOG-LINEAR MODELS
393 14.3.1 TWO WAY TABLES 394 14.3.2 LOG-LINEAR MODELS FOR THREE WAY
TABLES 395 14.3.3 MORE COMPLEX TABLES 400 14.4 GENERAL ISSUES AND HINTS
FOR ANALYSIS 400 14.4.1 GENERAL ISSUES 400 14.4.2 HINTS FOR ANALYSIS 400
LSL NTRODUCTION TO MULTIVARIATE ANALYSES 401 15.1 MULTIVARIATE DATA 401
15.2 DISTRIBUTIONS AND ASSOCIATIONS 402 15.3 LINEAR COMBINATIONS,
EIGENVECTORS AND EIGENVALUES 405 15.3.1 LINEAR COMBINATIONS OFVARIABLES
405 15.3.2 EIGENVALUES 405 15.3.3 EIGENVECTORS 406 15.3.4 DERIVATION OF
COMPONENTS 409 15.4 MULTIVARIATE DISTANCE AND DISSIMILARITY MEASURES 409
15.4.1 DISSIMILARITY MEASURES FOR CONTINUOUS VARIABLES 412 15.4.2
DISSIMILARITY MEASURES FOR DICHOTOMOUS (BINARY) VARIABLES 413 15.4.3
GENERAL DISSIMILARITY MEASURES FOR MIXED VARIABLES 413 15.4.4 COMPARISON
OF DISSIMILARITY MEASURES 414 15.5 COMPARING DISTANCE ANDJOR
DISSIMILARITY MATRICES 414 15.6 DATA STANDARDIZATION 415 15.7
STANDARDIZATION, ASSOCIATION AND DISSIMILARITY 417 15.8 MULTIVARIATE
GRAPHICS 417 15.9 SCREENING MULTIVARIATE DATA SETS 418 15.9.1
MULTIVARIATE OUTLIERS 419 15.9.2 MISSING OBSERVATIONS 419 15.10 GENERAL
ISSUES AND HINTS FOR ANALYSIS 423 15.10.1 GENERAL ISSUES 423 15.10.2
HINTS FOR ANALYSIS 424 161 MULTIVARIATE ANALYSIS OF VARIANCE AND
DISCRIMINANT ANALYSIS 425 16.1 MULTIVARIATE ANALYSIS OFVARIANCE (MANOVA)
425 16.1.1 SINGLE FACTOR MANOVA 426 16.1.2 SPECI:FICCOMPARISONS 432
16.1.3 RELATIVE IMPORTANCE OF EACH RESPONSE VARIABLE 432 16.1.4
ASSUMPTIONS OFMANOVA 433 16.1.5 ROBUST MANOVA 434 16.1.6 MORE COMPLEX
DESIGNS 434 16.2 DISCRIMINANT FUNCTION ANALYSIS 435 16.2.1 DESCRIPTION
AND HYPOTHESIS TESTING 437 16.2.2 CLASSIFICATION AND PREDICTION 439
16.2.3 ASSUMPTIONS OF DISCRIMINANT FUNCTION ANALYSIS 441 16.2.4 MORE
COMPLEX DESIGNS 441 16.3 MANOVA VS DISCRIMINANT FUNCTION ANALYSIS 441
16.4 GENERAL ISSUES AND HINTS FOR ANALYSIS 441 16.4.1 GENERAL ISSUES 441
16.4.2 HINTS FOR ANALYSIS 441 171 PRINCIPAL COMPONENTS AND
CORRESPONDENCE ANALYSIS 443 17.1 PRINCIPAL COMPONENTS ANALYSIS 17.1.1
DERIVING COMPONENTS 17.1.2 WHICH ASSOCIATION MATRIX TO USE? 17.1.3
INTERPRETING THE COMPONENTS 17.1.4 ROTATION OF COMPONENTS 17.1.5 HOW
MANY COMPONENTS TO RETAIN? 17.1.6 ASSUMPTIONS 17.1.7 ROBUST PCA 17.1.8
GRAPHICAL REPRESENTATIONS 17.1.9 OTHER USES OF COMPONENTS 17.2 FACTOR
ANALYSIS 17.3 CORRESPONDENCE ANALYSIS 17.3.1 MECHANICS 17.3.2 SCALING
ANDJOINT PLOTS 17.3.3 RECIPROCAL AVERAGING 17.3.4 USE OFCA WITH
ECOLOGICAL DATA 17.3.5 DETRENDING 17.4 CANONICAL CORRELATION ANALYSIS
443 447 450 451 451 452 453 454 454 456 458 459 459 461 462 462 463 463
XIV I CONTENTS 17.5 REDUNDANCY ANALYSIS 17.6 CANONICAL CORRESPONDENCE
ANALYSIS 17.7 CONSTRAINED AND PARTIAL ORDINATION 17.8 GENERAL ISSUES
AND HINTS FOR ANALYSIS 17.8.1 GENERAL ISSUES 17.8.2 HINTS FOR ANALYSIS
L8L MULTIDIMENSIONAL SCALING AND CLUSTER ANALYSIS 18.1 MULTIDIMENSIONAL
SCALING 18.1.1 CLASSICAL SCALING - PRINCIPAL COORDINATES ANALYSIS (PCOA)
18.1.2 ENHANCED MULTIDIMENSIONAL SCALING 18.1.3 DISSIMILARITIES AND
TESTING HYPOTHESES ABOUT GROUPS OF OBJEETS 18.1.4 RELATING MDS TO
ORIGINAL VARIABLES 18.1.5 RELATING MDS TO COVARIATES 18.2 CLASSIFICATION
18.2.1 CLUSTER ANALYSIS 18.3 SCALING(ORDINATION) AND CLUSTERING FOR
BIOLOGICAL DATA 18.4 GENERAL ISSUES AND HINTS FOR ANALYSIS 18.4.1
GENERAL ISSUES 18.4.2 HINTS FOR ANALYSIS I PRESENTATION OF RESULTS 19.1
PRESENTATION OF ANALYSES 19.1.1 LINEAR MODELS 19.1.2 OTHER ANALYSES 19.2
LAYOUTOFTABLES 19.3 DISPLAYING SUMMARIES OFTHE DATA 19.3.1 BAR GRAPH
19.3.2 LINE GRAPH (CATEGORY PLOT) 19.3.3 SCATTERPLOTS 19.3.4 PIE CHARTS
19.4 ERROR BARS 19.4.1 ALTERNATIVE APPROACHES 19.5 ORAL PRESENTATIONS
19.5.1 SUDES,COMPUTERS, OR OVERHEADS? 19.5.2 GRAPHICS PACKAGES 19.5.3
WORKING WITH COLOR 19.5.4 SCANNED IMAGES 19.5.5 INFORMATION CONTENT 19.6
GENERAL ISSUES AND HINTS REFERENCES INDEX 466 467 468 471 471 471 473
473 474 476 482 487 487 488 488 491 493 493 493 494 494 494 497 497 498
500 502 502 503 504 506 507 507 508 508 509 509 510 511 527
|
| adam_txt |
,----------,J I CONTENTS PREFACE PAGE XV ILLNTRODUCTION 1 1.1 SCIENTIFIC
METHOD 1.1.1 PATTERN DESCRIPTION 1.1.2 MODELS 1.1.3 HYPOTHESES AND TESTS
1.1.4 ALTERNATIVES TO FALSIFICATION 1.1.5 ROLE OF STATISTICAL ANALYSIS
1.2 EXPERIMENTS AND OTHER TESTS 1.3 DATA, OBSERVATIONS AND VARIABLES 1.4
PROBABILITY 1.5 PROBABILITY DISTRIBUTIONS 1.5.1 DISTRIBUTIONS FOR
VARIABLES 1.5.2 DISTRIBUTIONS FOR STATISTICS 1 2 2 3 4 5 5 7 7 9 10 12
TL ESTIMATION 14 2.1 SAMPIES AND POPULATIONS 14 2.2 COMMON PARAMETERS
AND STATISTICS 15 2.2.1 CENTER (LOCATION) OF DISTRIBUTION 15 2.2.2
SPREAD OR VARIABILITY 16 2.3 STANDARD ERRORS AND CONFIDENCE INTERVALS
FOR THE MEAN 17 2.3.1 NORMAL DISTRIBUTIONS AND THE CENTRAL LIMIT THEOREM
17 2.3.2 STANDARD ERROR OFTHE SAMPLE MEAN 18 2.3.3 CONFIDENCE INTERVALS
FOR POPULATION MEAN 19 2.3.4 INTERPRETATION OF CONFIDENCE INTERVALS FOR
POPULATION MEAN 20 2.3.5 STANDARD ERRORS FOR OTHER STATISTICS 20 2.4
METHODS FOR ESTIMATING PARAMETERS 23 2.4.1 MAXIMUM LIKELIHOOD (ML) 23
2.4.2 ORDINARY LEAST SQUARES (OLS) 24 2.4.3 MLVS OLS ESTIMATION 25 2.5
RESAMPLING METHODS FOR ESTIMATION 25 2.5.1 BOOTSTRAP 25 2.5.2 [ACKKNIFE
26 2.6 BAYESIAN INFERENCE - ESTIMATION 27 2.6.1 BAYESIAN ESTIMATION 27
2.6.2 PRIOR KNOWLEDGE AND PROBABILITY 28 2.6.3 LIKELIHOOD FUNCTION 28
2.6.4 POSTERIOR PROBABILITY 28 2.6.5 EXAMPLES 29 2.6.6 OTHER COMMENTS 29
VI I CONTENTS 31 HYPOTHESIS TESTING 3.1 STATISTICAL HYPOTHESIS TESTING
3.1.1 CLASSICAL STATISTICAL HYPOTHESIS TESTING 3.1.2 ASSOCIATED
PROBABILITY AND TYPE I ERROR 3.1.3 HYPOTHESIS TESTS FOR A SINGLE
POPULATION 3.1.4 ONE- AND TWO-TAILED TESTS 3.1.5 HYPOTHESES FOR TWO
POPULATIONS 3.1.6 PARAMETRIE TESTS AND THEIR ASSUMPTIONS 3.2 DECISION
ERRORS 3.2.1 TYPE LAND II ERRORS 3.2.2 ASYMMETRY AND SCALABLE DECISION
CRITERIA 3.3 OTHER TESTING METHODS 3.3.1 ROBUST PARAMETRIC TESTS 3.3.2
RANDOMIZATION (PERMUTATION) TESTS 3.3.3 RANK-BASED NON-PARAMETRIC TESTS
3.4 MULTIPLE TESTING 3.4.1 THE PROBLEM 3.4.2 ADJUSTING SIGNIFICANCE
LEVELS ANDJOR PVALUES 3.5 COMBINING RESULTS FROM STATISTICAL TESTS 3.5.1
COMBINING P VALUES 3.5.2 META-ANALYSIS 3.6 CRITIQUE OF STATISTICAL
HYPOTHESIS TESTING 3.6.1 DEPENDENCE ON SAMPLE SIZE AND STOPPING RULES
3.6.2 SAMPLE SPACE - RELEVANCE OF DATA NOT OBSERVED 3.6.3 P VALUES AS
MEASURE OF EVIDENCE 3.6.4 NULL HYPOTHESIS ALWAYS FALSE 3.6.5 ARBITRARY
SIGNIFICANCE LEVELS 3.6.6 ALTERNATIVES TO STATISTICAL HYPOTHESIS TESTING
3.7 BAYESIAN HYPOTHESIS TESTING 4L GRAPHICAL EXPLORATION OF DATA 4.1
EXPLORATORY DATA ANALYSIS 4.1.1 EXPLORING SAMPLES 4.2 ANALYSIS WITH
GRAPHS 4.2.1 ASSUMPTIONS OFPARAMETRIC LINEAR MODELS 4.3 TRANSFORMING
DATA 4.3.1 TRANSFORMATIONS AND DISTRIBUTIONAL ASSUMPTIONS 4.3.2
TRANSFORMATIONS AND LINEARITY 4.3.3 TRANSFORMATIONS AND ADDITIVITY 4.4
STANDARDIZATIONS 4.5 OUTLIERS 4.6 CENSORED AND MISSING DATA 4.6.1
MISSING DATA 4.6.2 CENSORED (TRUNCATED) DATA 4.7 GENERAL ISSUES AND
HINTS FOR ANALYSIS 4.7.1 GENERAL ISSUES 32 32 32 34 35 37 37 39 42 42 44
45 45 45 46 48 48 49 50 50 50 51 51 52 53 53 53 53 54 58 58 58 62 62 64
65 67 67 67 68 68 68 69 71 71 51 CORRELATION AND REGRESSION 5.1
CORRELATION ANALYSIS 5.1.1 PARAMETRIE CORRELATION MODEL 5.1.2 ROBUST
CORRELATION 5.1.3 PARAMETRIE AND NON-PARAMETRIC CONFIDENCE REGIONS 5.2
LINEAR MODELS 5.3 LINEAR REGRESSION ANALYSIS 5.3.1 SIMPLE (BIVARIATE)
LINEAR REGRESSION 5.3.2 LINEAR MODEL FOR REGRESSION 5.3.3 ESTIMATING
MODEL PARAMETERS 5.3.4 ANALYSIS OFVARIANCE 5.3.5 NULL HYPOTHESES IN
REGRESSION 5.3.6 COMPARING REGRESSION MODELS 5.3.7 VARIANCE EXPLAINED
5.3.8 ASSUMPTIONS OFREGRESSION ANALYSIS 5.3.9 REGRESSION DIAGNOSTICS
5.3.10 DIAGNOSTIC GRAPHICS 5.3.11 TRANSFORMATIONS 5.3.12 REGRESSION
THROUGH THE ORIGIN 5.3.13 WEIGHTED LEAST SQUARES 5.3.14 X RANDOM (MODEL
11 REGRESSION) 5.3.15 ROBUST REGRESSION 5.4 RELATIONSHIP BETWEEN
REGRESSION AND CORRELATION 5.5 SMOOTHING 5.5.1 RUNNING MEANS 5.5.2
LO(W)ESS 5.5.3 SPLINES 5.5.4 KERNEIS 5.5.5 OTHER ISSUES 5.6 POWER OF
TESTS IN CORRELATION AND REGRESSION 5.7 GENERAL ISSUES AND HINTS FOR
ANALYSIS 5.7.1 GENERAL ISSUES 5.7.2 HINTS FOR ANALYSIS "61 MULTIPLE AND
COMPLEX REGRESSION 6.1 MULTIPLE LINEAR REGRESSION ANALYSIS 6.1.1
MULTIPLE LINEAR REGRESSION MODEL 6.1.2 ESTIMATING MODEL PARAMETERS 6.1.3
ANALYSIS OFVARIANCE 6.1.4 NULL HYPOTHESES AND MODEL COMPARISONS 6.1.5
VARIANCE EXPLAINED 6.1.6 WHICH PREDICTORS ARE IMPORTANT? 6.1.7
ASSUMPTIONS OF MULTIPLE REGRESSION 6.1.8 REGRESSION DIAGNOSTICS 6.1.9
DIAGNOSTIC GRAPHICS 6.1.10 TRANSFORMATIONS 6.1.11 COLLINEARITY 72 72 72
76 76 77 78 78 80 85 88 89 90 91 92 94 96 98 98 99 100 104 106 107 107
107 108 108 109 109 110 110 110 111 111 114 119 119 121 122 122 124 125
125 127 127 VIII I CONTENTS 6.1.12 INTERACTIONS IN MULTIPLE REGRESSION
6.1.13 POLYNOMIAL REGRESSION 6.1.14 INDICATOR (DUMMY)VARIABLES 6.1.15
FINDING THE "BEST" REGRESSION MODEL 6.1.16 HIERARCHICAL PARTITIONING
6.1.17 OTHER ISSUES IN MULTIPLE LINEAR REGRESSION 6.2 REGRESSION TREES
6.3 PATH ANALYSIS AND STRUCTURAL EQUATION MODELING 6.4 NONLINEAR MODELS
6.5 SMOOTHING AND RESPONSE SURFACES 6.6 GENERAL ISSUES AND HINTS FOR
ANALYSIS 6.6.1 GENERAL ISSUES 6.6.2 HINTS FOR ANALYSIS 71 DESIGN AND
POWER ANALYSIS 7.1 SAMPLING 7.1.1 SAMPLING DESIGNS 7.1.2 SIZE OF SAMPLE
7.2 EXPERIMENTAL DESIGN 7.2.1 REPLICATION 7.2.2 CONTROLS 7.2.3
RANDOMIZATION 7.2.4 INDEPENDENCE 7.2.5 REDUCING UNEXPLAINED VARIANCE 7.3
POWER ANALYSIS 7.3.1 USING POWER TO PLAN EXPERIMENTS (A PRIORI POWER
ANALYSIS) 7.3.2 POST HOC POWER CALCULATION 7.3.3 THEEFFECT SIZE 7.3.4
USING POWER ANALYSES 7.4 GENERAL ISSUES AND HINTS FOR ANALYSIS 7.4.1
GENERAL ISSUES 7.4.2 HINTS FOR ANALYSIS 81 COMPARING GROUPS OR
TREATMENTS - ANALYSIS OF VARIANCE 8.1 SINGLE FACTOR (ONEWAY) DESIGNS
8.1.1 TYPES OFPREDICTOR VARIABLES (FAETORS) 8.12 LINEAR MODEL FOR SINGLE
FACTOR ANALYSES 8.1.3 ANALYSIS OFVARIANCE 8.1.4 NULL HYPOTHESES 8.1.5
COMPARING ANAVA MODELS 8.1.6 UNEQUAL SAMPLE SIZES (UNBALANCED DESIGNS)
8.2 FACTOR EFFECTS 8.2.1 RANDOM EFFEETS:VARIANCE COMPONENTS 8.2.2 FIXED
EFFEETS 8.3 ASSUMPTIONS 8.3.1 NORMALITY 8.3.2 VARIANCE HOMOGENEITY 8.3.3
INDEPENDENCE 130 133 135 137 141 142 143 145 150 152 153 153 154 155 155
155 157 157 158 160 161 163 164 164 166 168 168 170 171 171 172 173 173
176 178 184 186 187 187 188 188 190 191 192 193 193 8.4 ANOVA
DIAGNOSTICS 194 8.5 ROBUST ANOVA 195 8.5.1 TESTS WITH HETEROGENEOUS
VARIANCES 195 8.5.2 RANK-BASED ("NON-PARAMETRIC") TESTS 195 8.5.3
RANDOMIZATION TESTS 196 8.6 SPECIFIC COMPARISONS OFMEANS 196 8.6.1
PLANNED COMPARISONS OR CONTRASTS 197 8.6.2 UNPLANNED PAIRWISE
COMPARISONS 199 8.6.3 SPECIFIC CONTRASTS VERSUS UNPLANNED PAIRWISE
COMPARISONS 201 8.7 TESTS FOR TRENDS 202 8.8 TESTING EQUALITY OF GROUP
VARIANCES 203 8.9 POWER OF SINGLE FACTOR ANOVA 204 8.10 GENERAL ISSUES
AND HINTS FOR ANALYSIS 206 8.10.1 GENERAL ISSUES 206 8.10.2 HINTS FOR
ANALYSIS 206 9L MULTIFACTOR ANALYSIS OF VARIANCE 208 9.1 NESTED
(HIERARCHIEAL) DESIGNS 208 9.1.1 LINEAR MODELS FOR NESTED ANALYSES 210
9.1.2 ANALYSIS OFVARIANCE 214 9.1.3 NULL HYPOTHESES 215 9.1.4 UNEQUAL
SAMPLE SIZES (UNBALANCED DESIGNS) 216 9.1.5 COMPARING ANOVA MODELS 216
9.1.6 FACTOR EFFECTS IN NESTED MODELS 216 9.1.7 ASSUMPTIONS FOR NESTED
MODELS 218 9.1.8 SPECIFIC COMPARISONS FOR NESTED DESIGNS 219 9.1.9 MORE
COMPLEX DESIGNS 219 9.1.10 DESIGN AND POWER 219 9.2 FACTORIAL DESIGNS
221 9.2.1 LINEAR MODELS FOR FACTORIAL DESIGNS 225 9.2.2 ANALYSIS
OFVARIANCE 230 9.2.3 NULL HYPOTHESES 232 9.2.4 WHAT ARE MAIN EFFECTS AND
INTERACTIONS REALLY MEASURING? 237 9.2.5 COMPARING ANOVA MODELS 241
9.2.6 UNBALANCED DESIGNS 241 9.2.7 FACTOR EFFECTS 247 9.2.8 ASSUMPTIONS
249 9.2.9 ROBUST FACTORIALANOVAS 250 9.2.10 SPECIFIC COMPARISONS ON MAIN
EFFECTS 250 9.2.11 INTERPRETING INTERACTIONS 251 9.2.12 MORE COMPLEX
DESIGNS 255 9.2.13 POWER AND DESIGN IN FACTORIAL ANOVA 259 9.3 POOLING
IN MULTIFACTOR DESIGNS 260 9.4 RELATIONSHIP BETWEEN FACTORIAL AND NESTED
DESIGNS 261 9.5 GENERAL ISSUES AND HINTS FOR ANALYSIS 261 9.5.1 GENERAL
ISSUES 261 9.5.2 HINTS FOR ANALYSIS 261 X CONTENTS 10 RANDOMIZED BLOCKS
AND SIMPLE REPEATED MEASURES: UNREPLICATED TWO FACTOR DESIGNS 262 10.1
UNREPLICATED TWO FACTOR EXPERIMENTAL DESIGNS 10.1.1 RANDORNIZED COMPLETE
BLOCK (RCB) DESIGNS 10.1.2 REPEATED MEASURES (RM) DESIGNS 10.2 ANALYZING
RCB AND RM DESIGNS 10.2.1 LINEAR MODELS FOR RCB AND RM ANALYSES 10.2.2
ANALYSIS OFVARIANCE 10.2.3 NULL HYPOTHESES 10.2.4 COMPARING ANAVA MODELS
10.3 INTERACTIONS IN RCB AND RMMODELS 10.3.1 IMPORTANCE OFTREATMENT BY
BLOCK INTERACTIONS 10.3.2 CHECKS FOR INTERACTION IN UNREPLICATED DESIGNS
10.4 ASSURNPTIONS 10.4.1 NORMALITY, INDEPENDENCE OF ERRORS 10.4.2
VARIANCES AND COVARIANCES - SPHERICITY 10.4.3 RECOMMENDED STRATEGY 10.5
ROBUST RCB AND RM ANALYSES 10.6 SPECIFIC COMPARISONS 10.7 EFFICIENCY
OFBLOCKING (TO BLOCK OR NOT TO BLOCK?) 10.8 TIME AS A BLOCKING FACTOR
10.9 ANALYSIS OFUNBALANCED RCB DESIGNS 10.10 POWER OFRCB OR SIMPLE
RMDESIGNS 10.11 MORE CORNPLEX BLOCK DESIGNS 10.11.1 FACTORIAL RANDOMIZED
BLOCK DESIGNS 10.11.2 INCOMPLETE BLOCK DESIGNS 10.11.3 LATIN SQUARE
DESIGNS 10.11.4 CROSSOVER DESIGNS 10.12 GENERALIZED RANDOMIZED BLOCK
DESIGNS 10.13 RCB AND RM DESIGNS AND STATISTICAL SOFTWARE 10.14 GENERAL
ISSUES AND HINTS FOR ANALYSIS 10.14.1 GENERAL ISSUES 10.14.2 HINTS FOR
ANALYSIS 262 262 265 268 268 272 273 274 274 274 277 280 280 280 284 284
285 285 287 287 289 290 290 292 292 296 298 298 299 299 300 I I
SPLIT-PLOT AND REPEATED MEASURES DESIGNS: PARTLY NESTED ANALYSES OF
VARIANCE 301 11.1 PARTLY NESTED DESIGNS 11.1.1 SPLIT-PLOT DESIGNS 11.1.2
REPEATED MEASURES DESIGNS 11.1.3 REASONS FOR USING THESE DESIGNS 11.2
ANALYZING PARTLY NESTED DESIGNS 11.2.1 LINEAR MODELS FOR PARTLY NESTED
ANALYSES 112.2 ANALYSIS OFVARIANCE 11.2.3 NULL HYPOTHESES 11.2.4
COMPARING ANAVAMODELS 11.3 ASSUMPTIONS 11.3.1 BETWEEN PLOTSFSUBJECTS
11.3.2 WITHIN PLOTSFSUBJECTS AND MULTISAMPLE SPHERICITY 301 301 305 309
309 310 313 315 318 318 318 318 11.4 ROBUST PARTLY NESTED ANALYSES 320
11.5 SPECIFIC COMPARISONS 320 11.5.1 MAIN EFFECTS 320 11.5.2
INTERACTIONS 321 11.5.3 PROFILE (I.E. TREND) ANALYSIS 321 11.6 ANALYSIS
OF UNBALANCED PARTLY NESTED DESIGNS 322 11.7 POWER FOR PARTLY NESTED
DESIGNS 323 11.8 MORE COMPLEX DESIGNS 323 11.8.1 ADDITIONAL
BETWEEN-PLOTSJSUBJECTS FACTORS 324 11.8.2 ADDITIONAL
WITHIN-PLOTSJSUBJECTS FACTORS 329 11.8.3 ADDITIONAL
BETWEEN-PLOTSJSUBJECTS AND WITHIN-PLOTSJ SUBJECTS FACTORS 332 11.8.4
GENERAL COMMENTS ABOUT COMPLEX DESIGNS 335 11.9 PARTLY NESTED DESIGNS
AND STATISTICAL SOFTWARE 335 11.10 GENERAL ISSUES AND HINTS FOR ANALYSIS
337 11.10.1 GENERAL ISSUES 337 11.10.2 HINTS FOR INDIVIDUAL ANALYSES 337
121 ANALYSES OF COVARIANCE 339 12.1 SINGLE FACTOR ANALYSIS OF COVARIANCE
(ANCOVA) 339 12.1.1 LINEAR MODELS FOR ANALYSIS OF COVARIANCE 342 12.1.2
ANALYSIS OF(CO)VARIANCE 347 12.1.3 NULL HYPOTHESES 347 12.1.4 COMPARING
ANCOVA MODELS 348 12.2 ASSUMPTIONS OF ANCOVA 348 12.2.1 LINEARITY 348
12.2.2 COVARIATE VALUES SIMILAR ACROSS GROUPS 349 12.2.3 FIXED COVARIATE
(X) 349 12.3 HOMOGENEOUS SLOPES 349 12.3.1 TESTING FOR HOMOGENEOUS
WITHIN-GROUP REGRESSION SLOPES 349 12.3.2 DEALING WITH HETEROGENEOUS
WITHIN-GROUP REGRESSION SLOPES 350 12.3.3 COMPARING REGRESSION LINES 352
12.4 ROBUST ANCOVA 352 12.5 UNEQUAL SAMPLE SIZES (UNBALANCED DESIGNS)
353 12.6 SPECIFIC COMPARISONS OF ADJUSTED MEANS 353 12.6.1 PLANNED
CONTRASTS 353 12.6.2 UNPLANNED COMPARISONS 353 12.7 MORE COMPLEX DESIGNS
353 12.7.1 DESIGNS WITH TWO OR MORE COVARIATES 353 12.7.2 FACTORIAL
DESIGNS 354 12.7.3 NESTED DESIGNS WITH ONE COVARIATE 355 12.7.4 PARTLY
NESTED MODELS WITH ONE COVARIATE 356 12.8 GENERAL ISSUES AND HINTS FOR
ANALYSIS 357 12.8.1 GENERAL ISSUES 357 12.8.2 HINTS FOR ANALYSIS 358 XII
I CONTENTS L3L GENERALIZED LINEAR MODELS AND LOGISTIC REGRESSION 359
13.1 GENERALIZED LINEAR MODELS 359 13.2 LOGISTIC REGRESSION 360 13.2.1
SIMPLE LOGISTIC REGRESSION 360 13.2.2 MULTIPLE LOGISTIC REGRESSION 365
13.2.3 CATEGORICAL PREDICTORS 368 13.2.4 ASSUMPTIONS OFLOGISTIC
REGRESSION 368 13.2.5 GOODNESS-OF-FIT AND RESIDUALS 368 13.2.6 MODEL
DIAGNOSTICS 370 13.2.7 MODEL SELECTION 370 13.2.8 SOFTWARE FOR LOGISTIC
REGRESSION 371 13.3 POISSON REGRESSION 371 13.4 GENERALIZED ADDITIVE
MODELS 372 13.5 MODELS FOR CORRELATED DATA 375 13.5.1 MULTI-LEVEL
(RANDOM EFFECTS) MODELS 376 13.5.2 GENERALIZED ESTIMATING EQUATIONS 377
13.6 GENERAL ISSUES AND HINTS FOR ANALYSIS 378 13.6.1 GENERAL ISSUES 378
13.6.2 HINTS FOR ANALYSIS 379 141 ANALYZING FREQUENCIES 380 14.1 SINGLE
VARIABLE GCODNESS-OF-FIT TESTS 381 14.2 CONTINGENCY TABLES 381 14.2.1
TWO WAY TABLES 381 14.2.2 THREE WAY TABLES 388 14.3 LOG-LINEAR MODELS
393 14.3.1 TWO WAY TABLES 394 14.3.2 LOG-LINEAR MODELS FOR THREE WAY
TABLES 395 14.3.3 MORE COMPLEX TABLES 400 14.4 GENERAL ISSUES AND HINTS
FOR ANALYSIS 400 14.4.1 GENERAL ISSUES 400 14.4.2 HINTS FOR ANALYSIS 400
LSL'NTRODUCTION TO MULTIVARIATE ANALYSES 401 15.1 MULTIVARIATE DATA 401
15.2 DISTRIBUTIONS AND ASSOCIATIONS 402 15.3 LINEAR COMBINATIONS,
EIGENVECTORS AND EIGENVALUES 405 15.3.1 LINEAR COMBINATIONS OFVARIABLES
405 15.3.2 EIGENVALUES 405 15.3.3 EIGENVECTORS 406 15.3.4 DERIVATION OF
COMPONENTS 409 15.4 MULTIVARIATE DISTANCE AND DISSIMILARITY MEASURES 409
15.4.1 DISSIMILARITY MEASURES FOR CONTINUOUS VARIABLES 412 15.4.2
DISSIMILARITY MEASURES FOR DICHOTOMOUS (BINARY) VARIABLES 413 15.4.3
GENERAL DISSIMILARITY MEASURES FOR MIXED VARIABLES 413 15.4.4 COMPARISON
OF DISSIMILARITY MEASURES 414 15.5 COMPARING DISTANCE ANDJOR
DISSIMILARITY MATRICES 414 15.6 DATA STANDARDIZATION 415 15.7
STANDARDIZATION, ASSOCIATION AND DISSIMILARITY 417 15.8 MULTIVARIATE
GRAPHICS 417 15.9 SCREENING MULTIVARIATE DATA SETS 418 15.9.1
MULTIVARIATE OUTLIERS 419 15.9.2 MISSING OBSERVATIONS 419 15.10 GENERAL
ISSUES AND HINTS FOR ANALYSIS 423 15.10.1 GENERAL ISSUES 423 15.10.2
HINTS FOR ANALYSIS 424 161 MULTIVARIATE ANALYSIS OF VARIANCE AND
DISCRIMINANT ANALYSIS 425 16.1 MULTIVARIATE ANALYSIS OFVARIANCE (MANOVA)
425 16.1.1 SINGLE FACTOR MANOVA 426 16.1.2 SPECI:FICCOMPARISONS 432
16.1.3 RELATIVE IMPORTANCE OF EACH RESPONSE VARIABLE 432 16.1.4
ASSUMPTIONS OFMANOVA 433 16.1.5 ROBUST MANOVA 434 16.1.6 MORE COMPLEX
DESIGNS 434 16.2 DISCRIMINANT FUNCTION ANALYSIS 435 16.2.1 DESCRIPTION
AND HYPOTHESIS TESTING 437 16.2.2 CLASSIFICATION AND PREDICTION 439
16.2.3 ASSUMPTIONS OF DISCRIMINANT FUNCTION ANALYSIS 441 16.2.4 MORE
COMPLEX DESIGNS 441 16.3 MANOVA VS DISCRIMINANT FUNCTION ANALYSIS 441
16.4 GENERAL ISSUES AND HINTS FOR ANALYSIS 441 16.4.1 GENERAL ISSUES 441
16.4.2 HINTS FOR ANALYSIS 441 171 PRINCIPAL COMPONENTS AND
CORRESPONDENCE ANALYSIS 443 17.1 PRINCIPAL COMPONENTS ANALYSIS 17.1.1
DERIVING COMPONENTS 17.1.2 WHICH ASSOCIATION MATRIX TO USE? 17.1.3
INTERPRETING THE COMPONENTS 17.1.4 ROTATION OF COMPONENTS 17.1.5 HOW
MANY COMPONENTS TO RETAIN? 17.1.6 ASSUMPTIONS 17.1.7 ROBUST PCA 17.1.8
GRAPHICAL REPRESENTATIONS 17.1.9 OTHER USES OF COMPONENTS 17.2 FACTOR
ANALYSIS 17.3 CORRESPONDENCE ANALYSIS 17.3.1 MECHANICS 17.3.2 SCALING
ANDJOINT PLOTS 17.3.3 RECIPROCAL AVERAGING 17.3.4 USE OFCA WITH
ECOLOGICAL DATA 17.3.5 DETRENDING 17.4 CANONICAL CORRELATION ANALYSIS
443 447 450 451 451 452 453 454 454 456 458 459 459 461 462 462 463 463
XIV I CONTENTS 17.5 REDUNDANCY ANALYSIS 17.6 CANONICAL CORRESPONDENCE
ANALYSIS 17.7 CONSTRAINED AND PARTIAL "ORDINATION" 17.8 GENERAL ISSUES
AND HINTS FOR ANALYSIS 17.8.1 GENERAL ISSUES 17.8.2 HINTS FOR ANALYSIS
L8L MULTIDIMENSIONAL SCALING AND CLUSTER ANALYSIS 18.1 MULTIDIMENSIONAL
SCALING 18.1.1 CLASSICAL SCALING - PRINCIPAL COORDINATES ANALYSIS (PCOA)
18.1.2 ENHANCED MULTIDIMENSIONAL SCALING 18.1.3 DISSIMILARITIES AND
TESTING HYPOTHESES ABOUT GROUPS OF OBJEETS 18.1.4 RELATING MDS TO
ORIGINAL VARIABLES 18.1.5 RELATING MDS TO COVARIATES 18.2 CLASSIFICATION
18.2.1 CLUSTER ANALYSIS 18.3 SCALING(ORDINATION) AND CLUSTERING FOR
BIOLOGICAL DATA 18.4 GENERAL ISSUES AND HINTS FOR ANALYSIS 18.4.1
GENERAL ISSUES 18.4.2 HINTS FOR ANALYSIS I PRESENTATION OF RESULTS 19.1
PRESENTATION OF ANALYSES 19.1.1 LINEAR MODELS 19.1.2 OTHER ANALYSES 19.2
LAYOUTOFTABLES 19.3 DISPLAYING SUMMARIES OFTHE DATA 19.3.1 BAR GRAPH
19.3.2 LINE GRAPH (CATEGORY PLOT) 19.3.3 SCATTERPLOTS 19.3.4 PIE CHARTS
19.4 ERROR BARS 19.4.1 ALTERNATIVE APPROACHES 19.5 ORAL PRESENTATIONS
19.5.1 SUDES,COMPUTERS, OR OVERHEADS? 19.5.2 GRAPHICS PACKAGES 19.5.3
WORKING WITH COLOR 19.5.4 SCANNED IMAGES 19.5.5 INFORMATION CONTENT 19.6
GENERAL ISSUES AND HINTS REFERENCES INDEX 466 467 468 471 471 471 473
473 474 476 482 487 487 488 488 491 493 493 493 494 494 494 497 497 498
500 502 502 503 504 506 507 507 508 508 509 509 510 511 527 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Quinn, Gerry P. 1956- Keough, Michael J. |
| author_GND | (DE-588)13930052X |
| author_facet | Quinn, Gerry P. 1956- Keough, Michael J. |
| author_role | aut aut |
| author_sort | Quinn, Gerry P. 1956- |
| author_variant | g p q gp gpq m j k mj mjk |
| building | Verbundindex |
| bvnumber | BV023361468 |
| callnumber-first | Q - Science |
| callnumber-label | QH323 |
| callnumber-raw | QH323.5 |
| callnumber-search | QH323.5 |
| callnumber-sort | QH 3323.5 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | WC 7600 |
| classification_tum | BIO 110f MAT 620f |
| ctrlnum | (OCoLC)254682269 (DE-599)BVBBV023361468 |
| discipline | Biologie Informatik Mathematik |
| discipline_str_mv | Biologie Informatik Mathematik |
| edition | 6. print. |
| format | Book |
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| id | DE-604.BV023361468 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:09:21Z |
| indexdate | 2024-07-09T21:16:51Z |
| institution | BVB |
| isbn | 0521009766 0521811287 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016544917 |
| oclc_num | 254682269 |
| open_access_boolean | |
| owner | DE-20 DE-188 |
| owner_facet | DE-20 DE-188 |
| physical | XVII, 537 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | Quinn, Gerry P. 1956- Verfasser (DE-588)13930052X aut Experimental design and data analysis for biologists Gerry P. Quinn ; Michael J. Keough 6. print. Cambridge [u.a.] Cambridge Univ. Press 2007 XVII, 537 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Regression, analysis of variance, correlation, graphical. Biologie (DE-588)4006851-1 gnd rswk-swf Biostatistik (DE-588)4729990-3 gnd rswk-swf Biometrie (DE-588)4124925-2 gnd rswk-swf Versuchsplanung (DE-588)4078859-3 gnd rswk-swf Biostatistik (DE-588)4729990-3 s DE-604 Biologie (DE-588)4006851-1 s Versuchsplanung (DE-588)4078859-3 s Biometrie (DE-588)4124925-2 s 1\p DE-604 Keough, Michael J. Verfasser aut OEBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016544917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Quinn, Gerry P. 1956- Keough, Michael J. Experimental design and data analysis for biologists Biologie (DE-588)4006851-1 gnd Biostatistik (DE-588)4729990-3 gnd Biometrie (DE-588)4124925-2 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| subject_GND | (DE-588)4006851-1 (DE-588)4729990-3 (DE-588)4124925-2 (DE-588)4078859-3 |
| title | Experimental design and data analysis for biologists |
| title_auth | Experimental design and data analysis for biologists |
| title_exact_search | Experimental design and data analysis for biologists |
| title_exact_search_txtP | Experimental design and data analysis for biologists |
| title_full | Experimental design and data analysis for biologists Gerry P. Quinn ; Michael J. Keough |
| title_fullStr | Experimental design and data analysis for biologists Gerry P. Quinn ; Michael J. Keough |
| title_full_unstemmed | Experimental design and data analysis for biologists Gerry P. Quinn ; Michael J. Keough |
| title_short | Experimental design and data analysis for biologists |
| title_sort | experimental design and data analysis for biologists |
| topic | Biologie (DE-588)4006851-1 gnd Biostatistik (DE-588)4729990-3 gnd Biometrie (DE-588)4124925-2 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| topic_facet | Biologie Biostatistik Biometrie Versuchsplanung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016544917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT quinngerryp experimentaldesignanddataanalysisforbiologists AT keoughmichaelj experimentaldesignanddataanalysisforbiologists |