Applied regression including computing and graphics:
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| Format: | Buch |
| Sprache: | English |
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New York [u.a.]
Wiley
1999
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| Schriftenreihe: | Wiley series in probability and statistics
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| Beschreibung: | XXVI, 593 S. graph. Darst. |
| ISBN: | 047131711X |
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| 245 | 1 | 0 | |a Applied regression including computing and graphics |c R. Dennis Cook ; Sanford Weisberg |
| 264 | 1 | |a New York [u.a.] |b Wiley |c 1999 | |
| 300 | |a XXVI, 593 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and statistics | |
| 650 | 0 | 7 | |a Regressionsanalyse |0 (DE-588)4129903-6 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Regressionsanalyse |0 (DE-588)4129903-6 |D s |
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| adam_text | IMAGE 1
APPLIED REGRESSION
INCLUDING COMPUTING AND GRAPHICS
R. DENNIS COOK SANFORD WEISBERG THE UNIVERSITY OF MINNESOTA ST. PAUL,
MINNESOTA
A WILEY-INTERSCIENCE PUBLICATION JOHN WILEY & SONS, INC. NEW YORK *
CHICHESTER * WEINHEIM * BRISBANE * SINGAPORE * TORONTO
IMAGE 2
CONTENTS
PREFACE
PARTI INTRODUCTION
1 LOOKING FORWARD AND BACK
1.1 EXAMPLE: HAYSTACK DATA, 3 1.2 EXAMPLE: BLUEGILL DATA, 5 1.3 LOADING
DATA INTO ARE, 6 1.4 NUMERICAL SUMMARIES, 7
1.4.1 DISPLAY SUMMARIES, 7 1.4.2 COMMAND LINE, 9 1.4.3 DISPLAYING DATA,
10 1.4.4 SAVING OUTPUT TO A FILE AND PRINTING, 10 1.5 GRAPHICAL
SUMMARIES, 10
1.5.1 HISTOGRAMS, 10 1.5.2 BOXPLOTS, 12 1.6 BRINGING IN THE POPULATION,
13 1.6.1 THE DENSITY FUNCTION, 14
1.6.2 NORMAL DISTRIBUTION, 14 1.6.3 COMPUTING NORMAL QUANTILES, 16 1.6.4
COMPUTING NORMAL PROBABILITIES, 16 1.6.5 BOXPLOTS OF NORMAL DATA, 17
1.6.6 THE SAMPLING DISTRIBUTION OF THE MEAN, 18 1.7 INFERENCE, 20 1.7.1
SAMPLE MEAN, 20
1.7.2 CONFIDENCE INTERVAL FOR THE MEAN, 21 1.7.3 PROBABILITY OF A RECORD
BLUEGILL, 21
IMAGE 3
VIII CONTENTS
1.8 COMPLEMENTS, 22
PROBLEMS, 22
2 INTRODUCTION TO REGRESSION 27
2.1 USING BOXPLOTS TO STUDY LENGTH | AGE, 28 2.2 USING A SCATTERPLOT TO
STUDY LENGTH | AGE, 31 2.3 MOUSE MODES, 31 2.3.1 SHOW COORDINATES MOUSE
MODE, 32
2.3.2 SLICING MODE, 32 2.3.3 BRUSHING MODE, 33 2.4 CHARACTERIZING LENGTH
| AGE, 33 2.5 MEAN AND VARIANCE FUNCTIONS, 35
2.5.1 MEAN FUNCTION, 35 2.5.2 VARIANCE FUNCTION, 36 2.6 HIGHLIGHTS, 37
2.7 COMPLEMENTS, 37 PROBLEMS, 37
3 INTRODUCTION TO SMOOTHING 40
3.1 SLICING A SCATTERPLOT, 40 3.2 ESTIMATING E(Y X) BY SLICING, 42 3.3
ESTIMATING E(Y | JC) BY SMOOTHING, 42 3.4 CHECKING A THEORY, 45
3.5 BOXPLOTS, 45 3.6 SNOW GEESE, 48 3.6.1 SNOW GOOSE REGRESSION, 49
3.6.2 MEAN FUNCTION, 51
3.6.3 VARIANCE FUNCTION, 51 3.7 COMPLEMENTS, 53 PROBLEMS, 53
4 BIVARIATE DISTRIBUTIONS 56
4.1 GENERAL BIVARIATE DISTRIBUTIONS, 56 4.1.1 BIVARIATE DENSITIES, 58
4.1.2 CONNECTING WITH REGRESSION, 59 4.1.3 INDEPENDENCE, 59 4.1.4
COVARIANCE, 60 4.1.5 CORRELATION COEFFICIENT, 62
IMAGE 4
CONTENTS
IX
4.2 BIVARIATE NORMAL DISTRIBUTION, 63 4.2.1 CORRELATION COEFFICIENT IN
NORMAL POPULATIONS, 64 4.2.2 CORRELATION COEFFICIENT IN NON-NORMAL
POPULATIONS, 68
4.3 REGRESSION IN BIVARIATE NORMAL POPULATIONS, 69 4.3.1 MEAN FUNCTION,
70 4.3.2 MEAN FUNCTION IN STANDARDIZED VARIABLES, 70 4.3.3 MEAN FUNCTION
AS A STRAIGHT LINE, 72 4.3.4 VARIANCE FUNCTION, 74 4.4 SMOOTHING
BIVARIATE NORMAL DATA, 76 4.5 COMPLEMENTS, 78
4.5.1 CONFIDENCE INTERVAL FOR A CORRELATION, 78 4.5.2 REFERENCES, 78
PROBLEMS, 78
5 TWO-DIMENSIONAL PLOTS 81
5.1 ASPECT RATIO AND FOCUSING, 81 5.2 POWER TRANSFORMATIONS, 84 5.3
THINKING ABOUT POWER TRANSFORMATIONS, 86
5.4 LOG TRANSFORMATIONS, 87 5.5 SHOWING LABELS AND COORDINATES, 88 5.6
LINKING PLOTS, 89 5.7 POINT SYMBOLS AND COLORS, 90
5.8 BRUSHING, 90 5.9 NAMELISTS, 90 5.10 PROBABILITY PLOTS, 90 5.11
COMPLEMENTS, 92 PROBLEMS, 93
PART II TOOLS 95
6 SIMPLE LINEAR REGRESSION 97
6.1 SIMPLE LINEAR REGRESSION, 98 6.2 LEAST SQUARES ESTIMATION, 101 6.2.1
NOTATION, 101 6.2.2 THE LEAST SQUARES CRITERION, 102
6.2.3 ORDINARY LEAST SQUARES ESTIMATORS, 105 6.2.4 MORE ON SAMPLE
CORRELATION, 106
IMAGE 5
X
CONTENTS
6.2.5 SOME PROPERTIES OF LEAST SQUARES ESTIMATES, 106
6.2.6 ESTIMATING THE COMMON VARIANCE, A 2 , 107 6.2.7 SUMMARY, 107 6.3
USING ARE, 107
6.3.1 INTERPRETING THE INTERCEPT, 110 6.4 INFERENCE, 112 6.4.1
INFERENCES ABOUT PARAMETERS, 112
6.4.2 ESTIMATING POPULATION MEANS, 115 6.4.3 PREDICTION, 117 6.5 FORBES
EXPERIMENTS, REVISITED, 118 6.6 MODEL COMPARISON, 120
6.6.1 MODELS, 120 6.6.2 ANALYSIS OF VARIANCE, 122 6.7 COMPLEMENTS, 125
6.7.1 DERIVATION OF ESTIMATES, 125
6.7.2 MEANS AND VARIANCES OF ESTIMATES, 126 6.7.3 WHY LEAST SQUARES?,
128 6.7.4 ALTERNATIVES TO LEAST SQUARES, 129
6.7.5 ACCURACY OF ESTIMATES, 130 6.7.6 ROLE OF NORMALITY, 130 6.7.7
MEASUREMENT ERROR, 130 6.7.8 REFERENCES, 132 PROBLEMS, 132
7 INTRODUCTION TO MULTIPLE LINEAR REGRESSION 139
7.1 THE SCATTERPLOT MATRIX, 140 7.1.1 PAIRS OF VARIABLES, 141 7.1.2
SEPARATED POINTS, 142 7.1.3 MARGINAL RESPONSE PLOTS, 143
7.1.4 EXTRACTING PLOTS, 145 7.2 TERMS AND PREDICTORS, 145 7.3 EXAMPLES,
147 7.3.1 SIMPLE LINEAR REGRESSION, 147
7.3.2 POLYNOMIAL MEAN FUNCTIONS WITH ONE PREDICTOR, 148 7.3.3 TWO
PREDICTORS, 150 7.3.4 POLYNOMIAL MEAN FUNCTIONS WITH TWO
PREDICTORS, 151 7.3.5 MANY PREDICTORS, 151
IMAGE 6
CONTENTS
XI
7.4 MULTIPLE LINEAR REGRESSION, 152 7.5 ESTIMATION OF PARAMETERS, 153
7.6 INFERENCE, 158 7.6.1 TESTS AND CONFIDENCE STATEMENTS ABOUT
PARAMETERS, 159 7.6.2 PREDICTION, 160 7.6.3 LEVERAGE AND EXTRAPOLATION,
161 7.6.4 GENERAL LINEAR COMBINATIONS, 163
7.6.5 OVERALL ANALYSIS OF VARIANCE, 164 7.6.6 THE COEFFICIENT OF
DETERMINATION, 165 7.7 THE LAKE MARY DATA, 166 7.8 REGRESSION THROUGH
THE ORIGIN, 167 7.9 COMPLEMENTS, 168
7.9.1 AN INTRODUCTION TO MATRICES, 168 7.9.2 RANDOM VECTORS, 172 7.9.3
CORRELATION MATRIX, 173 7.9.4 APPLICATIONS TO MULTIPLE LINEAR
REGRESSION, 173
7.9.5 ORDINARY LEAST SQUARES ESTIMATES, 174 7.9.6 REFERENCES, 178
PROBLEMS, 178
8 THREE-DIMENSIONAL PLOTS 185
8.1 VIEWING A THREE-DIMENSIONAL PLOT, 185 8.1.1 ROTATION CONTROL, 187
8.1.2 RECALLING VIEWS, 187 8.1.3 ROCKING, 187 8.1.4 SHOW AXES, 188 8.1.5
DEPTH CUING, 188
8.1.6 ZOOMING, 188 8.2 ADDING A POLYNOMIAL SURFACE, 188 8.2.1 PARAMETRIC
SMOOTHER SLIDEBAR, 188
8.2.2 EXTRACTING FITTED VALUES, 189 8.2.3 ADDING A FUNCTION, 189 8.2.4
RESIDUAIS, 190 8.3 SCALING AND CENTERING, 190 8.4 2D PLOTS FROM 3D
PLOTS, 191
8.4.1 SAVING A LINEAR COMBINATION, 192 8.4.2 ROTATION IN 2D, 192
IMAGE 7
XUE CONTENTS
8.4.3 EXTRACTING A 2D PLOT, 194
8.4.4 SUMMARY, 194 8.5 REMOVING A LINEAR TREND IN 3D PLOTS, 194 8.6
USING UNCORRELATED VARIABLES, 196 8.7 COMPLEMENTS, 198 PROBLEMS, 199
9 WEIGHTS AND LACK-OF-FIT 202
9.1 SNOW GEESE, 202 9.1.1 VISUALLY ASSESSING LACK-OF-FIT, 202 9.1.2
NONCONSTANT VARIANCES, 204 9.2 WEIGHTED LEAST SQUARES, 204
9.2.1 PARTICLE PHYSICS EXAMPLE, 206 9.2.2 PREDICTIONS, 209 9.3
LACK-OF-FIT METHODS, 210 9.3.1 VISUAL LACK-OF-FIT WITH SMOOTHS, 211
9.3.2 LACK-OF-FIT BASED ON VARIANCE, 212 9.3.3 VARIANCE KNOWN, 213 9.3.4
EXTERNAL ESTIMATES OF VARIATION, 214 9.3.5 REPLICATE OBSERVATIONS, 214
9.3.6 SUBSAMPLING, 217 9.4 FITTING WITH SUBPOPULATION AVERAGES, 217 9.5
COMPLEMENTS, 219
9.5.1 WEIGHTED LEAST SQUARES, 219 9.5.2 THE LOWESS SMOOTHER, 220 9.5.3
REFERENCES, 220 PROBLEMS, 221
10 UNDERSTANDING COEFSCIENTS 230
10.1 INTERPRETING COEFFICIENTS, 230 10.1.1 RESCALING, 230 10.1.2 RATE OF
CHANGE, 231 10.1.3 REPARAMETERIZATION, 232 10.1.4 NONLINEAR FUNCTIONS OF
TERMS, 234
10.1.5 VARIANCES OF COEFFICIENT ESTIMATES, 234 10.1.6 STANDARDIZATION OF
TERMS, 235 10.2 THE MULTIVARIATE NORMAL DISTRIBUTION, 235 10.3 SAMPLING
DISTRIBUTIONS, 237
IMAGE 8
CONTENTS
XIII
10.4 CORRELATION VERSUS CAUSATION AND THE SLEEP DATA, 238 10.4.1 MISSING
DATA, 239 10.4.2 THE MEAN FUNCTION, 240 10.4.3 THE DANGER INDICATOR, 240
10.4.4 INTERPRETATION, 242 10.5 2D ADDED-VARIABLE PLOTS, 243 10.5.1
ADDING A PREDICTOR TO SIMPLE REGRESSION, 244
10.5.2 ADDED-VARIABLE PLOTS IN ARE, 247 10.6 PROPERTIES OF 2D
ADDED-VARIABLE PLOTS, 247 10.6.1 INTERCEPT, 247
10.6.2 SLOPE, 247 10.6.3 RESIDUAIS, 248 10.6.4 SAMPLE PARTIAL
CORRELATION, 248
10.6.5 /-STATISTICS, 248 10.6.6 THREE EXTREME CASES, 248 10.7 3D
ADDED-VARIABLE PLOTS, 250 10.8 CONFIDENCE REGIONS, 250 10.8.1 CONFIDENCE
REGIONS FOR TWO COEFFICIENT
ESTIMATES, 251
10.8.2 BIVARIATE CONFIDENCE REGIONS WHEN THE MEAN FUNCTION HAS MANY
TERMS, 254 10.8.3 GENERAL CONFIDENCE REGIONS, 255 10.9 COMPLEMENTS, 256
10.9.1 MISSING DATA, 256 10.9.2 CAUSATION, ASSOCIATION, AND EXPERIMENTAL
DESIGNS, 256 10.9.3 NET EFFECTS PLOTS, 256 10.9.4 REFERENCES, 257
PROBLEMS, 257
11 RELATING MEAN FUNCTIONS 263
11.1 REMOVING TERMS, 263 11.1.1 MARGINAL MEAN FUNCTIONS, 264 11.1.2
MARGINAL VARIANCE FUNCTIONS, 265 11.1.3 EXAMPLE, 266
11.2 TESTS TO COMPARE MODELS, 266 11.3 HIGHWAY ACCIDENT DATA, 267 11.3.1
TESTING EQUALITY OF COEFFICIENTS, 269 11.3.2 OFFSETS, 270
IMAGE 9
XIV
CONTENTS
11.4 SEQUENTIAL FITTING, 271
11.5 SELECTING TERMS, 272 11.5.1 CRITERIA FOR SELECTING SUBMODELS, 274
11.5.2 STEPWISE METHODS, 275 11.5.3 HIGHWAY ACCIDENT DATA, 276 11.6
COMPLEMENTS, 283 PROBLEMS, 283
12 FACTORS AND INTERACTIONS 287
12.1 FACTORS, 287 12.1.1 TWO LEVELS, 287 12.1.2 MANY LEVELS, 288 12.2
TWIN DATA, 288
12.3 ONE-WAY ANALYSIS OF VARIANCE, 290 12.4 MODELS WITH CATEGORICAL AND
CONTINUOUS PREDICTORS, 292 12.4.1 FITTING, 294 12.4.2 TESTS, 296
12.5 TURKEY DIETS, 297 12.5.1 THE ZERO DOSE, 298 12.5.2 ADAPTING TO
CURVATURE, 298 12.6 CASUARINA DATA, 299
12.6.1 EFFECT THROUGH THE INTERCEPT, 301 12.6.2 EFFECT THROUGH INTERCEPT
AND SLOPE, 304 12.7 FACTORIAL EXPERIMENTS, 305 12.8 COMPLEMENTS, 308
12.8.1 ALTERNATE DEFINITIONS OF FACTORS, 308 12.8.2 COMPARING SLOPES
FROM SEPARATE FITS, 309 12.8.3 REFERENCES, 309 PROBLEMS, 310
13 RESPONSE TRANSFORMATIONS 316
13.1 RESPONSE TRANSFORMATIONS, 316 13.1.1 VARIANCE STABILIZING
TRANSFORMATIONS, 317 13.1.2 TRANSFORMING TO LINEARITY WITH ONE
PREDICTOR, 317
13.1.3 INVERSE FITTED VALUE PLOT, 320 13.1.4 NUMERICAL CHOICE OF
TRANSFORMATION, 321 13.2 TRANSFORMATIONS TO NORMALITY, 324 13.2.1 VISUAL
CHOICE OF TRANSFORMATION, 324
IMAGE 10
CONTENTS
13.2.2 AUTOMATIC CHOICE OF TRANSFONNATIONS, 326
13.2.3 POSSIBLE ROUTES, 329 13.3 COMPLEMENTS, 329 13.3.1 THE BOX-COX
METHOD, 329 13.3.2 PROFILE LOG-LIKELIHOODS AND CONFIDENCE
CURVES, 330
13.3.3 TRANSFORMATION FAMILIES, 330 13.3.4 REFERENCES, 331 PROBLEMS, 332
14 DIAGNOSTICS I: CURVATURE AND NONCONSTANT VARIANCE
14.1 THE RESIDUAIS, 336 14.1.1 DEFINITIONS AND RATIONALE, 336 14.1.2
RESIDUAL PLOTS, 337 14.1.3 CHOOSING RESIDUAL PLOTS, 339
14.1.4 EXAMPLES OF RESIDUAL PLOTS, 340 14.1.5 A NOTE OF CAUTION, 342
14.2 TESTING FOR CURVATURE, 343 14.3 TESTING FOR NONCONSTANT VARIANCE,
346 14.3.1 TRANSACTIONS DATA, 347 14.3.2 CAUTION DATA, 349 14.4
COMPLEMENTS, 350 PROBLEMS, 350
15 DIAGNOSTICS II: INFLUENCE AND OUTLIERS
15.1 ADAPTIVE SCORE DATA, 356 15.2 INFLUENTIAL CASES AND COOK S
DISTANCE, 357 15.3 RESIDUAIS, 360 15.3.1 STUDENTIZED RESIDUAIS, 360
15.3.2 COOK S DISTANCE AGAIN, 360 15.4 OUTLIERS, 361 15.4.1 TESTING FOR
A SINGLE OUTLIER, 362
15.4.2 CHECKING EVERY CASE, 364 15.4.3 ADAPTIVE SCORE DATA, 364 15.5
FUELDATA, 365 15.6 COMPLEMENTS, 368 15.6.1 UPDATING FORMULA, 368
IMAGE 11
XVI
CONTENTS
15.6.2 LOCAL INFLUENCE, 368
15.6.3 REFERENCES, 369 PROBLEMS, 369
16 PREDICTOR TRANSFORMATIONS 373
16.1 REGRESSION THROUGH TRANSFORMATION, 373 16.1.1 POWER CURVES AND
POLYNOMIAL FITS, 373 16.1.2 TRANSFORMATIONS VIA SMOOTHING, 375 16.1.3
GENERAL FORMULATION, 375 16.2 CERES PLOTS, 376
16.2.1 CONSTANT E(MJ | U 2 ), NO AUGMENTATION, 377 16.2.2 LINEAR E(U L
J U 2 ), LINEAR AUGMENTATION, 377 16.2.3 QUADRATIC E(WJ * | W 2 ),
QUADRATIC AUGMENTATION, 377 16.2.4 GENERAL E(MJ | W 2 ), SMOOTH
AUGMENTATION, 378 16.3 BERKELEY GUIDANCE STUDY, 378
16.4 HAYSTACK DATA, 380 16.5 TRANSFORMING MULTIPLE TERMS, 383 16.5.1
ESTIMATING ADDITIVE TRANSFORMATIONS OF SEVERAL TERMS, 383
16.5.2 ASSESSING THE TRANSFORMATIONS, 384 16.6 CERES PLOTS WITH SMOOTH
AUGMENTATION, 384 16.7 TRANSFORMING TWO TERMS SIMULTANEOUSLY, 388 16.7.1
MODELS FOR TRANSFORMING TWO TERMS, 388
16.7.2 EXAMPLE: PLANT HEIGHT, 389 16.8 COMPLEMENTS, 392 16.8.1 MIXED
FORMS OF E(W 1; -1 W 2 ), 392
16.8.2 REFERENCES, 393 PROBLEMS, 393
17 MODEL ASSESSMENT 396
17.1 MODEL CHECKING PLOTS, 397 17.1.1 CHECKING MEAN FUNCTIONS, 399
17.1.2 CHECKING VARIANCE FUNCTIONS, 401 17.2 RELATION TO RESIDUAL PLOTS,
403 17.3 SLEEP DATA, 404 17.4 COMPLEMENTS, 406 PROBLEMS, 407
IMAGE 12
CONTENTS
XVII
PART III REGRESSION GRAPHICS 409
18 VISUALIZING REGRESSION 411
18.1 PINETREES, 411 18.2 THE ESTIMATED 2D SUMMARY PLOT, 412 18.3
STRUCTURAL DIMENSION, 413 18.3.1 ZERO-DIMENSIONAL STRUCTURE, 413
18.3.2 ONE-DIMENSIONAL STRUCTURE, 413 18.3.3 TWO-DIMENSIONAL STRUCTURE,
416 18.4 CHECKING AN ESTIMATED SUMMARY PLOT, 417 18.5 MORE EXAMPLES AND
REFINEMENTS, 419 18.5.1 VISUALIZING LINEAR REGRESSION IN 3D PLOTS, 419
18.5.2 LINEAR REGRESSION WITHOUT LINEARLY RELATED
PREDICTORS, 422 18.5.3 MORE ON ORDINARY LEAST SQUARES SUMMARY VIEWS, 423
18.6 COMPLEMENTS, 425 PROBLEMS, 425
19 VISUALIZING REGRESSION WITH MANY PREDICTORS 430
19.1 LINEARLY RELATED PREDICTORS, 430 19.2 CHECKING LINEARLY RELATED
PREDICTORS, 431 19.3 LINEARLY RELATED PREDICTORS AND THE ID MODEL, 432
19.4 TRANSFORMING TO GET LINEARLY RELATED PREDICTORS, 433
19.5 FINDING DIMENSION GRAPHICALLY, 434 19.5.1 THE INVERSE REGRESSION
CURVE, 434 19.5.2 INVERSE MARGINAL RESPONSE PLOTS, 436 19.6 AUSTRALIAN
ATHLETES DATA, 438 19.7 COMPLEMENTS, 441
19.7.1 SLICED INVERSE REGRESSION, 441 19.7.2 REFERENCES, 442 PROBLEMS,
442
20 GRAPHICAL REGRESSION 446
20.1 OVERVIEW OF GRAPHICAL REGRESSION, 446 20.2 MUSSEIS MUSCLES, 447
20.2.1 THE GREG PREDICTORS, 447 20.2.2 GRAPHICAL REGRESSION, 448
IMAGE 13
XVIII
CONTENTS
20.3 REACTION YIELD, 452
20.3.1 LINEARLY RELATED PREDICTORS, 453 20.3.2 GRAPHICAL REGRESSION, 454
20.3.3 CONTINUING THE ANALYSIS, 454 20.4 VARIATIONS, 457
20.4.1 STANDARDIZING THE LINEAR PREDICTORS, 457 20.4.2 IMPROVING
RESOLUTION IN 3D ADDED-VARIABLE PLOTS, 457 20.4.3 MODEL CHECKING, 458
20.4.4 USING THE LINEARLY RELATED PREDICTORS, 460 20.5 COMPLEMENTS, 461
20.5.1 GREG PREDICTORS AND PRINCIPAL HESSIAN DIRECTIONS, 461 20.5.2
REFERENCES, 462 PROBLEMS, 462
PART IV LOGISTIC REGRESSION AND GENERALIZED LINEAR MODELS 465
21 BINOMIAL REGRESSION 467
21.1 RECUMBENT COWS, 467 21.1.1 CATEGORICAL PREDICTORS, 468 21.1.2
CONTINUOUS PREDICTORS, 470
21.2 PROBABILITY MODELS FOR COUNTED DATA, 471 21.2.1 THE BERNOULLI
DISTRIBUTION, 471 21.2.2 BINOMIAL RANDOM VARIABLES, 472 21.2.3
INFERENCE, 474 21.3 BINOMIAL REGRESSION, 475
21.3.1 MEAN FUNCTIONS FOR BINOMIAL REGRESSION, 476 21.3.2 SUMMARY, 477
21.4 FITTING LOGISTIC REGRESSION, 478 21.4.1 UNDERSTANDING COEFFICIENTS,
480
21.4.2 MANY TERMS, 482 21.4.3 DEVIANCE, 483 21.4.4 GOODNESS-OF-FIT
TESTS, 485 21.5 WEEVIL PREFERENCES, 486 21.6 COMPLEMENTS, 489
21.6.1 NORMAL APPROXIMATION TO THE BINOMIAL, 489 21.6.2 SMOOTHING A
BINARY RESPONSE, 490
IMAGE 14
CONTENTS
XIX
21.6.3 PROBIT AND CLOG-LOG KERNEL MEAN FUNCTIONS, 490
21.6.4 THE LOG-LIKELIHOOD FOR LOGISTIC REGRESSION, 491
21.6.5 REFERENCES, 493
PROBLEMS, 493
22 GRAPHICAL AND DIAGNOSTIC METHODS FOR LOGISTIC REGRESSION 497
22.1 ONE-PREDICTOR METHODS, 497
22.1.1 JITTERING TO SEE RELATIVE DENSITY, 498
22.1.2 USING THE CONDITIONAL DENSITY OF X | Y, 498
22.1.3 LOGISTIC REGRESSION FROM CONDITIONAL DENSITIES, 500
22.1.4 SPECIFIC CONDITIONAL DENSITIES, 500
22.1.5 IMPLICATIONS FOR THE RECUMBENT COW DATA, 501
22.2 VISUALIZING LOGISTIC REGRESSION WITH TWO OR MORE PREDICTORS, 504
22.2.1 ASSESSING THE PREDICTORS, 505 22.2.2 ASSESSING A LOGISTIC MODEL
WITH TWO PREDICTORS, 506
22.2.3 ASSESSING A LOGISTIC MODEL WITH THREE PREDICTORS, 507
22.3 TRANSFORMING PREDICTORS, 509
22.3.1 GUIDELINES, 509
22.3.2 TRANSFORMING X|Y TO MULTIVARIATE NORMALITY, 510 22.4 DIAGNOSTIC
METHODS, 512
22.4.1 RESIDUAL PLOTS, 512
22.4.2 INFLUENCE, 513
22.4.3 MODEL CHECKING PLOTS, 514
22.5 ADDING FACTORS, 517 22.6 EXTENDING PREDICTOR TRANSFORMATIONS, 519
22.6.1 POWER TRANSFORMATIONS WITH A BINOMIAL RESPONSE, 519
22.6.2 CERES PLOTS, 519
22.7 COMPLEMENTS, 519
22.7.1 MARGINAL ODDS RATIO, 519
22.7.2 RELATIVE DENSITY, 520
22.7.3 DEVIANCE RESIDUAIS, 520
IMAGE 15
XX
CONTENTS
22.7.4 OUTLIERS, 520
22.7.5 OVERDISPERSION, 521 22.7.6 GRAPHICAL REGRESSION, 521 22.7.7
REFERENCES, 522 PROBLEMS, 522
23 GENERALIZED LINEAR MODELS 525
23.1 COMPONENTS OF A GENERALIZED LINEAR MODEL, 525 23.2 NORMAL MODELS,
527 23.2.1 TRANSFORMATION OF PARAMETERS, 531 23.2.2 TRANSFORMATION TO
SIMPLE LINEAR REGRESSION, 531
23.3 POISSON REGRESSION, 532 23.3.1 LOG-LINEAR MODELS, 538 23.4 GAMMA
REGRESSION, 539 23.5 COMPLEMENTS, 540
23.5.1 POISSON DISTRIBUTION, 540 23.5.2 GAMMA DISTRIBUTION, 542 23.5.3
REFERENCES, 542 PROBLEMS, 542
APPENDIX A ARE 545
A.L GETTING THE SOFTWARE, 545 A.L.L MACINTOSH OS, 545 A.1.2 WINDOWS OS,
547 A.L.3 UNIX, 548 A.L.4 WHATYOUGET, 548 A.L.5 DATA FILES, 548 A.2 THE
TEXT WINDOW, 549
A.2.1 TYPING IN THE TEXT WINDOW, 549 A.2.2 TYPING DATA, 549 A.2.3
WORKING WITH LISTS, 551 A.2.4 CALCULATING THE SLOPE AND INTERCEPT, 552
A.3 SAVING AND PRINTING, 553
A.3.1 TEXT, 553 A.3.2 GRAPHICS, 554 A.4 QUITTING, 554 A.5 DATA FILES,
554
A.5.1 PIAIN DATA, 554 A.5.2 PIAIN DATA FILE WITH VARIABLE LABELS, 555
IMAGE 16
CONTENTS
XXI
A.5.3 IMPORTING DATA FROM A SPREADSHEET, 555 A.5.4 SPECIAL CHARACTERS,
555 A.5.5 GETTING INTO TROUBLE WITH PIAIN DATA FILES, 555 A.5.6
FORMATTED DATA FILE, 556 A.5.7 CREATING A DATA SET FROM THE TEXT WINDOW,
558 A.5.8 OLD-STYLE DATA FILES, 558 A.5.9 MISSING VALUES, 559 A.6 THE
ARE MENUE, 559 A.7 THE DATA SET MENUE, 560
A.7.1 DESCRIPTION OF DATA, 560 A.7.2 MODIFYING DATA, 561 A.8 GRAPHICS
FROM THE GRAPH&FIT MENUE, 562 A.8.1 HISTOGRAMS AND PLOT CONTROLS, 563
A.8.2 TWO-DIMENSIONAL PLOTS AND PLOT CONTROLS, 564 A.8.3
THREE-DIMENSIONAL PLOTS, 566 A.8.4 BOXPLOTS, 566 A.8.5 SCATTERPLOT
MATRICES, 566 A.9 FITTING MODELS, 566 A.10 MODEL MENUES, 567
A . LL ADDING STATISTICS TO A DATA SET, 567 A.12 SOME USEFUL FUNCTIONS,
567 A.12.1 GETTING HELP, 570
REFERENCES 571
AUTHOR INDEX 579
SUBJECT INDEX 583
|
| any_adam_object | 1 |
| author | Cook, R. Dennis 1944- Weisberg, Sanford 1947- |
| author_GND | (DE-588)172034078 (DE-588)170214664 |
| author_facet | Cook, R. Dennis 1944- Weisberg, Sanford 1947- |
| author_role | aut aut |
| author_sort | Cook, R. Dennis 1944- |
| author_variant | r d c rd rdc s w sw |
| building | Verbundindex |
| bvnumber | BV013068531 |
| classification_rvk | SK 800 SK 840 |
| classification_tum | MAT 628f |
| ctrlnum | (OCoLC)633554174 (DE-599)BVBBV013068531 |
| discipline | Mathematik |
| format | Book |
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| id | DE-604.BV013068531 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:38:33Z |
| institution | BVB |
| isbn | 047131711X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008903314 |
| oclc_num | 633554174 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-703 DE-384 DE-29 DE-19 DE-BY-UBM DE-634 DE-83 DE-188 |
| owner_facet | DE-91G DE-BY-TUM DE-703 DE-384 DE-29 DE-19 DE-BY-UBM DE-634 DE-83 DE-188 |
| physical | XXVI, 593 S. graph. Darst. |
| publishDate | 1999 |
| publishDateSearch | 1999 |
| publishDateSort | 1999 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Cook, R. Dennis 1944- Verfasser (DE-588)172034078 aut Applied regression including computing and graphics R. Dennis Cook ; Sanford Weisberg New York [u.a.] Wiley 1999 XXVI, 593 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s DE-604 Weisberg, Sanford 1947- Verfasser (DE-588)170214664 aut Erscheint auch als Online-Ausgabe 978-0-470-31694-8 Erscheint auch als Online-Ausgabe 978-0-470-31778-5 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008903314&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cook, R. Dennis 1944- Weisberg, Sanford 1947- Applied regression including computing and graphics Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4129903-6 |
| title | Applied regression including computing and graphics |
| title_auth | Applied regression including computing and graphics |
| title_exact_search | Applied regression including computing and graphics |
| title_full | Applied regression including computing and graphics R. Dennis Cook ; Sanford Weisberg |
| title_fullStr | Applied regression including computing and graphics R. Dennis Cook ; Sanford Weisberg |
| title_full_unstemmed | Applied regression including computing and graphics R. Dennis Cook ; Sanford Weisberg |
| title_short | Applied regression including computing and graphics |
| title_sort | applied regression including computing and graphics |
| topic | Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Regressionsanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008903314&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cookrdennis appliedregressionincludingcomputingandgraphics AT weisbergsanford appliedregressionincludingcomputingandgraphics |