Introduction to linear regression analysis:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley-Interscience
2006
|
| Ausgabe: | 4. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XVI, 612 S. graph. Darst. |
| ISBN: | 0471754951 9780471754954 |
Internformat
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| 100 | 1 | |a Montgomery, Douglas C. |d 1943- |e Verfasser |0 (DE-588)12861448X |4 aut | |
| 245 | 1 | 0 | |a Introduction to linear regression analysis |c Douglas C. Montgomery ; Elizabeth A. Peck ; G. Geoffrey Vining |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley-Interscience |c 2006 | |
| 300 | |a XVI, 612 S. |b graph. Darst. | ||
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| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Analyse de régression | |
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| 650 | 7 | |a Regressieanalyse |2 gtt | |
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Datensatz im Suchindex
| _version_ | 1804135350303457280 |
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| adam_text | INTRODUCTION TO LINEAR REGRESSION ANALYSIS FOURTH EDITION DOUGLAS C.
MONTGOMERY ARIZONA STATE UNIVERSITY DEPARTMENT OF INDUSTRIAL ENGINEERING
TEMPE, PJL ELIZABETH A. PECK THE COCA-COLA COMPANY (RETIRED) ATLANTA, GA
G. GEOFFREY VINING VIRGINIA TECH DEPARTMENT OF STATISTICS BLACKSBURG, VA
WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC. PUBLICATION CONTENTS
PREFACE 1. INTRODUCTION 1.1 REGRESSION AND MODEL BUILDING, 1 1.2 DATA
COLLECTION, 5 1.3 USES OF REGRESSION, 9 1.4 ROLE OF THE COMPUTER, 10 2.
SIMPLE LINEAR REGRESSION 2.1 SIMPLE LINEAR REGRESSION MODEL, 12 2.2
LEAST-SQUARES ESTIMATION OF THE PARAMETERS, 13 2.2.1 ESTIMATION OF SS 0
AND SS V 13 2.2.2 PROPERTIES OF THE LEAST-SQUARES ESTIMATORS AND THE
FITTED REGRESSION MODEL, 17 2.2.3 ESTIMATION OF A 2 , 20 2.2.4 ALTERNATE
FORM OF THE MODEL, 21 2.3 HYPOTHESIS TESTING ON THE SLOPE AND INTERCEPT,
22 2.3.1 USE OF T TESTS, 22 2.3.2 TESTING SIGNIFICANCE OF REGRESSION, 23
2.3.3 ANALYSIS OF VARIANCE, 25 2.4 INTERVAL ESTIMATION IN SIMPLE LINEAR
REGRESSION, 28 2.4.1 CONFIDENCE INTERVALS ON SS 0 , SS U AND A 2 , 28
2.4.2 INTERVAL ESTIMATION OF THE MEAN RESPONSE, 30 2.5 PREDICTION OF NEW
OBSERVATIONS, 33 2.6 COEFFICIENT OF DETERMINATION, 35 2.7 USING SAS FOR
SIMPLE LINEAR REGRESSION, 36 2.8 SOME CONSIDERATIONS IN THE USE OF
REGRESSION, 37 2.9 REGRESSION THROUGH THE ORIGIN, 41 2.10 ESTIMATION BY
MAXIMUM LIKELIHOOD, 47 VI CONTENTS 2.11 CASE WHERE THE REGRESSOR X IS
RANDOM, 49 2.11.1 X AND Y JOINTLY DISTRIBUTED, 49 2.11.2 X AND Y JOINTLY
NORMALLY DISTRIBUTED: CORRELATION MODEL, 49 PROBLEMS, 54 3. MULTIPLE
LINEAR REGRESSION 63 3.1 MULTIPLE REGRESSION MODELS, 63 3.2 ESTIMATION
OF THE MODEL PARAMETERS, 66 3.2.1 LEAST-SQUARES ESTIMATION OF THE
REGRESSION COEFFICIENTS, 66 3.2.2 GEOMETRICAL INTERPRETATION OF LEAST
SQUARES, 74 3.2.3 PROPERTIES OF THE LEAST-SQUARES ESTIMATORS, 75 3.2.4
ESTIMATION OF O- 2 , 76 3.2.5 INADEQUACY OF SCATTER DIAGRAMS IN MULTIPLE
REGRESSION, 77 3.2.6 MAXIMUM-LIKELIHOOD ESTIMATION, 79 3.3 HYPOTHESIS
TESTING IN MULTIPLE LINEAR REGRESSION, 80 3.3.1 TEST FOR SIGNIFICANCE OF
REGRESSION, 80 3.3.2 TESTS ON INDIVIDUAL REGRESSION COEFFICIENTS, 84
3.3.3 SPECIAL CASE OF ORTHOGONAL COLUMNS IN X, 89 3.3.4 TESTING THE
GENERAL LINEAR HYPOTHESIS, 90 3.4 CONFIDENCE INTERVALS IN MULTIPLE
REGRESSION, 93 3.4.1 CONFIDENCE INTERVALS ON THE REGRESSION
COEFFICIENTS, 93 3.4.2 CONFIDENCE INTERVAL ESTIMATION OF THE MEAN
RESPONSE, 94 3.4.3 SIMULTANEOUS CONFIDENCE INTERVALS ON REGRESSION
COEFFICIENTS, 96 3.5 PREDICTION OF NEW OBSERVATIONS, 99 3.6 USING SAS
FOR BASIC MULTIPLE LINEAR REGRESSION, 101 3.7 HIDDEN EXTRAPOLATION IN
MULTIPLE REGRESSION, 101 3.8 STANDARDIZED REGRESSION COEFFICIENTS, 105
3.9 MULTICOLLINEARITY, 109 3.10 WHY DO REGRESSION COEFFICIENTS HAVE THE
WRONG SIGN?, 112 PROBLEMS, 114 4. MODEL ADEQUACY CHECKING 122 4.1
INTRODUCTION, 122 4.2 RESIDUAL ANALYSIS, 123 CONTENTS VII 4.2.1
DEFINITION OF RESIDUAIS, 123 4.2.2 METHODS FOR SCALING RESIDUAIS, 123
4.2.3 RESIDUAL PLOTS, 129 4.2.4 PARTIAL REGRESSION AND PARTIAL RESIDUAL
PLOTS, 134 4.2.5 USING MINITAB AND SAS FOR RESIDUAL ANALYSIS, 137 4.2.6
OTHER RESIDUAL PLOTTING AND ANALYSIS METHODS, 138 4.3 PRESS STATISTIC,
141 4.4 DETECTION AND TREATMENT OF OUTLIERS, 142 4.5 LACK OF FIT OF THE
REGRESSION MODEL, 145 4.5.1 FORMAL TEST FOR LACK OF FIT, 145 4.5.2
ESTIMATION OF PURE ERROR FROM NEAR NEIGHBORS, 149 PROBLEMS, 153 5.
TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES 160 5.1
INTRODUCTION, 160 5.2 VARIANCE-STABILIZING TRANSFORMATIONS, 161 5.3
TRANSFORMATIONS TO LINEARIZE THE MODEL, 164 5.4 ANALYTICAL METHODS FOR
SELECTING A TRANSFORMATION, 171 5.4.1 TRANSFORMATIONS ON V: THE BOX-COX
METHOD, 171 5.4.2 TRANSFORMATIONS ON THE REGRESSOR VARIABLES, 174 5.5
GENERALIZED AND WEIGHTED LEAST SQUARES, 176 5.5.1 GENERALIZED LEAST
SQUARES, 177 5.5.2 WEIGHTED LEAST SQUARES, 179 5.5.3 SOME PRACTICAL
ISSUES, 180 PROBLEMS, 183 6. DIAGNOSTICS FOR LEVERAGE AND INFLUENCE 189
6.1 IMPOERTANCE OF DETECTING INFLUENTIAL OBSERVATIONS, 189 6.2 LEVERAGE,
190 6.3 MEASURES OF INFLUENCE: COOK S D, 193 6.4 MEASURES OF INFLUENCE:
DFFITS AND DFBETAS, 195 6.5 A MEASURE OF MODEL PERFORMANCE, 197 6.6
DETECTING GROUPS OF INFLUENTIAL OBSERVATIONS, 198 6.7 TREATMENT OF
INFLUENTIAL OBSERVATIONS, 199 PROBLEMS, 199 7. POLYNOMIAL REGRESSION
MODELS 201 7.1 INTRODUCTION, 201 7.2 POLYNOMIAL MODELS IN ONE VARIABLE,
201 7.2.1 BASIC PRINCIPLES, 201 CONTENTS 7.2.2 PIECEWISE POLYNOMIAL
FITTING (SPLINES), 207 7.2.3 POLYNOMIAL AND TRIGONOMETRIE TERMS, 213 7.3
NONPARAMETRIC REGRESSION, 214 7.3.1 KERNEL REGRESSION, 214 7.3.2 LOCALLY
WEIGHTED REGRESSION (LOESS), 215 7.3.3 FINAL CAUTIONS, 219 7.4
POLYNOMIAL MODELS IN TWO OR MORE VARIABLES, 220 7.5 ORTHOGONAL
POLYNOMIALS, 226 PROBLEMS, 231 INDICATOR VARIABLES 237 8.1 GENERAL
CONCEPT OF INDICATOR VARIABLES, 237 8.2 COMMENTS ON THE USE OF INDICATOR
VARIABLES, 249 8.2.1 INDICATOR VARIABLES VERSUS REGRESSION ON ALLOCATED
CODES, 249 8.2.2 INDICATOR VARIABLES AS A SUBSTITUTE FOR A QUANTITATIVE
REGRESSOR, 250 8.3 REGRESSION APPROACH TO ANALYSIS OF VARIANCE, 251
PROBLEMS, 256 VARIABLE SELECTION AND MODEL BUILDING 261 9.1
INTRODUCTION, 261 9.1.1 MODEL-BUILDING PROBLEM, 261 9.1.2 CONSEQUENCES
OF MODEL MISSPECIFICATION, 262 9.1.3 CRITERIA FOR EVALUATING SUBSET
REGRESSION MODELS, 265 9.2 COMPUTATIONAL TECHNIQUES FOR VARIABLE
SELECTION, 270 9.2.1 ALL POSSIBLE REGRESSIONS, 270 9.2.2 STEPWISE
REGRESSION METHODS, 277 9.3 STRATEGY FOR VARIABLE SELECTION AND MODEL
BUILDING, 283 9.4 CASE STUDY: GORMAN AND TOMAN ASPHALT DATA USING SAS,
286 PROBLEMS, 300 VALIDATION OF REGRESSION MODELS 305 10.1 INTRODUCTION,
305 10.2 VALIDATION TECHNIQUES, 306 10.2.1 ANALYSIS OF MODEL
COEFFICIENTS AND PREDICTED VALUES, 306 10.2.2 COLLECTING FRESH
DATA*CONFIRMATION RUNS, 308 CONTENTS IX 10.2.3 DATA SPLITTING, 310 10.3
DATA FROM PLANNED EXPERIMENTS, 318 PROBLEMS, 319 11. MULTICOLLINEARITY
323 11.1 INTRODUCTION, 323 11.2 SOURCES OF MULTICOLLINEARITY, 323 11.3
EFFECTS OF MULTICOLLINEARITY, 326 11.4 MULTICOLLINEARITY DIAGNOSTICS,
331 11.4.1 EXAMINATION OF THE CORRELATION MATRIX, 333 11.4.2 VARIANCE
INFLATION FACTORS, 334 11.4.3 EIGENSYSTEM ANALYSIS OF X X, 335 11.4.4
OTHER DIAGNOSTICS, 340 11.4.5 SAS CODE FOR GENERATING MULTICOLLINEARITY
DIAGNOSTICS, 341 11.5 METHODS FOR DEALING WITH MULTICOLLINEARITY, 341
11.5.1 COLLECTING ADDITIONAL DATA, 341 11.5.2 MODEL RESPECIFICATION, 342
11.5.3 RIDGE REGRESSION, 344 11.5.4 PRINCIPAL-COMPONENT REGRESSION, 355
11.5.5 COMPARISON AND EVALUATION OF BIASED ESTIMATORS, 360 11.6 USING
SAS TO PERFORM RIDGE AND PRINCIPAL- COMPONENT REGRESSION, 363 PROBLEMS,
365 12. ROBUST REGRESSION 369 12.1 NEED FOR ROBUST REGRESSION, 369 12.2
M-ESTIMATORS, 372 12.3 PROPERTIES OF ROBUST ESTIMATORS, 384 12.3.1
BREAKDOWN POINT, 385 12.3.2 EFFICIENCY, 385 12.4 SURVEY OF OTHER ROBUST
REGRESSION ESTIMATORS, 386 12.4.1 HIGH-BREAKDOWN-POINT ESTIMATORS, 386
12.4.2 BOUNDED INFLUENCE ESTIMATORS, 389 12.4.3 OTHER PROCEDURES, 391
12.4.4 COMPUTING ROBUST REGRESSION ESTIMATORS, 392 PROBLEMS, 393 13.
INTRODUCTION TO NONLINEAR REGRESSION 397 13.1 LINEAR AND NONLINEAR
REGRESSION MODELS, 397 X CONTENTS 13.1.1 LINEAR REGRESSION MODELS, 397
13.1.2 NONLINEAR REGRESSION MODELS, 398 13.2 ORIGINS OF NONLINEAR
MODELS, 399 13.3 NONLINEAR LEAST SQUARES, 403 13.4 TRANSFORMATION TO A
LINEAR MODEL, 405 13.5 PARAMETER ESTIMATION IN A NONLINEAR SYSTEM, 408
13.5.1 LINEARIZATION, 408 13.5.2 OTHER PARAMETER ESTIMATION METHODS, 414
13.5.3 STARTING VALUES, 415 13.5.4 COMPUTER PROGRAMS, 416 13.6
STATISTICAL INFERENCE IN NONLINEAR REGRESSION, 417 13.7 EXAMPLES OF
NONLINEAR REGRESSION MODELS, 419 13.8 USING SAS PROC NLIN, 420 PROBLEMS,
423 14. GENERALIZED LINEAR MODELS 427 14.1 INTRODUCTION, 427 14.2
LOGISTIC REGRESSION MODELS, 428 14.2.1 MODELS WITH A BINARY RESPONSE
VARIABLE, 428 14.2.2 ESTIMATING THE PARAMETERS IN A LOGISTIC REGRESSION
MODEL, 430 14.2.3 INTERPRETATION OF THE PARAMETERS IN A LOGISTIC
REGRESSION MODEL, 433 14.2.4 STATISTICAL INFERENCE ON MODEL PARAMETERS,
436 14.2.5 DIAGNOSTIC CHECKING IN LOGISTIC REGFESSION, 444 14.2.6 OTHER
MODELS FOR BINARY RESPONSE DATA, 446 14.2.7 MORE THAN TWO CATEGORICAL
OUTCOMES, 447 14.3 POISSON REGRESSION, 449 14.4 THE GENERALIZED LINEAR
MODEL, 454 14.4.1 LINK FUNCTIONS AND LINEAR PREDICTORS, 455 14.4.2
PARAMETER ESTIMATION AND INFERENCE IN THE GLM, 456 14.4.3 PREDICTION AND
ESTIMATION WITH THE GLM, 460 14.4.4 RESIDUAL ANALYSIS IN THE GLM, 461
14.4.5 OVERDISPERSION, 464 PROBLEMS, 465 15. OTHER TOPICS IN THE USE OF
REGRESSION ANALYSIS 475 15.1 REGRESSION MODELS WITH AUTOCORRELATED
ERRORS, 475 15.1.1 SOURCE AND EFFECTS OF AUTOCORRELATION, 475 15.1.2
DETECTING THE PRESENCE OF AUTOCORRELATION, 476 15.1.3 PARAMETER
ESTIMATION METHODS, 479 CONTENTS XI 15.2 EFFECT OF MEASUREMENT ERRORS IN
THE REGRESSORS, 486 15.2.1 SIMPLE LINEAR REGRESSION, 486 15.2.2 BERKSON
MODEL, 488 15.3 INVERSE ESTIMATION*THE CALIBRATION PROBLEM, 488 15.4
BOOTSTRAPPING IN REGRESSION, 493 15.4.1 BOOTSTRAP SAMPLING IN
REGRESSION, 494 15.4.2 BOOTSTRAP CONFIDENCE INTERVALS, 494 15.5
CLASSIFICATION AND REGRESSION TREES (CART), 500 15.6 NEURAL NETWORKS,
502 15.7 DESIGNED EXPERIMENTS FOR REGRESSION, 505 PROBLEMS, 507 APPENDIX
A. STATISTICAL TABLES 511 APPENDIX B. DATA SETS FOR EXERCISES 529
APPENDIX C. SUPPLEMENTAL TECHNICAL MATERIAL 546 C.L BACKGROUND ON BASIC
TEST STATISTICS, 546 C.2 BACKGROUND FROM THE THEORY OF LINEAR MODELS,
548 C.3 IMPORTANT RESULTS ON SS R AND 55 RES , 552 C.4 GAUSS-MARKOV
THEOREM, VAR(E) = CR 2 I, 558 C.5 COMPUTATIONAL ASPECTS OF MULTIPLE
REGRESSION, 560 C.6 RESULT ON THE INVERSE OF A MATRIX, 562 C.7
DEVELOPMENT OF THE PRESS STATISTIC, 562 C.8 DEVELOPMENT OF S^, 564 C.9
OUTLIER TEST BASED ON FL-STUDENT, 565 CIO INDEPENDENCE OF RESIDUAIS AND
FITTED VALUES, 568 C.LL THE GAUSS-MARKOV THEOREM, VAR(E) = V, 569 C.12
BIAS IN MS RES WHEN THE MODEL IS UNDERSPECIFIED, 571 C.13 COMPUTATION OF
INFLUENCE DIAGNOSTICS, 572 C.14 GENERALIZED LINEAR MODELS, 573 APPENDIX
D. INTRODUCTION TO SAS 584 D.L BASIC DATA ENTRY, 584 D.2 CREATING
PERMANENT SAS DATA SETS, 589 D.3 IMPORTING DATA FROM AN EXCEL FILE, 590
D.4 OUTPUT COMMAND, 591 D.5 LOG FILE, 591 D.6 ADDING VARIABLES TO AN
EXISTING SAS DATA SET, 593 REFERENCES 594 INDEX 609
|
| adam_txt |
INTRODUCTION TO LINEAR REGRESSION ANALYSIS FOURTH EDITION DOUGLAS C.
MONTGOMERY ARIZONA STATE UNIVERSITY DEPARTMENT OF INDUSTRIAL ENGINEERING
TEMPE, PJL ELIZABETH A. PECK THE COCA-COLA COMPANY (RETIRED) ATLANTA, GA
G. GEOFFREY VINING VIRGINIA TECH DEPARTMENT OF STATISTICS BLACKSBURG, VA
WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC. PUBLICATION CONTENTS
PREFACE 1. INTRODUCTION 1.1 REGRESSION AND MODEL BUILDING, 1 1.2 DATA
COLLECTION, 5 1.3 USES OF REGRESSION, 9 1.4 ROLE OF THE COMPUTER, 10 2.
SIMPLE LINEAR REGRESSION 2.1 SIMPLE LINEAR REGRESSION MODEL, 12 2.2
LEAST-SQUARES ESTIMATION OF THE PARAMETERS, 13 2.2.1 ESTIMATION OF SS 0
AND SS V 13 2.2.2 PROPERTIES OF THE LEAST-SQUARES ESTIMATORS AND THE
FITTED REGRESSION MODEL, 17 2.2.3 ESTIMATION OF A 2 , 20 2.2.4 ALTERNATE
FORM OF THE MODEL, 21 2.3 HYPOTHESIS TESTING ON THE SLOPE AND INTERCEPT,
22 2.3.1 USE OF T TESTS, 22 2.3.2 TESTING SIGNIFICANCE OF REGRESSION, 23
2.3.3 ANALYSIS OF VARIANCE, 25 2.4 INTERVAL ESTIMATION IN SIMPLE LINEAR
REGRESSION, 28 2.4.1 CONFIDENCE INTERVALS ON SS 0 , SS U AND A 2 , 28
2.4.2 INTERVAL ESTIMATION OF THE MEAN RESPONSE, 30 2.5 PREDICTION OF NEW
OBSERVATIONS, 33 2.6 COEFFICIENT OF DETERMINATION, 35 2.7 USING SAS FOR
SIMPLE LINEAR REGRESSION, 36 2.8 SOME CONSIDERATIONS IN THE USE OF
REGRESSION, 37 2.9 REGRESSION THROUGH THE ORIGIN, 41 2.10 ESTIMATION BY
MAXIMUM LIKELIHOOD, 47 VI CONTENTS 2.11 CASE WHERE THE REGRESSOR X IS
RANDOM, 49 2.11.1 X AND Y JOINTLY DISTRIBUTED, 49 2.11.2 X AND Y JOINTLY
NORMALLY DISTRIBUTED: CORRELATION MODEL, 49 PROBLEMS, 54 3. MULTIPLE
LINEAR REGRESSION 63 3.1 MULTIPLE REGRESSION MODELS, 63 3.2 ESTIMATION
OF THE MODEL PARAMETERS, 66 3.2.1 LEAST-SQUARES ESTIMATION OF THE
REGRESSION COEFFICIENTS, 66 3.2.2 GEOMETRICAL INTERPRETATION OF LEAST
SQUARES, 74 3.2.3 PROPERTIES OF THE LEAST-SQUARES ESTIMATORS, 75 3.2.4
ESTIMATION OF O- 2 , 76 3.2.5 INADEQUACY OF SCATTER DIAGRAMS IN MULTIPLE
REGRESSION, 77 3.2.6 MAXIMUM-LIKELIHOOD ESTIMATION, 79 3.3 HYPOTHESIS
TESTING IN MULTIPLE LINEAR REGRESSION, 80 3.3.1 TEST FOR SIGNIFICANCE OF
REGRESSION, 80 3.3.2 TESTS ON INDIVIDUAL REGRESSION COEFFICIENTS, 84
3.3.3 SPECIAL CASE OF ORTHOGONAL COLUMNS IN X, 89 3.3.4 TESTING THE
GENERAL LINEAR HYPOTHESIS, 90 3.4 CONFIDENCE INTERVALS IN MULTIPLE
REGRESSION, 93 3.4.1 CONFIDENCE INTERVALS ON THE REGRESSION
COEFFICIENTS, 93 3.4.2 CONFIDENCE INTERVAL ESTIMATION OF THE MEAN
RESPONSE, 94 3.4.3 SIMULTANEOUS CONFIDENCE INTERVALS ON REGRESSION
COEFFICIENTS, 96 3.5 PREDICTION OF NEW OBSERVATIONS, 99 3.6 USING SAS
FOR BASIC MULTIPLE LINEAR REGRESSION, 101 3.7 HIDDEN EXTRAPOLATION IN
MULTIPLE REGRESSION, 101 3.8 STANDARDIZED REGRESSION COEFFICIENTS, 105
3.9 MULTICOLLINEARITY, 109 3.10 WHY DO REGRESSION COEFFICIENTS HAVE THE
WRONG SIGN?, 112 PROBLEMS, 114 4. MODEL ADEQUACY CHECKING 122 4.1
INTRODUCTION, 122 4.2 RESIDUAL ANALYSIS, 123 CONTENTS VII 4.2.1
DEFINITION OF RESIDUAIS, 123 4.2.2 METHODS FOR SCALING RESIDUAIS, 123
4.2.3 RESIDUAL PLOTS, 129 4.2.4 PARTIAL REGRESSION AND PARTIAL RESIDUAL
PLOTS, 134 4.2.5 USING MINITAB AND SAS FOR RESIDUAL ANALYSIS, 137 4.2.6
OTHER RESIDUAL PLOTTING AND ANALYSIS METHODS, 138 4.3 PRESS STATISTIC,
141 4.4 DETECTION AND TREATMENT OF OUTLIERS, 142 4.5 LACK OF FIT OF THE
REGRESSION MODEL, 145 4.5.1 FORMAL TEST FOR LACK OF FIT, 145 4.5.2
ESTIMATION OF PURE ERROR FROM NEAR NEIGHBORS, 149 PROBLEMS, 153 5.
TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES 160 5.1
INTRODUCTION, 160 5.2 VARIANCE-STABILIZING TRANSFORMATIONS, 161 5.3
TRANSFORMATIONS TO LINEARIZE THE MODEL, 164 5.4 ANALYTICAL METHODS FOR
SELECTING A TRANSFORMATION, 171 5.4.1 TRANSFORMATIONS ON V: THE BOX-COX
METHOD, 171 5.4.2 TRANSFORMATIONS ON THE REGRESSOR VARIABLES, 174 5.5
GENERALIZED AND WEIGHTED LEAST SQUARES, 176 5.5.1 GENERALIZED LEAST
SQUARES, 177 5.5.2 WEIGHTED LEAST SQUARES, 179 5.5.3 SOME PRACTICAL
ISSUES, 180 PROBLEMS, 183 6. DIAGNOSTICS FOR LEVERAGE AND INFLUENCE 189
6.1 IMPOERTANCE OF DETECTING INFLUENTIAL OBSERVATIONS, 189 6.2 LEVERAGE,
190 6.3 MEASURES OF INFLUENCE: COOK'S D, 193 6.4 MEASURES OF INFLUENCE:
DFFITS AND DFBETAS, 195 6.5 A MEASURE OF MODEL PERFORMANCE, 197 6.6
DETECTING GROUPS OF INFLUENTIAL OBSERVATIONS, 198 6.7 TREATMENT OF
INFLUENTIAL OBSERVATIONS, 199 PROBLEMS, 199 7. POLYNOMIAL REGRESSION
MODELS 201 7.1 INTRODUCTION, 201 7.2 POLYNOMIAL MODELS IN ONE VARIABLE,
201 7.2.1 BASIC PRINCIPLES, 201 CONTENTS 7.2.2 PIECEWISE POLYNOMIAL
FITTING (SPLINES), 207 7.2.3 POLYNOMIAL AND TRIGONOMETRIE TERMS, 213 7.3
NONPARAMETRIC REGRESSION, 214 7.3.1 KERNEL REGRESSION, 214 7.3.2 LOCALLY
WEIGHTED REGRESSION (LOESS), 215 7.3.3 FINAL CAUTIONS, 219 7.4
POLYNOMIAL MODELS IN TWO OR MORE VARIABLES, 220 7.5 ORTHOGONAL
POLYNOMIALS, 226 PROBLEMS, 231 INDICATOR VARIABLES 237 8.1 GENERAL
CONCEPT OF INDICATOR VARIABLES, 237 8.2 COMMENTS ON THE USE OF INDICATOR
VARIABLES, 249 8.2.1 INDICATOR VARIABLES VERSUS REGRESSION ON ALLOCATED
CODES, 249 8.2.2 INDICATOR VARIABLES AS A SUBSTITUTE FOR A QUANTITATIVE
REGRESSOR, 250 8.3 REGRESSION APPROACH TO ANALYSIS OF VARIANCE, 251
PROBLEMS, 256 VARIABLE SELECTION AND MODEL BUILDING 261 9.1
INTRODUCTION, 261 9.1.1 MODEL-BUILDING PROBLEM, 261 9.1.2 CONSEQUENCES
OF MODEL MISSPECIFICATION, 262 9.1.3 CRITERIA FOR EVALUATING SUBSET
REGRESSION MODELS, 265 9.2 COMPUTATIONAL TECHNIQUES FOR VARIABLE
SELECTION, 270 9.2.1 ALL POSSIBLE REGRESSIONS, 270 9.2.2 STEPWISE
REGRESSION METHODS, 277 9.3 STRATEGY FOR VARIABLE SELECTION AND MODEL
BUILDING, 283 9.4 CASE STUDY: GORMAN AND TOMAN ASPHALT DATA USING SAS,
286 PROBLEMS, 300 VALIDATION OF REGRESSION MODELS 305 10.1 INTRODUCTION,
305 10.2 VALIDATION TECHNIQUES, 306 10.2.1 ANALYSIS OF MODEL
COEFFICIENTS AND PREDICTED VALUES, 306 10.2.2 COLLECTING FRESH
DATA*CONFIRMATION RUNS, 308 CONTENTS IX 10.2.3 DATA SPLITTING, 310 10.3
DATA FROM PLANNED EXPERIMENTS, 318 PROBLEMS, 319 11. MULTICOLLINEARITY
323 11.1 INTRODUCTION, 323 11.2 SOURCES OF MULTICOLLINEARITY, 323 11.3
EFFECTS OF MULTICOLLINEARITY, 326 11.4 MULTICOLLINEARITY DIAGNOSTICS,
331 11.4.1 EXAMINATION OF THE CORRELATION MATRIX, 333 11.4.2 VARIANCE
INFLATION FACTORS, 334 11.4.3 EIGENSYSTEM ANALYSIS OF X'X, 335 11.4.4
OTHER DIAGNOSTICS, 340 11.4.5 SAS CODE FOR GENERATING MULTICOLLINEARITY
DIAGNOSTICS, 341 11.5 METHODS FOR DEALING WITH MULTICOLLINEARITY, 341
11.5.1 COLLECTING ADDITIONAL DATA, 341 11.5.2 MODEL RESPECIFICATION, 342
11.5.3 RIDGE REGRESSION, 344 11.5.4 PRINCIPAL-COMPONENT REGRESSION, 355
11.5.5 COMPARISON AND EVALUATION OF BIASED ESTIMATORS, 360 11.6 USING
SAS TO PERFORM RIDGE AND PRINCIPAL- COMPONENT REGRESSION, 363 PROBLEMS,
365 12. ROBUST REGRESSION 369 12.1 NEED FOR ROBUST REGRESSION, 369 12.2
M-ESTIMATORS, 372 12.3 PROPERTIES OF ROBUST ESTIMATORS, 384 12.3.1
BREAKDOWN POINT, 385 12.3.2 EFFICIENCY, 385 12.4 SURVEY OF OTHER ROBUST
REGRESSION ESTIMATORS, 386 12.4.1 HIGH-BREAKDOWN-POINT ESTIMATORS, 386
12.4.2 BOUNDED INFLUENCE ESTIMATORS, 389 12.4.3 OTHER PROCEDURES, 391
12.4.4 COMPUTING ROBUST REGRESSION ESTIMATORS, 392 PROBLEMS, 393 13.
INTRODUCTION TO NONLINEAR REGRESSION 397 13.1 LINEAR AND NONLINEAR
REGRESSION MODELS, 397 X CONTENTS 13.1.1 LINEAR REGRESSION MODELS, 397
13.1.2 NONLINEAR REGRESSION MODELS, 398 13.2 ORIGINS OF NONLINEAR
MODELS, 399 13.3 NONLINEAR LEAST SQUARES, 403 13.4 TRANSFORMATION TO A
LINEAR MODEL, 405 13.5 PARAMETER ESTIMATION IN A NONLINEAR SYSTEM, 408
13.5.1 LINEARIZATION, 408 13.5.2 OTHER PARAMETER ESTIMATION METHODS, 414
13.5.3 STARTING VALUES, 415 13.5.4 COMPUTER PROGRAMS, 416 13.6
STATISTICAL INFERENCE IN NONLINEAR REGRESSION, 417 13.7 EXAMPLES OF
NONLINEAR REGRESSION MODELS, 419 13.8 USING SAS PROC NLIN, 420 PROBLEMS,
423 14. GENERALIZED LINEAR MODELS 427 14.1 INTRODUCTION, 427 14.2
LOGISTIC REGRESSION MODELS, 428 14.2.1 MODELS WITH A BINARY RESPONSE
VARIABLE, 428 14.2.2 ESTIMATING THE PARAMETERS IN A LOGISTIC REGRESSION
MODEL, 430 14.2.3 INTERPRETATION OF THE PARAMETERS IN A LOGISTIC
REGRESSION MODEL, 433 14.2.4 STATISTICAL INFERENCE ON MODEL PARAMETERS,
436 14.2.5 DIAGNOSTIC CHECKING IN LOGISTIC REGFESSION, 444 14.2.6 OTHER
MODELS FOR BINARY RESPONSE DATA, 446 14.2.7 MORE THAN TWO CATEGORICAL
OUTCOMES, 447 14.3 POISSON REGRESSION, 449 14.4 THE GENERALIZED LINEAR
MODEL, 454 14.4.1 LINK FUNCTIONS AND LINEAR PREDICTORS, 455 14.4.2
PARAMETER ESTIMATION AND INFERENCE IN THE GLM, 456 14.4.3 PREDICTION AND
ESTIMATION WITH THE GLM, 460 14.4.4 RESIDUAL ANALYSIS IN THE GLM, 461
14.4.5 OVERDISPERSION, 464 PROBLEMS, 465 15. OTHER TOPICS IN THE USE OF
REGRESSION ANALYSIS 475 15.1 REGRESSION MODELS WITH AUTOCORRELATED
ERRORS, 475 15.1.1 SOURCE AND EFFECTS OF AUTOCORRELATION, 475 15.1.2
DETECTING THE PRESENCE OF AUTOCORRELATION, 476 15.1.3 PARAMETER
ESTIMATION METHODS, 479 CONTENTS XI 15.2 EFFECT OF MEASUREMENT ERRORS IN
THE REGRESSORS, 486 15.2.1 SIMPLE LINEAR REGRESSION, 486 15.2.2 BERKSON
MODEL, 488 15.3 INVERSE ESTIMATION*THE CALIBRATION PROBLEM, 488 15.4
BOOTSTRAPPING IN REGRESSION, 493 15.4.1 BOOTSTRAP SAMPLING IN
REGRESSION, 494 15.4.2 BOOTSTRAP CONFIDENCE INTERVALS, 494 15.5
CLASSIFICATION AND REGRESSION TREES (CART), 500 15.6 NEURAL NETWORKS,
502 15.7 DESIGNED EXPERIMENTS FOR REGRESSION, 505 PROBLEMS, 507 APPENDIX
A. STATISTICAL TABLES 511 APPENDIX B. DATA SETS FOR EXERCISES 529
APPENDIX C. SUPPLEMENTAL TECHNICAL MATERIAL 546 C.L BACKGROUND ON BASIC
TEST STATISTICS, 546 C.2 BACKGROUND FROM THE THEORY OF LINEAR MODELS,
548 C.3 IMPORTANT RESULTS ON SS R AND 55 RES , 552 C.4 GAUSS-MARKOV
THEOREM, VAR(E) = CR 2 I, 558 C.5 COMPUTATIONAL ASPECTS OF MULTIPLE
REGRESSION, 560 C.6 RESULT ON THE INVERSE OF A MATRIX, 562 C.7
DEVELOPMENT OF THE PRESS STATISTIC, 562 C.8 DEVELOPMENT OF S^, 564 C.9
OUTLIER TEST BASED ON FL-STUDENT, 565 CIO INDEPENDENCE OF RESIDUAIS AND
FITTED VALUES, 568 C.LL THE GAUSS-MARKOV THEOREM, VAR(E) = V, 569 C.12
BIAS IN MS RES WHEN THE MODEL IS UNDERSPECIFIED, 571 C.13 COMPUTATION OF
INFLUENCE DIAGNOSTICS, 572 C.14 GENERALIZED LINEAR MODELS, 573 APPENDIX
D. INTRODUCTION TO SAS 584 D.L BASIC DATA ENTRY, 584 D.2 CREATING
PERMANENT SAS DATA SETS, 589 D.3 IMPORTING DATA FROM AN EXCEL FILE, 590
D.4 OUTPUT COMMAND, 591 D.5 LOG FILE, 591" D.6 ADDING VARIABLES TO AN
EXISTING SAS DATA SET, 593 REFERENCES 594 INDEX 609 |
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| author | Montgomery, Douglas C. 1943- Peck, Elizabeth A. 1953- Vining, G. Geoffrey 1954- |
| author_GND | (DE-588)12861448X (DE-588)135772702 (DE-588)133013022 |
| author_facet | Montgomery, Douglas C. 1943- Peck, Elizabeth A. 1953- Vining, G. Geoffrey 1954- |
| author_role | aut aut aut |
| author_sort | Montgomery, Douglas C. 1943- |
| author_variant | d c m dc dcm e a p ea eap g g v gg ggv |
| building | Verbundindex |
| bvnumber | BV021579282 |
| callnumber-first | Q - Science |
| callnumber-label | QA278 |
| callnumber-raw | QA278.2 |
| callnumber-search | QA278.2 |
| callnumber-sort | QA 3278.2 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 234 SK 840 |
| ctrlnum | (OCoLC)61478843 (DE-599)BVBBV021579282 |
| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 4. ed. |
| format | Book |
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| genre_facet | Einführung |
| id | DE-604.BV021579282 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:41:08Z |
| indexdate | 2024-07-09T20:39:06Z |
| institution | BVB |
| isbn | 0471754951 9780471754954 |
| language | English |
| lccn | 2005054232 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014794977 |
| oclc_num | 61478843 |
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| owner | DE-703 DE-526 DE-11 DE-355 DE-BY-UBR |
| owner_facet | DE-703 DE-526 DE-11 DE-355 DE-BY-UBR |
| physical | XVI, 612 S. graph. Darst. |
| publishDate | 2006 |
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| publisher | Wiley-Interscience |
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| spelling | Montgomery, Douglas C. 1943- Verfasser (DE-588)12861448X aut Introduction to linear regression analysis Douglas C. Montgomery ; Elizabeth A. Peck ; G. Geoffrey Vining 4. ed. Hoboken, NJ Wiley-Interscience 2006 XVI, 612 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Analyse de régression Lineaire regressie gtt Regressieanalyse gtt Regression analysis Lineares Regressionsmodell (DE-588)4127971-2 gnd rswk-swf Lineare Regression (DE-588)4167709-2 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Lineare Regression (DE-588)4167709-2 s DE-604 Regressionsanalyse (DE-588)4129903-6 s 1\p DE-604 Lineares Regressionsmodell (DE-588)4127971-2 s 2\p DE-604 Peck, Elizabeth A. 1953- Verfasser (DE-588)135772702 aut Vining, G. Geoffrey 1954- Verfasser (DE-588)133013022 aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014794977&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 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Montgomery, Douglas C. 1943- Peck, Elizabeth A. 1953- Vining, G. Geoffrey 1954- Introduction to linear regression analysis Analyse de régression Lineaire regressie gtt Regressieanalyse gtt Regression analysis Lineares Regressionsmodell (DE-588)4127971-2 gnd Lineare Regression (DE-588)4167709-2 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4127971-2 (DE-588)4167709-2 (DE-588)4129903-6 (DE-588)4151278-9 |
| title | Introduction to linear regression analysis |
| title_auth | Introduction to linear regression analysis |
| title_exact_search | Introduction to linear regression analysis |
| title_exact_search_txtP | Introduction to linear regression analysis |
| title_full | Introduction to linear regression analysis Douglas C. Montgomery ; Elizabeth A. Peck ; G. Geoffrey Vining |
| title_fullStr | Introduction to linear regression analysis Douglas C. Montgomery ; Elizabeth A. Peck ; G. Geoffrey Vining |
| title_full_unstemmed | Introduction to linear regression analysis Douglas C. Montgomery ; Elizabeth A. Peck ; G. Geoffrey Vining |
| title_short | Introduction to linear regression analysis |
| title_sort | introduction to linear regression analysis |
| topic | Analyse de régression Lineaire regressie gtt Regressieanalyse gtt Regression analysis Lineares Regressionsmodell (DE-588)4127971-2 gnd Lineare Regression (DE-588)4167709-2 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Analyse de régression Lineaire regressie Regressieanalyse Regression analysis Lineares Regressionsmodell Lineare Regression Regressionsanalyse Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014794977&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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