Contemporary statistical models for the plant and soil sciences:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
2002
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 703 - 720 |
| Beschreibung: | XXII, 738 S. graph. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 1584881119 |
Internformat
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| 245 | 1 | 0 | |a Contemporary statistical models for the plant and soil sciences |c Oliver Schabenberger ; Francis J. Pierce |
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Datensatz im Suchindex
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| adam_text | CONTEMPORARY STATISTICAL MODELS FOR THE PLANT AND SOIL SCIENCES OLIVER
SCHABENBERGER FRANCIS J. PIERCE TECHNTSCHE UNIVERSITY FACHBEREICH TC * *
BIBLIOTHEK * SCHNITTSPAHNSTRAFTE 10 0-84287 DA R ME TADI !NV.-NR. CRC
PRESS BOCA RATON LONDON NEW YORK WASHINGTON, D.C. VII CONTENTS PREFACE
XV ABOUT THE AUTHORS . H XIX THE CD-ROM XXI 1 STATISTICAL MODELS 1 1.1
MATHEMATICAL AND STATISTICAL MODELS 2 1.2 FUNCTIONAL ASPECTS OF MODELS 4
1.3 THE INFERENTIAL STEPS * ESTIMATION AND TESTING 10 1.4 I-TESTS IN
TERMS OF STATISTICAL MODELS 14 1.5 EMBEDDING HYPOTHESES 18 1.6
HYPOTHESIS AND SIGNIFICANCE TESTING * INTERPRETATION OF THE P- VALUE 21
1.7 CLASSES OF STATISTICAL MODELS 25 1.7.1 THE BASIC COMPONENT EQUATION
25 1.7.2 LINEAR AND NONLINEAR MODELS 26 1.7.3 REGRESSION AND ANALYSIS OF
VARIANCE MODELS 28 1.7.4 UNIVARIATE AND MULTIVARIATE MODELS 29 1.7.5
FIXED, RANDOM, AND MIXED EFFECTS MODELS... 1 :30 1.7.6 GENERALIZED
LINEAR MODELS 32 1.7.7 ERRORS IN VARIABLE MODELS 34 2 DATA STRUCTURES 35
2.1 INTRODUCTION 36 2.2 CLASSIFICATION BY RESPONSE TYPE ? 37 2.3
CLASSIFICATION BY STUDY TYPE 39 2.4 CLUSTERED DATA 41 2.4.1 CLUSTERING
THROUGH HIERARCHICAL RANDOM PROCESSES 42 2.4.2 CLUSTERING THROUGH
REPEATED MEASUREMENTS 43 2.5 AUTOCORRELATED DATA 48 2.5.1 THE
AUTOCORRELATION FUNCTION 48 2.5.2 CONSEQUENCES OF IGNORING
AUTOCORRELATION 51 2.5.3 AUTOCORRELATION IN DESIGNED EXPERIMENTS 53 2.6
FROM INDEPENDENT TO SPATIAL DATA * A PROGRESSION OF CLUSTERING 54 VIII
LINEAR ALGEBRA TOOLS 59 3.1 INTRODUCTION 60 3.2 MATRICES AND VECTORS 61
3.3 BASIC MATRIX OPERATIONS *;-. 63 3.4 MATRIX INVERSION * REGULAR AND
GENERALIZED INVERSE 67 3.5 MEAN, VARIANCE, AND COVARIANCE OF RANDOM
VECTORS 70 3.6 THE TRACE AND EXPECTATION OF QUADRATIC FORMS 72 3.7 THE
MULTIVARIATE GAUSSIAN DISTRIBUTION 74 3.8 MATRIX AND VECTOR
DIFFERENTIATION 76 3.9 USING MATRIX ALGEBRA TO SPECIFY MODELS 77 3.9.1
LINEAR MODELS :~ 77 3.9.2 NONLINEAR MODELS 81 3.9.3 VARIANCE-COVARIANCE
MATRICES AND CLUSTERING 81 THE CLASSICAL LINEAR MODEL: LEAST SQUARES AND
ALTERNATIVES 85 4.1 INTRODUCTION . 87 4.2 LEAST SQUARES ESTIMATION AND
PARTITIONING OF VARIATION 95 4.2.1 THE PRINCIPLE 95 4.2.2 PARTITIONING
VARIABILITY THROUGH SUMS OF SQUARES 97 4.2.3 SEQUENTIAL AND PARTIAL SUMS
OF SQUARES AND THE SUM OF SQUARES REDUCTION TEST 98 4.3 FACTORIAL
CLASSIFICATION 104 4.3.1 THE MEANS AND EFFECTS MODEL 105 4.3.2 EFFECT
TYPES IN FACTORIAL DESIGNS... ?. 108 4.3.3 SUM OF SQUARES PARTITIONING
THROUGH CONTRASTS 110 4.3.4 EFFECTS AND CONTRASTS IN THE SAS SYSTEM 112
4.4 DIAGNOSING REGRESSION MODELS 119 4.4.1 RESIDUAL ANALYSIS 119 4.4.2
RECURSIVE AND LINEARLY RECOVERED ERRORS.... 122 4.4.3 CASE DELETION
DIAGNOSTICS 126 4.4.4 COLLINEARITY DIAGNOSTICS 130 4.4.5 RIDGE
REGRESSION TO COMBAT COLLINEARITY 133 4.5 DIAGNOSING CLASSIFICATION
MODELS : 137 4.5.1 WHAT MATTERS? 137 4.5.2 DIAGNOSING AND COMBATING
HETEROSCEDASTICITY 139 4.5.3 MEDIAN POLISHING OF TWO-WAY LAYOUTS 144 4.6
ROBUST ESTIMATION 152 4.6.1 II-ESTIMATION 153 4.6.2 M-ESTIMATION . 155
4.6.3 ROBUST REGRESSION FOR PREDICTION EFFICIENCY DATA 158 4.6.4
M-ESTIMATION IN CLASSIFICATION MODELS 164 4.7 NONPARAMETRIC REGRESSION
172 4.7.1 LOCAL AVERAGING AND LOCAL REGRESSION 174 4.7.2 CHOOSING THE
SMOOTHING PARAMETER 178 IX APPENDIX A ON CD-ROM A4.8 MATHEMATICAL
DETAILS A4.8.1 LEAST SQUARES A-3 A4.8.2 HYPOTHESIS TESTING IN THE
CLASSICAL LINEAR MODEL. A-5 A4.8.3 DIAGNOSTICS IN REGRESSION MODELS..S..
. A-8 A4.8.4 RIDGE REGRESSION ^4-77 A4.8.5 LI-ESTIMATION .4-7 7 A4.8.6
M-ESTIMATION A-13 A4.8.7 NONPARAMETRIC REGRESSION ^4-76 NONLINEAR MODELS
( 183 5.1 INTRODUCTION 185 5.2 MODELS AS LAWS OR TOOLS .. 189 5.3 LINEAR
POLYNOMIALS APPROXIMATE NONLINEAR MODELS : 193 5.4 FITTING A NONLINEAR
MODEL TO DATA 195 5.4.1 ESTIMATING THE PARAMETERS 195 5.4.2 .TRACKING
CONVERGENCE 201 5.4.3 STARTING VALUES 204 5.4.4 GOODNESS-OF-FIT .211 5.5
HYPOTHESIS TESTS AND CONFIDENCE INTERVALS 213 5.5.1 TESTING THE LINEAR
HYPOTHESIS 213 5.5.2 CONFIDENCE AND PREDICTION INTERVALS 221 5.6
TRANSFORMATIONS 223 5.6.1 TRANSFORMATION TO LINEARITY 223 5.6.2
TRANSFORMATION TO STABILIZE THE VARIANCE 226 5.7 PARAMETERIZATION OF
NONLINEAR MODELS 228 5.7.1 INTRINSIC AND PARAMETER-EFFECTS CURVATURE 229
5.7.2 REPARAMETERIZATION THROUGH DEFINING RELATIONSHIPS 234 5.8
APPLICATIONS 236 5.8.1 BASIC NONLINEAR ANALYSIS WITH THE SAS SYSTEM *
MITSCHERLICH S YIELD EQUATION 238 5.8.2 THE SAMPLING DISTRIBUTION OF
NONLINEAR ESTIMATORS * THE MITSCHERLICH EQUATION REVISITED 248 5.8.3
LINEAR-PLATEAU MODELS AND THEIR RELATIVES * A STUDY OF CORN YIELDS FROM
TENNESSEE 252 5.8.4 CRITICAL NO?, CONCENTRATIONS AS A FUNCTION OF
SAMPLING DEPTH * COMPARING JOIN-POINTS IN PLATEAU MODELS 259 5.8.5
FACTORIAL TREATMENT STRUCTURE WITH NONLINEAR RESPONSE 266 5.8.6 MODELING
HORMETIC DOSE RESPONSE THROUGH SWITCHING FUNCTIONS 273 5.8.7 MODELING A
YIELD-DENSITY RELATIONSHIP 285 5.8.8 WEIGHTED NONLINEAR LEAST SQUARES
ANALYSIS WITH HETEROSCEDASTIC ERRORS 293 APPENDIX A ON CD-ROM A5.9 FORMS
OF NONLINEAR MODELS A5.9.1 CONCAVE AND CONVEX MODELS, YIELD-DENSITY
MODELS A-20 A5.9.2 MODELS WITH SIGMOIDAL SHAPE, GROWTH MODELS : A-24
A5.10 MATHEMATICAL DETAILS AS.10.1 TAYLOR SERIES INVOLVING VECTORS ^4-27
A5.10.2 NONLINEAR LEAST SQUARES AND THE GAUSS-NEWTON ALGORITHM A-29
A5.10.3 NONLINEAR GENERALIZED LEAST SQUARES A-31 AS. 10.4 THE
NEWTON-RAPHSON ALGORITHM A-32 AS. 10.5 CONVERGENCE CRITERIA A-33 A5.10.6
HYPOTHESIS TESTING, CONFIDENCE AND PREDICTION INTERVALS .A-34
GENERALIZED LINEAR MODELS ^ 299 6.1 INTRODUCTION : V 301 6.2 COMPONENTS
OF A GENERALIZED LINEAR MODEL 304 6.2.1 RANDOM COMPONENT 305 6.2.2
SYSTEMATIC COMPONENT AND LINK FUNCTION 312 6.2.3 GENERALIZED LINEAR
MODELS IN THE SAS SYSTEM 320 6.3 GROUPED AND UNGROUPED DATA 328 6.4
PARAMETER ESTIMATION AND INFERENCE 331 6.4.1 SOLVING THE LIKELIHOOD
PROBLEM 331 6.4.2 TESTING HYPOTHESES ABOUT PARAMETERS AND THEIR
FUNCTIONS 333 6.4.3 DEVIANCE AND PEARSON S X 2 STATISTIC :?. 336 6.4.4
TESTING HYPOTHESES THROUGH DEVIANCE PARTITIONING 338 6.4.5 GENERALIZED R
2 MEASURES OF GOODNESS-OF-FIT 343 6.5 MODELING AN ORDINAL RESPONSE 344
6.5.1 CUMULATIVE LINK MODELS 346 6.5.2 SOFTWARE IMPLEMENTATION AND
EXAMPLE 349 6.6 OVERDISPERSION 356 6.7 APPLICATIONS 358 6.7.1
DOSE-RESPONSE AND LD 50 ESTIMATION IN A LOGISTIC REGRESSION MODEL 1359
6.7.2 BINOMIAL PROPORTIONS IN A RANDOMIZED BLOCK DESIGN * THE HESSIAN
FLY EXPERIMENT 365 6.7.3 GAMMA REGRESSION AND YIELD DENSITY MODELS 370
6.7.4 EFFECTS OF JUDGES EXPERIENCE ON BEAN CANNING QUALITY RATINGS 375
6.7.5 ORDINAL RATINGS IN A DESIGNED EXPERIMENT WITH FACTORIAL TREATMENT
STRUCTURE AND REPEATED MEASURES 379 6.7.6 LOG-LINEAR MODELING OF RATER
AGREEMENT... 383 6.7.7 MODELING THE SAMPLE VARIANCE OF SCAB INFECTION
393 6.7.8 A POISSON/GAMMA MIXING MODEL FOR OVERDISPERSED POPPY
COUNTS....397 APPENDIX A ON CD-ROM A6.8 MATHEMATICAL DETAILS AND SPECIAL
TOPICS A6.8.1 EXPONENTIAL FAMILY OF DISTRIBUTIONS A-36 A6.8.2 MAXIMUM
LIKELIHOOD ESTIMATION A-36 A6.8.3 ITERATIVELY REWEIGHTEDLEAST SQUARES
A-39 A6.8.4 HYPOTHESIS TESTING A-40 A6.8.5 FIELLER S THEOREM AND THE
VARIANCE OF A RATIO A-42 A6.8.6 OVERDISPERSION MECHANISMS FOR COUNTS
A-44 LINEAR MIXED MODELS FOR CLUSTERED DATA 403 7.1 INTRODUCTION 405 7.2
THE LAIRD-WARE MODEL 412 7.2.1 RATIONALE 412 7.2.2 THE TWO-STAGE CONCEPT
415 7.2.3 FIXED OR RANDOM EFFECTS 422 7.3 CHOOSING THE INFERENCE SPACE
425 7.4 ESTIMATION AND INFERENCE .-.... 430 7.4.1 MAXIMUM AND
RESTRICTED MAXIMUM LIKELIHOOD 432 7.4.2 ESTIMATEDGENERALIZEDLEAST
SQUARES 437 7.4.3 HYPOTHESIS TESTING 438 7.5 CORRELATIONS IN MIXED
MODELS 446 7.5.1 INDUCED CORRELATIONS AND THE DIRECT APPROACH 446 7.5.2
WITHIN-CLUSTER CORRELATION MODELS 450 7.5.3 SPLIT-PLOTS, REPEATED
MEASURES, AND THE HUYNH-FELDT CONDITIONS 461 7.6 APPLICATIONS 465 7.6.1
TWO-STAGE MODELING OF APPLE GROWTH OVER TIME 466 7.6.2 ON-FARM
EXPERIMENTATION WITH RANDOMLY SELECTED FARMS 474 7.6.3 NESTED ERRORS
THROUGH SUBSAMPLING 479 7.6.4 RECOVERY OF INTER-BLOCK INFORMATION IN
INCOMPLETE BLOCK DESIGNS 488 7.6.5 A SPLIT-STRIP-PLOT EXPERIMENT FOR
SOYBEAN YIELD 493 7.6.6 REPEATED MEASURES IN A COMPLETELY RANDOMIZED
DESIGN 504 7.6.7 A LONGITUDINAL STUDY OF WATER USAGE IN HORTICULTURAL
TREES 512 7.6.8 CUMULATIVE GROWTH OF MUSKMELONS IN SUBSAMPLING DESIGN
520 APPENDIX A ON CD-ROM A7.7 MATHEMATICAL DETAILS AND SPECIAL TOPICS
A7.7.1 HENDERSON S MIXED MODEL EQUATIONS A-52 A7.7.2 SOLUTIONS TO THE
MIXED MODEL EQUATIONS A-53 A7.7.3 LIKELIHOOD BASED ESTIMATION A-54
A7.7.4 ESTIMATED GENERALIZED LEAST SQUARES ESTIMATION A-58 A7.7.5
HYPOTHESIS TESTING ,4-60 A7.7.6 THE FIRST-ORDER AUTOREGRESSIVE MODEL
A-62 NONLINEAR MODELS FOR CLUSTERED DATA 525 8.1 INTRODUCTION 526 8.2
NONLINEAR AND GENERALIZED LINEAR MIXED MODELS 528 8.3 TOWARD AN
APPROXIMATE OBJECTIVE FUNCTION 529 8.3.1 THREE LINEARIZATIONS 531 8.3.2
LINEARIZATION IN GENERALIZED LINEAR MIXED MODELS 534 8.3.3 INTEGRAL
APPROXIMATION METHODS 535 8.4 APPLICATIONS 537 8.4.1 A NONLINEAR MIXED
MODEL FOR CUMULATIVE TREE BOLE VOLUME 539 8.4.2 POPPY COUNTS REVISITED *
A GENERALIZED LINEAR MIXED MODEL FOR OVERDISPERSED COUNT DATA 545 XII
8.4.3 REPEATED MEASURES WITH AN ORDINAL RESPONSE 551 APPENDIX A ON
CD-ROM 8.5 MATHEMATICAL DETAILS AND SPECIAL TOPICS 8.5.1 PA AND SS
LINEARIZATIONS .. A-64 8.5.2 GENERALIZED ESTIMATING EQUATIONS A-65
8.5.3 LINEARIZATION IN GENERALIZED LINEAR MIXED MODELS A-68 8.5.4
GAUSSIAN QUADRATURE .; A-69 STATISTICAL MODELS FOR SPATIAL DATA 561 9.1
CHANGING THE MINDSET ?. 563 9.1.1 SAMPLES OF SIZE ONE : 563 9.1.2 RANDOM
FUNCTIONS AND RANDOM FIELDS 565 9.1.3 TYPES OF SPATIAL DATA 567 9.1.4
STATIONARITY AND ISOTROPY * THE BUILT-IN REPLICATION MECHANISM OF RANDOM
FIELDS. 572 9.2 SEMIVARIOGRAM ANALYSIS AND ESTIMATION 577 9.2.1 ELEMENTS
OF THE SEMIVARIOGRAM 577 9.2.2 PARAMETRIC ISOTROPIC SEMIVARIOGRAM MODELS
581 9.2.3 THE DEGREE OF SPATIAL CONTINUITY (STRUCTURE) 585 9.2.4
SEMIVARIOGRAM ESTIMATION AND FITTING 587 9.3 THE SPATIAL MODEL 599 9.4
SPATIAL PREDICTION AND THE KRIGING PARADIGM 603 9.4.1 MOTIVATION OF THE
PREDICTION PROBLEM 603 9.4.2 THE CONCEPT OF OPTIMAL PREDICTION 607 9.4.3
ORDINARY AND UNIVERSAL KRIGING : 609 9.4.4 SOME NOTES ON KRIGING 613
9.4.5 - EXTENSIONS TO MULTIPLE ATTRIBUTES 619 9.5 SPATIAL REGRESSION
ARID CLASSIFICATION MODELS 625 9.5.1 RANDOM FIELD LINEAR MODELS 625
9.5.2 SOME PHILOSOPHICAL CONSIDERATIONS 628 9.5.3 PARAMETER ESTIMATION
629 9.6 AUTOREGRESSIVE MODELS FOR LATTICE DATA 632 9.6.1 THE
NEIGHBORHOOD STRUCTURE 632 9.6.2 FIRST-ORDER SIMULTANEOUS AND
CONDITIONAL MODELS 634 9.6.3 PARAMETER ESTIMATION 637 9.6.4 CHOOSING THE
NEIGHBORHOOD STRUCTURE 637 9.7 ANALYZING MAPPED SPATIAL POINT PATTERNS
638 9.7.1 INTRODUCTION 638 9.7.2 RANDOM, AGGREGATED, AND REGULAR
PATTERNS * THE NOTION OF COMPLETE SPATIAL RANDOMNESS .*. 640 9.7.3
TESTING THE CSR HYPOTHESIS IN MAPPED POINT PATTERNS 642 9.7.4
SECOND-ORDER PROPERTIES OF POINT PATTERNS 648 9.8 APPLICATIONS 650 9.8.1
EXPLORATORY TOOLS FOR SPATIAL DATA * DIAGNOSING SPATIAL AUTOCORRELATION
WITH MORAN S 1 651 9.8.2 MODELING THE SEMIVARIOGRAM OF SOIL CARBON 658
9.8.3 SPATIAL PREDICTION * KRIGING OF LEAD CONCENTRATIONS 669 XIII 9.8.4
SPATIAL RANDOM FIELD MODELS * COMPARING C/N RATIOS AMONG TILLAGE
TREATMENTS ..673 9.8.5 SPATIAL RANDOM FIELD MODELS * SPATIAL REGRESSION
OF SOIL CARBON ON SOIIN 679 9.8.6 SPATIAL GENERALIZED LINEAR MODELS *
SPATIAL TRENDS IN THE HESSIAN FLY EXPERIMENT 684 9.8.7 SIMULTANEOUS
SPATIAL AUTOREGRESSION * MODELING WIEBE S WHEAT YIELD DATA. 693 9.8.7
POINT PATTERNS * FIRST- AND SECOND-ORDER PROPERTIES OF A MAPPED
PATTERN?. 697 APPENDIX A ON CD-ROM * A9.9 MATHEMATICAL DETAILS AND
SPECIAL TOPICS GEOSTATISTICAL DATA A9.9.1 ESTIMATING THE EMPIRICAL
SEMIVARIOGRAM ,4-72 A9.9.2 PARAMETRIC FITTING OF THE SEMIVARIOGRAM A-75
A9.9.3 NONPARAMETRIC FITTING OF THE SEMIVARIOGRAM A-79 A9.9.4 SOLUTIONS
TO KRIGING EQUATIONS A-81 A9.9.5 IS KRIGING PERFECT INTERPOLATION? A-85
A9.9.6 BLOCK AND INDICATOR KRIGING A-91 SPATIAL RANDOM FIELD MODELS
A9.9.7 COMPOSITE LIKELIHOOD IN SPATIAL GENERALIZED LINEAR MODELS A-95
LATTICE DATA A9.9.8 MAXIMUM LIKELIHOOD ESTIMATION IN LATTICE MODELS A-97
A9.9.9 ARE AUTOREGRESSIVE MODELS STATIONARY? A-98 POINT PATTERNS A9.9.10
ESTIMATING FIRST- AND SECOND-ORDER PROPERTIES OF POINT PATTERNS..:
,4-700 A9.9.11 POINT PROCESS MODELS A-104 A9.9.12 SPECTRAL ANALYSIS OF
SPATIAL POINT PATTERNS ,4-709 SUPPLEMENTARY APPLICATION A9.9.13 POINT
PATTERNS * SPECTRAL ANALYSIS OF SEEDLING COUNTS A-112 BIBLIOGRAPHY 703
AUTHOR INDEX 721 SUBJECT INDEX ?. 725
|
| any_adam_object | 1 |
| author | Schabenberger, Oliver Pierce, Francis J. |
| author_facet | Schabenberger, Oliver Pierce, Francis J. |
| author_role | aut aut |
| author_sort | Schabenberger, Oliver |
| author_variant | o s os f j p fj fjp |
| building | Verbundindex |
| bvnumber | BV014085950 |
| callnumber-first | S - Agriculture |
| callnumber-label | SB91 |
| callnumber-raw | SB91 |
| callnumber-search | SB91 |
| callnumber-sort | SB 291 |
| callnumber-subject | SB - Plant Culture |
| classification_rvk | WC 7700 ZA 61000 |
| classification_tum | LAN 048f LAN 300f GEO 315f |
| ctrlnum | (OCoLC)150636819 (DE-599)BVBBV014085950 |
| dewey-full | 630/.727 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 630 - Agriculture and related technologies |
| dewey-raw | 630/.727 |
| dewey-search | 630/.727 |
| dewey-sort | 3630 3727 |
| dewey-tens | 630 - Agriculture and related technologies |
| discipline | Geowissenschaften Biologie Agrarwissenschaft Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Pflanzenbau |
| format | Book |
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| id | DE-604.BV014085950 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:57:24Z |
| institution | BVB |
| isbn | 1584881119 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009649029 |
| oclc_num | 150636819 |
| open_access_boolean | |
| owner | DE-M49 DE-BY-TUM DE-703 DE-11 DE-B768 |
| owner_facet | DE-M49 DE-BY-TUM DE-703 DE-11 DE-B768 |
| physical | XXII, 738 S. graph. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Schabenberger, Oliver Verfasser aut Contemporary statistical models for the plant and soil sciences Oliver Schabenberger ; Francis J. Pierce Boca Raton [u.a.] CRC Press 2002 XXII, 738 S. graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 703 - 720 Plants, Cultivated Statistical methods Soil science Statistical methods Pflanzenbaulehre (DE-588)4421347-5 gnd rswk-swf Bodenkunde (DE-588)4007379-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Biostatistik (DE-588)4729990-3 gnd rswk-swf Pflanzenbaulehre (DE-588)4421347-5 s Biostatistik (DE-588)4729990-3 s DE-604 Bodenkunde (DE-588)4007379-8 s Statistik (DE-588)4056995-0 s Pierce, Francis J. Verfasser aut HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009649029&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Schabenberger, Oliver Pierce, Francis J. Contemporary statistical models for the plant and soil sciences Plants, Cultivated Statistical methods Soil science Statistical methods Pflanzenbaulehre (DE-588)4421347-5 gnd Bodenkunde (DE-588)4007379-8 gnd Statistik (DE-588)4056995-0 gnd Biostatistik (DE-588)4729990-3 gnd |
| subject_GND | (DE-588)4421347-5 (DE-588)4007379-8 (DE-588)4056995-0 (DE-588)4729990-3 |
| title | Contemporary statistical models for the plant and soil sciences |
| title_auth | Contemporary statistical models for the plant and soil sciences |
| title_exact_search | Contemporary statistical models for the plant and soil sciences |
| title_full | Contemporary statistical models for the plant and soil sciences Oliver Schabenberger ; Francis J. Pierce |
| title_fullStr | Contemporary statistical models for the plant and soil sciences Oliver Schabenberger ; Francis J. Pierce |
| title_full_unstemmed | Contemporary statistical models for the plant and soil sciences Oliver Schabenberger ; Francis J. Pierce |
| title_short | Contemporary statistical models for the plant and soil sciences |
| title_sort | contemporary statistical models for the plant and soil sciences |
| topic | Plants, Cultivated Statistical methods Soil science Statistical methods Pflanzenbaulehre (DE-588)4421347-5 gnd Bodenkunde (DE-588)4007379-8 gnd Statistik (DE-588)4056995-0 gnd Biostatistik (DE-588)4729990-3 gnd |
| topic_facet | Plants, Cultivated Statistical methods Soil science Statistical methods Pflanzenbaulehre Bodenkunde Statistik Biostatistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009649029&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT schabenbergeroliver contemporarystatisticalmodelsfortheplantandsoilsciences AT piercefrancisj contemporarystatisticalmodelsfortheplantandsoilsciences |