Applied Statistics in Biology: A Practical Guide Using Sas, R, and Jmp (Paperback, 2)

· 제목 : Applied Statistics in Biology: A Practical Guide Using Sas, R, and Jmp (Paperback, 2) 
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9780891183945
· 쪽수 : 880쪽
· 출판일 : 2025-09-23

Preface xiii

1 Introduction 1

Statistics, 1

Application of Statistics, 1

Scientific Method, 2

Statistical Null Hypothesis, 4

Type I Error (α), 5

Type II Error (β), 5

Power of the Test, 6
P-Value Misuse, 8

Effect Size, 9

Diagnostic Tests, 10

Bias, 12

Summary, 13

SAS Code, 14

R Code, 19

JMP Method, 21

References, 25

Additional Reading, 25

2 Data Management 27

Data Management Plan, 27

Organize Files, 28

Data Workbooks, 29

Backup, 33

Securing Data, 33

Data Analysis, 33

Data Preservation, 34

Data Sharing, 35

Summary, 35

Additional Reading, 35

3 Distributions 37

Measures of Central Tendency, 37

Dispersion, 38

Accuracy and Precision, 41

Normal Distribution, 42

Normal Probability Plot, 43

Measures of Departures from Normality, 44

Tests of Normality, 45

Comparing Distributions, 48

Comparing Two Mean Estimates, 50

Student’s t-Test, 50

Wald Z-Test, 54

Bootstrap, 54

Summary, 57

SAS Code, 58

R Code, 63

JMP Method, 68

References, 80

Additional Reading, 81

4 Goodness-of-fit 83

χ 2 Distribution, 83

Enumeration Data, 83

Two Cell Tests, 85

Sample Size to Differentiate Alternative Ratios, 87

Contingency Tests, Goodness-of-Fit, 88

Contingency Tests, No Expected Distribution, 89

Meta-Analysis, 92

Summary, 94

SAS Code, 95

R Code, 100

JMP Method, 106

References, 121

Additional Reading, 121

5 Variance Analyses?gaussian 123

Factors, 123

Experimental Unit, 124

Effect Types, 124

One-Factor Analysis, 125

Experimental Error, 126

F-Distribution, 127

Replication, 128

Randomized Complete Block, 129

Arrangement, 130

Variance Analysis, 132

Block: Fixed or Random Effect?, 133

Mixed Model Analysis, 134

REML Estimation, 135

Significance of Effects, 136

Generalized Linear Mixed Model, 137

Conditional and Marginal Models, 138

Covariance Structure, 139

Negative Variance Estimates, 140

Means Comparisons, 142

Contrasts, 143

Estimate of a Difference, 144

BLUE and BLUP Estimates, 145

Multiplicity Adjustment, 146

Letter Codes, 147

Test CV, 148

Power Analyses, 148

Summary, 149

SAS Code, 149

R Code, 164

JMP Method, 173

References, 187

Additional Reading, 187

6 Correlation and Regression 189

Rank Correlations, 191

Linear Regression, 192

Model I, 194

Model II, 194

Prediction of Y from X, 196

Broad and Narrow Inference, 197

Regression Through the Origin, 198

Inverse Prediction, 198

Transformations for Linear Regression, 199

Nonlinear Regression, 203

Dosage Response, 206

Segmented or Spline Regression, 208

Logistic Regression, 209

Creating Plots for Publication, 213

Summary, 214

SAS Code, 214

R Code, 226

JMP Method, 236

References, 277

Additional Reading, 277

7 Regression in Anova 279

Unequally Spaced or Unequally Balanced Treatments, 281

Dummy Variables, 284

Optimum Treatment Level, 286

Comparison of Regression Response, 287

Comparison of Responses, 289

Non-Gaussian Data, 290

Summary, 291

SAS Code, 292

R Code, 300

JMP Method, 307

References, 324

Additional Reading, 324

8 Checking Model Fit 325

Violation of Assumptions, 326

Fit the Model to the Data, 326

Checking Assumptions, 326

Residual Types, 327

Residual Adjustment, 327

Plots of Residuals, 328

Model Modifications, 335

Fit Statistics, 337

Chi-Square/DF, 339

Link Function, 340

Outliers and Influential Observations, 340

Influence Statistics for Generalized Models, 342

Pea Study, Epilogue, 344

Summary, 345

SAS Code, 346

R Code, 355

JMP Method, 359

References, 374

Additional Reading, 375

9 Non-gaussian Data 377

Denominator df, 378

Quantitative Data, 378

Count Data, 379

Zero-Inflated Models, 382

Proportion Data, Continuous, 383

Values of 0 and 1, 383

Proportion Data, Discrete, 384

Multinomial Data, 386

Ordinal Multinomial Analysis, 387

Nominal Multinomial Analysis, 390

Compositional Data, 392

Summary, 393

SAS Code, 394

R Code, 404

JMP Method, 410

References, 424

Additional Reading, 425

10 Error Control 427

Experimental Error, 428

Variation Within Experimental Units, 428

Heterogeneity Among Experimental Units, 431

Analysis of Covariance, 431

Heterogeneity Within a Study, 436

Minimizing Heterogeneity, 437

Post-hoc Detection of Heterogeneity, 438

Spatial Error?Covariance Adjustment, 442

Beyond the RCBD, 451

Latin Square, 451

Lattice Designs, 452

Balanced Lattice, 452

Partially Balanced Lattice, 453

Simple Lattice Repeated, 454

Rectangular Lattice, 454

α-Designs, 455

Augmented Designs, 456

Partially Replicated Designs, 456

Experimental Design Software, 459

Summary, 459

SAS Code, 460

R Code, 474

JMP Method, 482

References, 503

Additional Reading, 504

11 Factorial Experiments 507

Expected Mean Squares, 509

Estimation of Variance Components, 512

Subsampling, 513

Two-Factor Factorials, 515

Three-Factor Factorials, 515

Split-Plot, 516

Do Not Under- or Over-Specify, 518

Model Specification, 518

Bias Correction, 521

Split-Block, 523

Repeated Measures, 524

Correlated Errors Are Not Restricted to Time, 527

Selection of Covariance Structure, 527

Repeated Measures, Non-Gaussian, 529

No Convergence, 532

Adjusting for Baseline, 533

Combined Experiments, 535

Coefficients for Contrasts and Estimates, 539

Investigating Interactions, 542

Fixed, Random, or a Bit of Both?, 544

Summary, 545

SAS Code, 545

R Code, 571

JMP Method, 579

References, 609

Additional Reading, 610

12 Response Surface 613

First-Order Designs, 614

Second-Order Designs, 615

Central Composite Design, 615

Central Rotatable Composite Design, 616

Mixture and Double Mixture Designs, 618

Plotting Response Surfaces, 623

Hoerl and Spline Models, 624

Avoid Extrapolation, 624

Summary, 627

SAS Code, 627

R Code, 643

JMP Method, 649

References, 661

Additional Reading, 661

13 Multiple Regression 663

Linear Model, 663

Assumptions, 664

Variable Selection?Fixed Effect Models, 664

Variable Selection?Mixed Models, 666

Multimodel Inference, 668

Collinear Variables, 670

Variance Inflation Factor, 670

Collinearity Diagnostics, 671

Adjusting Collinear Variables, 672

Polynomial Models, 672

Prediction Models Involving Collinear Variables, 673

Cross-Validation, 673

Model Validation, 674

Latent Factor Regression, 675

Summary, 680

SAS Code, 681

R Code, 692

JMP Method, 697

References, 714

Additional Reading, 714

14 Multivariate Analyses 717

Analyses of Dependence, 717

Genotypic Correlations, 719

Path Analysis, 720

Analyses of Interdependence, 722

Assumptions, 723

Example Multivariate Dataset (Grin), 723

Dimension Reduction, 726

Value of Variables, 728

Number of Components/Factors, 729

Clustering, 733

Distance Measures, 735

Cluster Methods, 737

Number of Clusters, 739

Groupings Unknown, 740

Partialling Out, 741

Cluster Validation, 743

Groupings Known, 745

Canonical Correlation Analysis, 746

Canonical Discriminant Analysis, 746

Comparing Distance Matrices, 750

Summary, 752

SAS Code, 753

R Code, 768

JMP Method, 774

References, 798

Additional Reading, 799

15 G×e Analysis 801

Fixed or Random Environments?, 802

I. Univariate Models, 803

Mean-CV, 803

Regression Coefficient, 805

Regression Deviation, 806

Random Environment Effect, 807

Yield Stability Index, 810

Superiority Measure, 811

II. Multivariate Models, 812

Biplots, 816

Confidence Intervals, 820

AMMI or GGE?, 821

G×E Analyses-Summary, 822

SAS Code, 822

R Code, 837

JMP Method, 847

References, 863

Additional Reading, 864