Experimental design and data analysis for biologists / Gerry P. Quinn, Michael J. Keough.

Format:

Book

Author/Creator:

Quinn, G. P. (Gerald Peter), 1956-

Contributor:

Keough, Michael J.

Rudolph G. Schmieder Fund.

Language:

English

Subjects (All):

Biometry.

Physical Description:

xvii, 537 pages : illustrations ; 26 cm

Place of Publication:

Cambridge, UK ; New York : Cambridge University Press, 2002.

Summary:

An essential textbook for any biologist needing to design experiments, sampling programs or analyse the resulting data.

Contents:

1.1 Scientific method 1

1.2 Experiments and other tests 5

1.3 Data, observations and variables 7

1.4 Probability 7

1.5 Probability distributions 9

2 Estimation 14

2.1 Samples and populations 14

2.2 Common parameters and statistics 15

2.3 Standard errors and confidence intervals for the mean 17

2.4 Methods for estimating parameters 23

2.5 Resampling methods for estimation 25

2.6 Bayesian inference - estimation 27

3 Hypothesis testing 32

3.1 Statistical hypothesis testing 32

3.2 Decision errors 42

3.3 Other testing methods 45

3.4 Multiple testing 48

3.5 Combining results from statistical tests 50

3.6 Critique of statistical hypothesis testing 51

3.7 Bayesian hypothesis testing 54

4 Graphical exploration of data 58

4.1 Exploratory data analysis 58

4.2 Analysis with graphs 62

4.3 Transforming data 64

4.4 Standardizations 67

4.5 Outliers 68

4.6 Censored and missing data 68

4.7 General issues and hints for analysis 71

5 Correlation and regression 72

5.1 Correlation analysis 72

5.2 Linear models 77

5.3 Linear regression analysis 78

5.4 Relationship between regression and correlation 106

5.5 Smoothing 107

5.6 Power of tests in correlation and regression 109

5.7 General issues and hints for analysis 110

6 Multiple and complex regression 111

6.1 Multiple linear regression analysis 111

6.2 Regression trees 143

6.3 Path analysis and structural equation modeling 145

6.4 Nonlinear models 150

6.5 Smoothing and response surfaces 152

6.6 General issues and hints for analysis 153

7 Design and power analysis 155

7.1 Sampling 155

7.2 Experimental design 157

7.3 Power analysis 164

7.4 General issues and hints for analysis 171

8 Comparing groups or treatments - analysis of variance 173

8.1 Single factor (one way) designs 173

8.2 Factor effects 188

8.4 ANOVA diagnostics 194

8.5 Robust ANOVA 195

8.6 Specific comparisons of means 196

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

9 Multifactor analysis of variance 208

9.1 Nested (hierarchical) designs 208

9.2 Factorial designs 221

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

10 Randomized blocks and simple repeated measures: unreplicated two factor designs 262

10.1 Unreplicated two factor experimental designs 262

10.2 Analyzing RCB and RM designs 268

10.3 Interactions in RCB and RM models 274

10.5 Robust RCB and RM analyses 284

10.6 Specific comparisons 285

10.7 Efficiency of blocking (to block or not to block?) 285

10.8 Time as a blocking factor 287

10.9 Analysis of unbalanced RCB designs 287

10.10 Power of RCB or simple RM designs 289

10.11 More complex block designs 290

10.12 Generalized randomized block designs 298

10.13 RCB and RM designs and statistical software 298

10.14 General issues and hints for analysis 299

11 Split-plot and repeated measures designs: partly nested analyses of variance 301

11.1 Partly nested designs 301

11.2 Analyzing partly nested designs 309

11.4 Robust partly nested analyses 320

11.5 Specific comparisons 320

11.6 Analysis of unbalanced partly nested designs 322

11.7 Power for partly nested designs 323

11.8 More complex designs 323

11.9 Partly nested designs and statistical software 335

11.10 General issues and hints for analysis 337

12 Analyses of covariance 339

12.1 Single factor analysis of covariance (ANCOVA) 339

12.2 Assumptions of ANCOVA 348

12.3 Homogeneous slopes 349

12.4 Robust ANCOVA 352

12.5 Unequal sample sizes (unbalanced designs) 353

12.6 Specific comparisons of adjusted means 353

12.7 More complex designs 353

12.8 General issues and hints for analysis 357

13 Generalized linear models and logistic regression 359

13.1 Generalized linear models 359

13.2 Logistic regression 360

13.3 Poisson regression 371

13.4 Generalized additive models 372

13.5 Models for correlated data 375

13.6 General issues and hints for analysis 378

14 Analyzing frequencies 380

14.1 Single variable goodness-of-fit tests 381

14.2 Contingency tables 381

14.3 Log-linear models 393

14.4 General issues and hints for analysis 400

15 Introduction to multivariate analyses 401

15.1 Multivariate data 401

15.2 Distributions and associations 402

15.3 Linear combinations, eigenvectors and eigenvalues 405

15.4 Multivariate distance and dissimilarity measures 409

15.5 Comparing distance and/or 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

15.10 General issues and hints for analysis 423

16 Multivariate analysis of variance and discriminant analysis 425

16.1 Multivariate analysis of variance (MANOVA) 425

16.2 Discriminant function analysis 435

16.3 MANOVA vs discriminant function analysis 441

16.4 General issues and hints for analysis 441

17 Principal components and correspondence analysis 443

17.1 Principal components analysis 443

17.2 Factor analysis 458

17.3 Correspondence analysis 459

17.4 Canonical correlation analysis 463

17.5 Redundancy analysis 466

17.6 Canonical correspondence analysis 467

17.7 Constrained and partial "ordination" 468

17.8 General issues and hints for analysis 471

18 Multidimensional scaling and cluster analysis 473

18.1 Multidimensional scaling 473

18.2 Classification 488

18.3 Scaling (ordination) and clustering for biological data 491

18.4 General issues and hints for analysis 493

19 Presentation of results 494

19.1 Presentation of analyses 494

19.2 Layout of tables 497

19.3 Displaying summaries of the data 498

19.4 Error bars 504

19.5 Oral presentations 507.

Notes:

Includes bibliographical references (pages [511]-526) and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Rudolph G. Schmieder Fund.

ISBN:

0521811287

0521009766

OCLC:

47254190