Experimental Design and Data Analysis for BiologistsAn essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software. |
Contents
II | 1 |
III | 5 |
IV | 7 |
VII | 9 |
VIII | 14 |
IX | 15 |
XI | 17 |
XIII | 23 |
LXXXIII | 289 |
LXXXIV | 290 |
LXXXV | 298 |
LXXXVIII | 299 |
XC | 301 |
XCI | 309 |
XCIII | 318 |
XCIV | 320 |
XIV | 25 |
XVII | 27 |
XVIII | 32 |
XIX | 42 |
XX | 45 |
XXI | 48 |
XXII | 50 |
XXIV | 51 |
XXVI | 54 |
XXVII | 58 |
XXVIII | 62 |
XXIX | 64 |
XXX | 67 |
XXXI | 68 |
XXXIII | 71 |
XXXV | 72 |
XXXVI | 77 |
XXXVII | 78 |
XXXVIII | 106 |
XL | 107 |
XLI | 109 |
XLII | 110 |
XLIV | 111 |
XLV | 143 |
XLVI | 145 |
XLVII | 150 |
XLVIII | 152 |
XLIX | 153 |
L | 155 |
LI | 157 |
LII | 164 |
LIII | 171 |
LV | 173 |
LVI | 188 |
LVII | 191 |
LVIII | 194 |
LIX | 195 |
LX | 196 |
LXI | 202 |
LXII | 203 |
LXIII | 204 |
LXIV | 206 |
LXVI | 208 |
LXVII | 221 |
LXVIII | 260 |
LXIX | 261 |
LXXII | 262 |
LXXIII | 268 |
LXXV | 274 |
LXXVI | 280 |
LXXVII | 284 |
LXXVIII | 285 |
LXXXI | 287 |
XCVI | 322 |
XCVIII | 323 |
CI | 335 |
CIII | 337 |
CV | 339 |
CVI | 348 |
CVII | 349 |
CVIII | 352 |
CIX | 353 |
CXIII | 357 |
CXV | 359 |
CXVI | 360 |
CXVII | 371 |
CXVIII | 372 |
CXIX | 375 |
CXX | 378 |
CXXI | 380 |
CXXII | 381 |
CXXV | 393 |
CXXVI | 400 |
CXXVIII | 401 |
CXXIX | 402 |
CXXX | 405 |
CXXXI | 409 |
CXXXII | 414 |
CXXXIII | 417 |
CXXXV | 418 |
CXXXVII | 423 |
CXXXVIII | 425 |
CXXXIX | 435 |
CXL | 441 |
CXLIII | 443 |
CXLIV | 458 |
CXLV | 459 |
CXLVI | 463 |
CXLVII | 466 |
CXLVIII | 467 |
CXLIX | 468 |
CLI | 471 |
CLIII | 473 |
CLIV | 488 |
CLV | 491 |
CLVII | 493 |
CLIX | 494 |
CLX | 497 |
CLXI | 498 |
CLXIII | 504 |
CLXIV | 507 |
CLXV | 510 |
CLXVI | 511 |
| 527 | |
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Experimental Design and Data Analysis for Biologists Gerry P. Quinn,Michael J. Keough Limited preview - 2002 |
Common terms and phrases
abundance analysis ANOVA ANOVA model assumptions Bayesian biological blocks calculated cell means Chapter coefficients combination comparing confidence intervals contrast correlation covariate data set density described dissimilarities Ecology eigenvectors equals zero error terms estimate example experiment experimental units F test F-ratio factor ANOVA Figure fixed factor full model habitat hypothesis testing Legendre levels of factor limpets linear models linear regression linear regression model log-linear models logistic regression main effects marginal means matrix methods MSResidual multiple multivariate normal distribution null hypotheses objects observations odds ratio outliers parameters partly nested patch plots pooling predicted predictor variables probability distribution quadrats random factors reduced model regression slope relationship replicate Residual response variable sample sizes sampling units scaling scatterplot Section significant simple single factor standard error statistical software Table tion transformed treatment Type I error usually values variation Y-values σ²