The Design of Experiments: Statistical Principles for Practical ApplicationsIn all the experimental sciences, good design of experiments is crucial to the success of research. Well-planned experiments can provide a great deal of information efficiently and can be used to test several hypotheses simultaneously. This book is about the statistical principles of good experimental design and is intended for all applied statisticians and practising scientists engaged in the design, implementation and analysis of experiments. Professor Mead has written the book with the emphasis on the logical principles of statistical design and employs a minimum of mathematics. Throughout he assumes that the large-scale analysis of data will be performed by computers and he is thus able to devote more attention to discussions of how all of the available information can be used to extract the clearest answers to many questions. The principles are illustrated with a wide range of examples drawn from medicine, agriculture, industry and other disciplines. Numerous exercises are given to help the reader practise techniques and to appreciate the difference that good design of experiments can make to a scientific project. |
Contents
I | 3 |
III | 4 |
IV | 5 |
V | 7 |
VI | 9 |
VIII | 12 |
IX | 19 |
X | 23 |
LXXXVII | 283 |
LXXXVIII | 286 |
LXXXIX | 291 |
XC | 297 |
XCI | 301 |
XCII | 307 |
XCIV | 308 |
XCV | 309 |
XI | 24 |
XII | 27 |
XIII | 31 |
XV | 32 |
XVI | 33 |
XVII | 36 |
XVIII | 40 |
XIX | 42 |
XX | 45 |
XXII | 46 |
XXIII | 48 |
XXIV | 52 |
XXV | 58 |
XXVI | 64 |
XXVII | 66 |
XXVIII | 72 |
XXXI | 75 |
XXXII | 78 |
XXXIII | 80 |
XXXIV | 85 |
XXXV | 88 |
XXXVII | 90 |
XXXVIII | 96 |
XXXIX | 98 |
XL | 101 |
XLI | 102 |
XLII | 107 |
XLIV | 108 |
XLV | 109 |
XLVI | 112 |
XLVII | 117 |
XLVIII | 122 |
XLIX | 124 |
L | 129 |
LI | 130 |
LIII | 134 |
LIV | 142 |
LV | 150 |
LVI | 154 |
LVII | 163 |
LVIII | 172 |
LIX | 176 |
LX | 177 |
LXI | 181 |
LXII | 183 |
LXIII | 192 |
LXIV | 197 |
LXV | 201 |
LXVI | 208 |
LXVII | 214 |
LXIX | 216 |
LXX | 218 |
LXXI | 224 |
LXXII | 229 |
LXXIII | 231 |
LXXIV | 237 |
LXXV | 242 |
LXXVI | 245 |
LXXVII | 249 |
LXXVIII | 251 |
LXXIX | 254 |
LXXX | 261 |
LXXXI | 263 |
LXXXII | 266 |
LXXXIII | 270 |
LXXXIV | 274 |
LXXXV | 275 |
LXXXVI | 277 |
XCVI | 315 |
XCVII | 316 |
XCVIII | 326 |
XCIX | 331 |
C | 336 |
CI | 338 |
CII | 340 |
CIII | 345 |
CV | 352 |
CVI | 357 |
CVII | 361 |
CVIII | 366 |
CIX | 370 |
CX | 378 |
CXI | 382 |
CXIII | 384 |
CXIV | 389 |
CXV | 393 |
CXVI | 402 |
CXVII | 407 |
CXVIII | 414 |
CXIX | 417 |
CXX | 422 |
CXXII | 432 |
CXXIII | 439 |
CXXIV | 442 |
CXXV | 455 |
CXXVI | 459 |
CXXVII | 470 |
CXXIX | 471 |
CXXX | 473 |
CXXXI | 479 |
CXXXII | 485 |
CXXXIII | 491 |
CXXXIV | 496 |
CXXXV | 502 |
CXXXVI | 509 |
CXXXVII | 514 |
CXXXIX | 515 |
CXL | 517 |
CXLI | 521 |
CXLII | 526 |
CXLIII | 528 |
CXLIV | 530 |
CXLV | 533 |
CXLVI | 536 |
CXLVII | 538 |
CXLIX | 542 |
CL | 549 |
CLI | 551 |
CLII | 554 |
CLIII | 558 |
CLIV | 567 |
CLV | 572 |
CLVI | 577 |
CLVII | 579 |
CLVIII | 580 |
CLIX | 583 |
CLX | 587 |
CLXI | 588 |
CLXII | 595 |
CLXIII | 603 |
CLXIV | 605 |
CLXV | 609 |
615 | |
618 | |
Other editions - View all
The Design of Experiments: Statistical Principles for Practical Applications Roger Mead No preview available - 1988 |
Common terms and phrases
analysis of variance assumed assumptions block size blocking system calculated central composite design Chapter compared component confounded effects consider contrasts covariance cowpea degrees of freedom discussed drug eight example expected value experiment experimental design experimental units factorial structure fitted four blocks Graeco-Latin square groups important interaction effects interpretation Latin square least squares equations least squares estimates linear main effects main plot main unit maize mean yields normal distribution number of treatments observations occur orthogonal pairs parameters particular patient pattern possible precision quadratic random allocation randomised block design residual SS row and column sample set of treatments shown in Figure shown in Table split plot SS df standard errors statistical sums of squares three-factor interaction treatment combinations treatment comparisons treatment differences treatment effects treatment means two-factor interactions variables variance-covariance matrix variation