Skip to main content Accessibility help
×
Home
Statistical Principles for the Design of Experiments
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 33
  • Export citation
  • Recommend to librarian
  • Buy the print book

Book description

This book is about the statistical principles behind the design of effective experiments and focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis. Emphasising the logical principles of statistical design, rather than mathematical calculation, the authors demonstrate how all 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 real experiments in medicine, industry, agriculture and many experimental disciplines. Numerous exercises are given to help the reader practise techniques and to appreciate the difference that good design can make to an experimental research project. Based on Roger Mead's excellent Design of Experiments, this new edition is thoroughly revised and updated to include modern methods relevant to applications in industry, engineering and modern biology. It also contains seven new chapters on contemporary topics, including restricted randomisation and fractional replication.

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Send to Kindle
  • Send to Dropbox
  • Send to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Contents

References
References
Bailey, R. A. (1982) The decomposition of treatment degrees of freedom in quantitative factorial experiments. Journal of the Royal Statistical Society, Series B, 44, 63–70.
Bartlett, M. S. (1978) Nearest neighbour models in the analysis of field experiments (with discussion). Journal of the Royal Statistical Society, Series B, 40, 147–174.
Besag, J. E. (1974) Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, Series B, 36, 192–236.
Besag, J. E. and Kempton, R. A. (1986) Analyis of field experiments using spatial statistics. Biometrics, 42, 231–251.
Bleasdale, J. K. A. (1967) Systematic designs for spacing experiments. Experimental Agriculture, 3, 73–85.
Box, G. E. P. and Draper, N. R. (1975) Robust designs. Biometrika, 62, 347–352.
Box, G. E. P. and Tidwell, P. W. (1962) Transformation of the independent variables. Technometrics, 4, 531–550.
Butler, N. A., Mead, R., Eskridge, K. M. and Gilmour, S. G. (2001) A general method of constructing E(s2)-optimal supersaturated designs. Journal of the Royal Statistical Society, Series B, 63, 621–632.
Bryan-Jones, J. and Finney, D. J. (1983) On an error in ‘Instruction to Authors’. Horticultural Science, 18, 279–282.
Carmer, S. G. and Jackobs, J. A. (1965) An exponential model for predicting optimum plant density and maximum corn yield. Agronomy Journal, 57, 241–244.
Cleaver, T. J., Greenwood, D. J. and Wood, J. T. (1970) Systematically arranged fertiliser experiments. Journal of Horticultural Science, 45, 457–469.
Cochran, W. G. and Cox, G. M. (1957) Experimental Designs, Second edition. New York: Wiley.
Cornell, J. A. (2002) Experiments with Mixtures, Third edition. New York: Wiley.
Corsten, L. C. A. (1958) Vectors, a tool in statistical regression theory. Mededelingen van de Land-bouwhogeschool te Wageningen, 58, 1–92.
Cox, D. R. (1958) Planning of Experiments. New York: Wiley.
Cullis, B. R. and Gleeson, A. C. (1989) Efficiency of neighbour analysis for replicated variety trials in Australia. Journal of Agricultural Science, Cambridge, 113, 233–239.
Cullis, B. R. and Gleeson, A. C. (1991) Spatial analysis of field experiments — an extension to two dimensions. Biometrics, 47, 1449–1460.
Curnow, R. N. (1961) Optimal programmes for varietal selection. Journal of the Royal Statistical Society, Series B, 23, 282–318.
Darby, L. A. and Gilbert, N. (1958) The Trojan square. Euphytica, 7, 183–188.
Davis, T. P. and Draper, N. R. (1995) A note on remnant three-level second order designs. Technical Report 954, Department of Statistics, University of Wisconsin-Madison.
Dorfman, R. (1943) The detection of defective members of large populations. Annals of Mathematical Statistics, 14, 436–440.
Dyke, G. V. and Shelley, C. F. (1976) Serial designs balanced for effects of neighbours on both sides. Journal of Agricultural Science, Cambridge, 87, 303–305.
Edmondson, R. N. (1994) Fractional factorial designs for factors with a prime number of quantitative levels. Journal of the Royal Statistical Society, Series B, 56, 611–622.
Edmondson, R. N. (1998) Trojan square and incomplete Trojan square designs for crop research. Journal of Agricultural Science, Cambridge, 131, 135–142.
Eskridge, K. M., Gilmour, S. G., Mead, R., Butler, N. A. and Travnicek, D. A. (2004) Large supersaturated designs. Journal of Statistical Computation and Simulation, 74, 525–542.
Finney, D. J. (1958) Statistical problems of plant selection. Bulletin of the International Statistical Institute, 36, 242–268.
Fisher, R. A. and Yates, F. (1963) Statistical Tables for Biological Agriculture and Medical Research. Edinburgh: Oliver & Boyd.
Francis, L. (1978) Experimental designs for the two-parameter exponential response curve. MSc dissertation, University of Reading.
Freeman, G. H. (1979) Some two-dimensional designs balanced for nearest neighbours. Journal of the Royal Statistical Society, Series B, 41, 88–95.
Gilmour, A., Cullis, B. and Verbyla, A. (1997) Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological and Environmental Statistics, 2, 269–293.
Gilmour, S. G. (2006) Response surface designs for experiments in bioprocessing. Biometrics, 62, 323–331.
Gilmour, S. G. (2006) Supersaturated designs in factor screening. In Screening (eds. S.M., Lewis and A. M., Dean), pp. 169–190. New York: Springer.
Gilmour, S. G. and Trinca, L. A. (2005) Fractional polynomial response surface models. Journal of Agricultural, Biological and Environmental Statistics, 10, 50–60.
Gleeson, A. C. and Cullis, B. R. (1987) Residual maximum likelihood (REML) estimation of a neighbour model for field experiments. Biometrics, 43, 277–287.
Gordon, T. and Foss, B. M. (1966) The role of stimulation in the delay of the onset of crying in the new-born infant. Journal of Experimental Psychology, 16, 79–81.
Green, P. J., Jennison, C. and Seheult, A. H. (1985) Analysis of field experiments by least squares smoothing. Journal of the Royal Statistical Society, Series B, 47, 299–315.
Hills, M. and Armitage, P. (1979) Two-period cross-over clinical trial. British Journal of Clinical Pharmacology, 8, 7–20.
Hozumi, K., Asahira, T. and Kira, T. (1972) Intraspecific competition among higher plants: VI. Effect of some growth factors on the process of competition. Journal of the Institute of Polytechnics of Osaka City University, D7, 15–28.
John, J. A. and Williams, E. R. (1995) Cyclic and Computer Generated Designs, Second edition. London: Chapman & Hall.
Kempthorne, O. (1952) The Design and Analysis of Experiments. New York: Wiley.
Kempton, R. A. and Howes, C. W. (1981) The use of neighbouring plot values in the analysis of variety trials. Applied Statistics, 30, 59–70.
Kerr, M. K. and Churchill, G. A. (2001) Statistical design and the analysis of gene expression microarray data. Genetics Research, 77, 123–128.
Kuipers, N. H. (1952) Variantie-Analyse. Statistica, 6, 149–194.
Lee, Y., Nelder, J. A. and Pawitan, Y. (2006) Generalized Linear Models with Random Effects. London: CRC Press.
McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, Second edition. London: Chapman & Hall.
Maindonald, J. H. and Cox, N. R. (1984) Use of statistical evidence in some recent issues of DSIR agricultural journals. New Zealand Journal of Agriculture, 27, 597–610.
Morse, P. M. and Thompson, B. K. (1981) Presentation of experimental results. Canadian Journal of Plant Science, 61, 799–802.
Nelder, J. A. (1962) New kinds of systematic design for spacing experiments. Biometrics, 18, 283–307.
Nelder, J. A. (1966) Inverse polynomials, a useful group of multi-factor response functions. Biometrics, 22, 128–141.
Nelder, J. A. (1991) Generalized linear models for enzyme kinetic data. Biometrics, 47, 1605–1615.
Nelder, J. A. and Mead, R. (1965) A simplex method for function minimization. The Computer Journal, 7, 308–313.
O'Neill, R. and Wetheril, G. B. (1971) The present state of multiple comparison methods (with discussion). Journal of the Royal Statistical Society, Series B, 33, 218–241.
Patterson, H. D. and Thompson, R. A. (1971) Recovery of inter-block information when block sizes are unequal. Biometrika, 5, 545–554.
Patterson, H. D. and Williams, E. R. (1976) A new class of resolvable incomplete block designs. Biometrika, 63, 83–92.
Pearce, S. C. (1963) The use and classification of non-orthogonal designs (with discussion). Journal of the Royal Statistical Society, Series B, 25, 353–377.
Pearce, S. C. (1975) Row-and-column designs. Applied Statistics, 24, 60–74.
Pocock, S. J. (1979) Allocation of patients to treatment in clinical trials. Biometrics, 35, 183–197.
Rayner, A. A. (1969) A First Course in Biometry for Agriculture Students. Pietermaritzburg: University of Natal Press.
Reid, D. (1972) The effects of long-term application of a wide range of nitrogen rates on the yields from perennial ryegrass swards with and without white clover. Journal of Agricultural Science, Cambridge, 79, 291–301.
Rojas, B. A. (1963) The San Cristobal design for fertiliser experiments. Proceedings of the International Society of Sugar Came Technologists, Mauritius.
Rojas, B. A. (1972) The orthogonalised San Cristobal design. Proceedings of the International Society of Sugar Came Technologists, Louisiana.
Royston, P. and Altman, D. G. (1994) Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with discussion). Applied Statistics, 43, 429–467.
Ruppert, D., Cressie, N. and Carroll, R. (1989) A transformation/weighting model for estimating Michaelis— Menten parameters. Biometrics, 45, 637–656.
Sobel, M. and Groll, P. A. (1959) Group testing to eliminate effectively all defectives in a binomial sample. Bell Systems Technology Journal, 38, 1179–1252.
Sparrow, P. E. (1979) Nitrogen response curves of spring barley. Journal of Agricultural Science, Cambridge, 92, 307–317.
Trinca, L. A. and Gilmour, S. G. (2000) An algorithm for arranging response surface designs in small blocks. Computational Statistics and Data Analysis, 33, 25–43. Erratum (2002), 40, 475.
Trinca, L. A. and Gilmour, S. G. (2001) Multi-stratum response surface designs. Technometrics, 43, 25–33.
Varnalis, A. I., Brennan, J. G., MacDougall, D. B. and Gilmour, S. G. (2004) Optimisation of high temperature puffing of potato cubes using response surface methodology. Journal of Food Engineering, 61, 153–163.
Whitehead, J. R. (1997) Sequential Clinical Trials, Second edition. New York: Wiley.
Wilkinson, G. N. (1970) A general recursive procedure for analysis of variance. Biometrika, 57, 19–46.
Wilkinson, G. N., Eckert, S. R., Hancock, T. W. and Mayo, O. (1983) Nearest neighbour (NN) analysis of field experiments (with discussion). Journal of the Royal Statistical Society, Series B, 45, 151–211.
Williams, R. M. (1952) Experimental designs for serially correlated observations. Biometrika, 49, 151–167.
Wit, E., Nobile, A. and Khanin, R. (2005) Near-optimal designs for dual channel microarray studies. Applied Statistics, 54, 817–830.
Yates, F. (1935) Complex experiments (with discussion). Supplement to the Journal of the Royal Statistical Society, 2, 181–247.
Yates, F. (1936) A new method of arranging variety trials involving a large number of varieties. Journal of Agricultural Science, Cambridge, 26, 424–455.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed