Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-22T18:52:52.108Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  11 May 2024

John H. Maindonald
Affiliation:
Statistics Research Associates, Wellington, New Zealand
W. John Braun
Affiliation:
University of British Columbia, Okanagan
Jeffrey L. Andrews
Affiliation:
University of British Columbia, Okanagan
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
A Practical Guide to Data Analysis Using R
An Example-Based Approach
, pp. 495 - 507
Publisher: Cambridge University Press
Print publication year: 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aitchison, J. (2003). The Statistical Analysis of Compositional Data. Blackburn Press. 416 pp. (cit. on p. 187).Google Scholar
Akaike, H. (1978). “On the likelihood of a time series model.” Journal of the Royal Statistical Society: Series D (The Statistician) 27.3-4, pp. 217235 (cit. on p. 223).Google Scholar
Aldrich (1995). “Correlations genuine and spurious in Pearson and Yule.” Statistical Science 10, pp. 364376 (cit. on p. 90).Google Scholar
Allison, D. B. et al. (2016). “Reproducibility: A tragedy of errors.” Nature 530.7588, pp. 2729. https://doi.org/10.1038/530027a (cit. on p. 79).CrossRefGoogle ScholarPubMed
Ambroise, C. and McLachlan, G. J. (2002). “Selection bias in gene extraction on the basis of microarray gene-expression data.” Proceedings of the National Academy of Sciences (PNAS) 99, pp. 62626266 (cit. on pp. 434, 462).CrossRefGoogle ScholarPubMed
Andersen, B. (1990). Methodological Errors in Medical Research: an Incomplete Catalogue. Blackwell Scientific. 288 pp. (cit. on p. 79).Google Scholar
Andrews, D. F. and Herzberg, A. M. (1985). Data. A Collection of Problems from Many Fields for the Student and Research Worker. Springer-Verlag (cit. on p. 319).Google Scholar
Andrews, J. L. et al. (2018). “teigen: An R package for model-based clustering and classification via the multivariate t distribution.” Journal of Statistical Software 83.1, pp. 132 (cit. on p. 418).CrossRefGoogle Scholar
Anon (2016). Cholera epidemics in Victorian London. www.thegazette.co.uk/all-notices/content/100519 (visited on 01/01/2021) (cit. on p. 257).Google Scholar
Aphalo, P. J. (2020). Learn R: As a Language. Chapman and Hall/CRC (cit. on p. 79).CrossRefGoogle Scholar
Barnett, V. (2002). Sample Survey: Principles & Methods. 3rd ed. Wiley-Blackwell. (cit. on p. 3).Google Scholar
Bartholemew, D. (2004). Measuring Intelligence. Facts and Fallacies. Cambridge University Press. 186 pp. (cit. on p. 368).CrossRefGoogle Scholar
Bates, D. M. and Watts, D. G. (1988). Nonlinear Regression Analysis and Its Applications. Wiley. 392 pp. (cit. on p. 203).CrossRefGoogle Scholar
Begley, C. G. (2013). “Reproducibility: Six red flags for suspect work.” Nature 497.7450, pp. 433434 (cit. on p. 76).CrossRefGoogle ScholarPubMed
Begley, C. G. and Ellis, L. M. (2012). “Drug development: Raise standards for preclinical cancer research.” Nature 483.7391, pp. 531533 (cit. on p. 76).CrossRefGoogle ScholarPubMed
Belson, W. A. (1959). “Matching and prediction on the principle of biological classification.” Applied Statistics 8, pp. 6575 (cit. on p. 373).CrossRefGoogle Scholar
Benjamin, D. J. et al. (2018). “Redefine statistical significance.” Nature Human Behaviour 2.1, pp. 610 (cit. on p. 78).CrossRefGoogle ScholarPubMed
Berk, R. A. (2008). Statistical Learning from a Regression Perspective. Springer. 360 pp. (cit. on pp. 395, 462).Google Scholar
Berkson, J. (1942). “Tests of significance considered as evidence.” Journal of the American Statistical Association 37.219, pp. 325335 (cit. on p. 58).CrossRefGoogle Scholar
Bezdek, J. C., Ehrlich, R., and Full, W. (1984). “FCM: The fuzzy c-means clustering algorithm.” Computers & Geosciences 10.2-3, pp. 191203 (cit. on p. 416).CrossRefGoogle Scholar
Bhaskaran, K. and Smeeth, L. (2014). “What is the difference between missing completely at random and missing at random?International Journal of Epidemiology 43.4, pp. 13361339. https://doi.org/10.1093/ije/dyu080. https://academic.oup.com/ije/article-pdf/43/4/1336/9727786/dyu080.pdf (cit. on p. 457).CrossRefGoogle ScholarPubMed
Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). “Sex bias in graduate admissions: data from Berkeley.” Science 187, pp. 398403 (cit. on p. 89).CrossRefGoogle ScholarPubMed
Bioconductor Open Source Software for Bioinformatics (2022). http://bioconductor.org/ (cit. on p. 462).Google Scholar
Blackwell, M., Honaker, J., and King, G. (2017). “A unified approach to measurement error and missing data: overview and applications.” Sociological Methods & Research 46.3, pp. 303341. https://doi.org/10.1177/0049124115585360 (cit. on pp. 195, 461).CrossRefGoogle Scholar
Blake, C. and Merz, C. (1998). UCI Repository of Machine Learning Databases. www.ics.uci.edu/~mlearn/MLRepository.html (cit. on p. 374).Google Scholar
Bland, M. and Altman, D. (2005). “Do the left-handed die young?Significance 2, pp. 166170 (cit. on pp. 196, 290).Google Scholar
Bolker, B. M. (2008). Ecological Models and Data in R. Princeton University Press. 408 pp. (cit. on pp. 137, 203).Google Scholar
Boot, H. M. and Maindonald, J. (2008). “New estimates of age- and sex-specific earnings and the male–female earnings gap in the British cotton industry, 1833–1906.” Economic History Review 61, pp. 380408 (cit. on p. 242).CrossRefGoogle Scholar
Bowman, A. W. et al. (2019). “Graphics for uncertainty.” Journal of the Royal Statistical Society, Series A (Statistics in Society) 182. Pt 2, pp. 40318 (cit. on p. 80).CrossRefGoogle Scholar
Box, G. E. P. and Cox, D. R. (1964). “An analysis of transformations (with discussion).” Journal of the Royal Statistical Society B 26, pp. 211252 (cit. on p. 113).CrossRefGoogle Scholar
Box, G. E., Hunter, J. S., and Hunter, W. G. (2005). “Statistics for experimenters.” In Wiley Series in Probability and Statistics. Wiley (cit. on p. 123).Google Scholar
Braun, W. J. (2012). “Naive analysis of variance.” Journal of Statistics Education 20.2 (cit. on p. 123).Google Scholar
Braun, W. J. and Murdoch, D. J. (2021). A First Course in Statistical Programming with R. 3rd ed. Cambridge University Press. 280 pp. (cit. on p. 79).CrossRefGoogle Scholar
Breheny, P. J. (2019). “Marginal false discovery rates for penalized regression models.” Biostatistics 20.2, pp. 299314. https://academic.oup.com/biostatistics/article/20/2/299/4840255 (cit. on p. 186).CrossRefGoogle ScholarPubMed
Breheny, P. and Huang, J. (2011). “Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection.” Annals of Applied Statistics. www.ncbi.nlm.nih.gov/pmc/articles/PMC3212875/ (cit. on pp. 186, 191).Google Scholar
Breiman, L. (2001). “Statistical modeling: The two cultures.” Statistical Science 16, pp. 199215 (cit. on p. 137).CrossRefGoogle Scholar
Brillinger, D. R. (2002). “John W. Tukey: His life and professional contributions.” Annals of Statistics, pp. 15351575 (cit. on p. xiv).Google Scholar
Brockwell, P. and Davis, R. A. (2002). Introduction to Time Series and Forecasting. 2nd ed. Springer (cit. on p. 314).CrossRefGoogle Scholar
Burns, N. R. et al. (1999). “Effects of car window tinting on visual performance: a comparison of elderly and young drivers.” Ergonomics 42, pp. 428443 (cit. on p. 344).CrossRefGoogle Scholar
Bussolari, S. (1987). “Human factors of long-distance human-powered aircraft flights.” Human Power 5, pp. 812 (cit. on p. 360).Google Scholar
Button, K. S. et al. (2013). “Power failure: Why small sample size undermines the reliability of neuroscience.” Nature Reviews Neuroscience 14.5, pp. 365376. https://doi.org/10.1038/nrn3475. www.projectimplicit.net/nosek/papers/BIMNFRM2013.pdf (cit. on pp. 55, 56, 79).CrossRefGoogle ScholarPubMed
Camerer, C. F. et al. (2016). “Evaluating replicability of laboratory experiments in economics.” Science 351.6280, pp. 14331436. http://science.sciencemag.org/content/351/6280/1433.full.pdf. https://authors.library.caltech.edu/64988/1/aaf0918-Camerer-SM.pdf (cit. on p. 76).CrossRefGoogle ScholarPubMed
Carroll, R. (2004). Measuring Diet. Texas A & M Distinguished Lecturer series (cit. on p. 192).Google Scholar
Carroll, R. J., Ruppert, D., and Stefanski, L. A. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. 2nd ed. Chapman and Hall/CRC. 484 pp. (cit. on pp. 193, 203).CrossRefGoogle Scholar
Celeux, G. and Govaert, G. (1992). “A classification EM algorithm for clustering and two stochastic versions.” Computational Statistics and Data Analysis 14.3, pp. 315332 (cit. on p. 422).CrossRefGoogle Scholar
Chadwick, E. (1842). Report on the Sanitary Condition of the Labouring Population of Great Britain. W. Clowes (cit. on p. 257).Google Scholar
Chalmers, I. and Altman, D. G. (1995). Systematic Reviews. BMJ Publishing Group, London. 119 pp. (cit. on p. 371).Google Scholar
Chambers, J. M. (2008). Software for Data Analysis: Programming ’with R. Springer. 516 pp. (cit. on pp. xvi, 79).CrossRefGoogle Scholar
Chang, W. (2013). R Graphics Cookbook. 1st ed. O’Reilly (cit. on p. 80).Google Scholar
Charig, C. R. (1986). “Comparison of treatment of renal calculi by operative surgery, percutaneous nephrolithotomy, and extracorporeal shock wave lithotripsy.” British Medical Journal 292, pp. 879882 (cit. on p. 19).CrossRefGoogle Scholar
Chatfield, C. (2003). The Analysis of Time Series: an Introduction. 6th ed. Chapman and Hall. 352 pp. (cit. on p. 314).CrossRefGoogle Scholar
Christie, M. (2000). The Ozone Layer: a Philosophy of Science Perspective. Cambridge University Press (cit. on p. 243).Google Scholar
Chu, I. et al. (1988). “Reproduction study of toxaphene in the rat.” Journal of Environmental Science and Health Part B. Pesticides and Food Contamination 23, pp. 101126 (cit. on p. 139).CrossRefGoogle ScholarPubMed
Clarke, D. (1968). Analytical Archaeology. Methuen. 684 pp. (cit. on p. 137).Google Scholar
Cleveland, W. S. (1981). “LOWESS: a program for smoothing scatterplots by robust locally weighted regression.” The American Statistician 35, p. 54 (cit. on p. 237).CrossRefGoogle Scholar
Clutton-Brock, T. H. et al. (1999). “Selfish sentinels in cooperative mammals.” Science pp. 16401644 (cit. on p. 242).CrossRefGoogle ScholarPubMed
Clyde, M. et al. (2022). “An introduction to Bayesian thinking. A companion to the Statistics with R course.” https://statswithr.github.io/book/ (cit. on p. 62).Google Scholar
Cochran, W. G. and Cox, G. M. (1957). Experimental Designs. 2nd ed. Wiley. 640 pp. (cit. on p. 369).Google Scholar
Cohen, P. (1996). “Pain discriminates between the sexes.” New Scientist 2054, p. 16 (cit. on p. 196).Google Scholar
Coleman, T. (2019). “Causality in the time of cholera: John Snow as a prototype for causal inference.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3262234 (cit. on p. 257).Google Scholar
Collett, D. (2014). Modelling Survival Data in Medical Research. 3rd ed. Chapman and Hall. 548 pp. (cit. on p. 290).Google Scholar
Cook, D. and Swayne, D. F. (2007). Interactive and Dynamic Graphics for Data Analysis. Springer (cit. on p. 462).CrossRefGoogle Scholar
Cook, R. D. and Weisberg, S. (1999). Applied Regression Including Computing and Graphics. Wiley. 632 pp. (cit. on pp. 171, 202).CrossRefGoogle Scholar
Cowpertwait, P. S. P. and Metcalfe, A. V. (2009). Introductory Time Series with R. Springer (cit. on p. 314).Google Scholar
Cox, D. R. (1958). Planning of Experiments. Wiley. 320 pp. (cit. on pp. 79, 240, 371).Google Scholar
Cox, D. R. (2006). Principles of Statistical Inference. Cambridge University Press (cit. on p. xiv).CrossRefGoogle Scholar
Cox, D. R. and Reid, N. (2000). Theory of the Design of Experiments. Chapman and Hall. 326 pp. (cit. on pp. 240, 371).CrossRefGoogle Scholar
Cox, D. R. and Wermuth, N. (1996). Multivariate Dependencies: Models, Analysis and Interpretation. Chapman and Hall. 272 pp. (cit. on pp. 30, 203).Google Scholar
Cox, T. F. and Cox, M. A. A. (2000). Multidimensional Scaling. 2nd ed. Chapman and Hall/CRC. 328 pp. (cit. on p. 408).Google Scholar
Cunningham, S. (2021). Causal Inference. Yale University Press. https://mixtape.scunning.com/index.html (visited on 03/28/2022) (cit. on pp. 159, 202, 462).Google Scholar
Dalgaard, P. (2008). Introductory Statistics with R. 2nd ed. Springer. 364 pp. (cit. on p. 79).CrossRefGoogle Scholar
Daniels, M., Devlin, B., and Roeder, K. (1997). “Of genes and IQ.” In Intelligence, Genes and Success. Ed. by Devlin, B., Fienberg, S., and Roeder, K.. Chapter 3. Springer (cit. on p. 368).Google Scholar
Dann, C. (2022). “Sewage, water and waste – stinking cities.” In Te Ara – the Encyclopedia of New Zealand. New Zealand Government. https://teara.govt.nz/en/zoomify/24431/dunedin-renamed-stinkapool (visited on 02/20/2022) (cit. on p. 260).Google Scholar
Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press. 594 pp. (cit. on p. 121).CrossRefGoogle Scholar
Dehejia, R. H. and Wahba, S. (1999). “Causal effects in non-experimental studies: re-evaluating the evaluation of training programs.” Journal of the American Statistical Association 94, pp. 10531062 (cit. on pp. 445, 449).CrossRefGoogle Scholar
Diedenhofen, B. and Musch, J. (2015). “cocor: A comprehensive solution for the statistical comparison of correlations.” PloS one 10.4, e0121945 (cit. on p. 95).CrossRefGoogle ScholarPubMed
Diggle, P. J. et al. (2002). Analysis of Longitudinal Data. 2nd ed. Clarendon Press (cit. on pp. 347, 359, 371).CrossRefGoogle Scholar
Dobson, A. J. and Barnett, A. (2008). An Introduction to Generalized Linear Models. Chapman and Hall/CRC. 320 pp. (cit. on p. 289).CrossRefGoogle Scholar
Donner, A. and Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Wiley. 194 pp. (cit. on p. 3).Google Scholar
Donohue, J. J. and Levitt, S. D. (May 2019). The Impact of Legalized Abortion on Crime over the Last Two Decades. Working Paper 25863.CrossRefGoogle Scholar
National Bureau of Economic Research. https://doi.org/10.3386/w25863. www.nber.org/papers/w25863 (cit. on p. 202).CrossRefGoogle Scholar
Doorn, J. van et al. (2021). “The JASP guidelines for conducting and reporting a Bayesian analysis.” Psychonomic Bulletin & Review 28.3, pp. 813826 (cit. on p. 137).CrossRefGoogle ScholarPubMed
Edwards, D. (2000). Introduction to Graphical Modelling. 2nd ed. Springer. 335 pp. (cit. on p. 203).CrossRefGoogle Scholar
Efron, B. and Tibshirani, R. (1993). An Introduction to the Bootstrap. Chapman and Hall. 456 pp. (cit. on p. 69).CrossRefGoogle Scholar
Efron, B. et al. (2003). Least Angle Regression. www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf (cit. on p. 186).Google Scholar
Ellenberg, J. (2015). How Not To Be Wrong. 1st ed. Penguin Books (cit. on p. 80).Google Scholar
Errington, T. (2021). Replication Study Results. https://osf.io/e81xl/wiki/home/ (visited on 12/07/2021) (cit. on p. 77).Google Scholar
Errington, T. M. et al. (2021). “Investigating the replicability of preclinical cancer biology.” Elife 10, e71601 (cit. on p. 77).Google ScholarPubMed
Eubank, R. L. (1999). Nonparametric Regression and Spline Smoothing. 2nd ed. Marcel Dekker. 360 pp. (cit. on p. 240).CrossRefGoogle Scholar
Ezzet, F. and Whitehead, J. (1991). “A random effects model for ordinal responses from a crossover trial.” Statistics in Medicine 10, pp. 901907 (cit. on p. 278).CrossRefGoogle ScholarPubMed
Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and Its Applications. Chapman and Hall/CRC. 360 pp. (cit. on p. 237).Google Scholar
Faraway, J. J. (2014). Linear Models with R. 2nd ed. Taylor & Francis Ltd. 286 pp. (cit. on p. 202).Google Scholar
Faraway, J. J. (2016). Extending the Linear Model with R. 2nd ed. Taylor & Francis Inc. 413 pp. (cit. on pp. 240, 289).Google Scholar
Farmer, C. (2005). “Another look at Meyer and Finney’s ‘Who wants airbags?”’ Chance 19, pp. 1522 (cit. on pp. 23, 24).CrossRefGoogle Scholar
Fisher, R. A. (1926). “The arrangement of field experiments.” Journal of Ministry of Agriculture of Great Britain 33, pp. 503513 (cit. on p. 56).Google Scholar
Fisher, R. A. (1935). The Design of Experiments. (7th ed, 1960). Oliver and Boyd (cit. on pp. 57, 371).Google Scholar
Follett, P. A. and Neven, L. G. (2006). “Current trends in quarantine entomology.” Annual Review of Entomology 51.1, pp. 359385. https://doi.org/10.1146/annurev.ento.49.061802.123314 (cit. on p. 350).CrossRefGoogle ScholarPubMed
Follett, P. A., Manoukis, N. C., and Mackey, B. (2018). “Comparative cold tolerance in Ceratitis capitata and Zeugodacus cucurbitae (Diptera: Tephritidae).” Journal of Economic Entomology 111.6, pp. 26322636 (cit. on p. 350).Google Scholar
Fox, J. and Weisberg, S. (2018). An R Companion to Applied Regression. Sage Publications. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion (cit. on pp. 176, 202).Google Scholar
Franco-Watkins, A., Derks, P., and Dougherty, M. (2003). “Reasoning in the Monty Hall problem: Examining choice behaviour and probability judgements.” Thinking & Reasoning 9.1, pp. 6790 (cit. on p. 142).CrossRefGoogle Scholar
Frazer, K. A. (2012). “Decoding the human genome.” Genome Research 22.9, pp. 15991601 (cit. on p. 462).CrossRefGoogle Scholar
Friedlingstein, P. et al. (2022). “Global carbon budget 2021.” Earth System Science Data 14.4, pp. 19172005 (cit. on p. 471).CrossRefGoogle Scholar
Galili, T. (2015). Tutorials for learning R. (Accessed on 02/03/2017) (cit. on p. 79).Google Scholar
Gaver, D. P. et al. (1992). Combining Information: Statistical Issues and Opportunities for Research. National Research Council, National Academy Press. 234 pp. (cit. on pp. 370, 371).Google Scholar
Gelman, A. and Carlin, J. (2014). “Beyond power calculations assessing type S (sign) and type M (magnitude) errors.” Perspectives on Psychological Science 9.6, pp. 641651 (cit. on p. 56).CrossRefGoogle ScholarPubMed
Gelman, A. and Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. 648 pp. (cit. on p. 371).CrossRefGoogle Scholar
Gelman, A. B. et al. (2003). Bayesian Data Analysis. 2nd ed. Chapman and Hall/CRC. 690 pp. (cit. on p. 80).CrossRefGoogle Scholar
Gelman, A., Hill, J., and Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press (cit. on p. 202).CrossRefGoogle Scholar
Gigerenzer, G. (1998). “We need statistical thinking, not statistical rituals.” Behavioural and Brain Sciences 21, pp. 199200 (cit. on p. 79).CrossRefGoogle Scholar
Gigerenzer, G. (2002). Reckoning with Risk: Learning to Live with Uncertainty. Penguin Books. 320 pp. (cit. on p. 80).Google Scholar
Gigerenzer, G. et al. (1989). The Empire of Chance. Cambridge University Press. 360 pp. (cit. on pp. xv, 80).CrossRefGoogle Scholar
Gihr, M. and Pilleri, G. (1969). “Anatomy and biometry of Stenella and Delphinus.” In Investigations on Cetacea. Ed. by Pilleri, G.. Hirnanatomisches Institute der Universität Bern (cit. on p. 115).Google Scholar
Glass, T. A. et al. (2013). “Causal inference in public health.” Annual Review of Public Health 34, pp. 6175 (cit. on pp. 161, 444).CrossRefGoogle ScholarPubMed
Goldstein, H. (2010). Multilevel Statistical Models. 4th ed. John Wiley & Sons. 384 pp. (cit. on p. 371).CrossRefGoogle Scholar
Golub, T. R. et al. (1999). “Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring.” Science 286, pp. 531537 (cit. on p. 433).CrossRefGoogle ScholarPubMed
Gordon, A. D. (1999). Classification. 2nd ed. Chapman and Hall/CRC. 272 pp. (cit. on p. 408).CrossRefGoogle Scholar
Gordon, N. C. et al. (1995). “Enhancement of morphine analgesia by the GABAB agonist baclofen.” Neuroscience 69, pp. 345349 (cit. on p. 196).CrossRefGoogle ScholarPubMed
Gordon, W. (1894). Our Country’s Birds and How to Know Them. Day and Son (cit. on p. 5).Google Scholar
Gourieroux, C. (1997). ARCH Models and Financial Applications. Springer. 229 pp. (cit. on p. 314).CrossRefGoogle Scholar
Grasso, L. C. et al. (2008). “Microarray analysis identifies candidate genes for key roles in coral development.” BMC Genomics 9, p. 540 (cit. on p. 126).CrossRefGoogle ScholarPubMed
Greenland, S. et al. (2016). “Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations.” European Journal of Epidemiology 31.4, pp. 337350. https://doi.erg/10.1007/s10654-016-0149-3 (cit. on p. 79).CrossRefGoogle Scholar
Greenwood, M. and Woods, H. M. (1919). “A report on the incidence of industrial accidents upon individuals with special reference to multiple accidents.” Reports of the Industrial Fatigue Research Board 4, pp. 328 (cit. on p. 98).Google Scholar
Guo, S. et al. (2021). “Improving Google flu trends for COVID-19 estimates using Weibo posts.” Data Science and Management 3, pp. 1321 (cit. on p. 74).CrossRefGoogle Scholar
Guy, W. A. (1882). “Two hundred and fifty years of small pox in London, together with a supplement relating to England and Wales.” Journal of the Royal Statistical Society 45, pp. 399443 (cit. on p. 15).Google Scholar
Hales, S. et al. (2002). “Potential effect of population and climate change global, distribution of dengue fever: an emprical model.” The Lancet 360, pp. 830834 (cit. on p. 26).CrossRefGoogle Scholar
Hall, P. (2003). “A possum’s tale – how statistics revealed a new mammal species.” Chance 16, pp. 813 (cit. on p. 427).CrossRefGoogle Scholar
Hand, D. J. et al. (1993). A Handbook of Small Data Sets. CRC Press (cit. on p. 104).CrossRefGoogle Scholar
Harker, F. R. and Maindonald, J. H. (1994). “Ripening of nectarine fruit.” In: Plant Physiology 106, pp. 165171 (cit. on p. 226).CrossRefGoogle ScholarPubMed
Harrell, F. E. (2015). Regression Modeling Strategies, with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. Springer. XXV + 581 pp. (cit. on pp. 184, 202, 203, 290, 463).CrossRefGoogle Scholar
Hassall, A. H. (1850). “Memoir on the organic analysis or microscopic examination of water: Supplied to the inhabitants of London and the suburban districts.” The Lancet 55.1382, pp. 230235 (cit. on p. 258).CrossRefGoogle Scholar
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning. Data Mining, Inference and Prediction. 2nd ed. Springer. 745 pp. (cit. on pp. 202, 203, 347, 395, 462).Google Scholar
Hauck, W. W. J. and Donner, A. (1977). “Wald’s test as applied to hypotheses in logit analysis.” Journal of the American Statistical Association 72, pp. 851853 (cit. on p. 277).Google Scholar
Held, L. and Ott, M. (2018). “On p-values and Bayes factors.” Annual Review of Statistics and Its Application 5, pp. 393419. https://doi.org/10.5167/uzh-148600 (cit. on p. 132).CrossRefGoogle Scholar
Hernán, M. A. and Robins, J. M. (2020). Causal inference. What if? https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2020/01/ci_hernanrobins_21jan20.pdf (visited on 03/28/2022) (cit. on pp. 158, 202).Google Scholar
Hernández-Díaz, S., Schisterman, E. F., and Hernán, M. A. (2006). “The birth weight paradox uncovered?American Journal of Epidemiology 164.11, pp. 11151120 (cit. on pp. 160, 161).CrossRefGoogle ScholarPubMed
Hoaglin, D. C. (2003). “John W. Tukey and Data Analysis.” Statistical Science 18, pp. 311318 (cit. on p. 10).CrossRefGoogle Scholar
Hobson, J. A. (1988). The Dreaming Brain. Basic Books (cit. on p. 102).Google Scholar
Höfler, M. (2005). “The Bradford Hill considerations on causality: a counterfactual perspective.” Emerging Themes in Epidemiology 2.1, pp. 19. https://link.springer.com/article/10.1186/1742-7622-2-11 (cit. on p. 158).CrossRefGoogle Scholar
Honaker, J. and King, G. (2010). “What to do about missing values in time-series cross-section data.” American Journal of Political Science 54.2, pp. 561581. https://citeseerx.ist.psu.edu/document?repid=rep1{&}type=pdf{&}doi=d927076009fc1d86676d5c2da5b11d9fae159bbf (cit. on pp. 315, 457, 463).CrossRefGoogle Scholar
Hong, G. (2010). “Marginal mean weighting through stratification: Adjustment for selection bias in multilevel data.” Journal of Educational and Behavioral Statistics 35.5, pp. 499531 (cit. on p. 456).CrossRefGoogle Scholar
Hothorn, T., Bretz, F., and Westfall, P. (2008). “Simultaneous inference in general parametric models.” Biometrical Journal 50.3, pp. 346363 (cit. on p. 124).CrossRefGoogle ScholarPubMed
Huang, Z. (1997). “Clustering large data sets with mixed numeric and categorical values.” In: Proceedings of the 1st Pacific–Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Citeseer, pp. 2134 (cit. on p. 416).Google Scholar
Hunter, D. (2000). “The conservation and demography of the southern corroboree frog (Pseudophryne corroboree).MSc, University of Canberra (cit. on p. 250).Google Scholar
Hyndman, R. J. and Athanasopoulos, G. (2021). Forecasting: Principles and Practice. 3rd ed. The second edition can be accessed online. OTexts. https://otexts.com/fpp2/ (cit. on pp. 293, 314).Google Scholar
Hyndman, R. J. and Khandakar, Y. (2008). “Automatic time series forecasting: The forecast package for R.” Journal of Statistical Software 27.3, pp. 122 (cit. on p. 314).CrossRefGoogle Scholar
Hyndman, R. J. et al. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer. 360 pp. (cit. on p. 314).CrossRefGoogle Scholar
Imbens, G. W. (2015). “Matching methods in practice: Three examples.” Journal of Human Resources 50.2, pp. 373419 (cit. on pp. 161, 462).CrossRefGoogle Scholar
Ioannidis, J. P. (2005). “Why most published research findings are false.” PLoS Medicine 2.8, e124 (cit. on pp. 55, 56, 79).CrossRefGoogle Scholar
Ioannidis, J. P. (2018). “The proposal to lower P value thresholds to .005.” JAMA 319.14, pp. 14291430. www.academia.edu/download/62536357/jama_Ioannidis_2018_vp_1800120200329-98596-ugfydf.pdf (cit. on p. 78).CrossRefGoogle ScholarPubMed
Izenman, A. J. (2008). Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer. 733 pp. (cit. on p. 408).CrossRefGoogle Scholar
Jakobsen, J. C. et al. (2017). “When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts.” BMC Medical Research Methodology 17.1, pp. 110. https://rdcu.be/c9plG (cit. on p. 461).CrossRefGoogle ScholarPubMed
Johnson, D. H. (1995). “Statistical sirens: the allure of nonparametrics.” Ecology 76, pp. 19982000 (cit. on p. 137).CrossRefGoogle Scholar
Jung, K. et al. (2014). “Female hurricanes are deadlier than male hurricanes.” Proceedings of the National Academy of Sciences (PNAS) 111, pp. 87828787. www.pnas.org/cgi/doi/10.1073/pnas.1402786111 (cit. on p. 168).CrossRefGoogle ScholarPubMed
Kahneman, D. (2013). Thinking, Fast and Slow. 1st ed. Farrar, Straus and Giroux (cit. on pp. 75, 79).Google Scholar
Kahneman, D., Sibony, O., and Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. Little, Brown (cit. on p. 10).Google Scholar
Kaminski, J. A. et al. (2018). “Epigenetic variance in dopamine D2 receptor: A marker of IQ malleability?Translational Psychiatry 8.1, pp. 111 (cit. on p. 368).CrossRefGoogle ScholarPubMed
Kass, R. E. and Raftery, A. E. (Mar. 1993). Bayes Factors and Model Uncertainty. Tech. rep. 254. Carnegie-Mellon University, Dept. of Statistics. www.semanticscholar.org/paper/Bayes-factors-and-model-uncertainty-Kass-Raftery/42d671ae17a611ac474cb39f59f4cf31f65b51ef (cit. on pp. 61, 62, 80).Google Scholar
Kass, R. E. and Raftery, A. E. (1995). “Bayes factors.” Journal of the American Statistical Association 90.430, pp. 773795 (cit. on p. 65).CrossRefGoogle Scholar
King, D. A. (1998). “Relationship between crown architecture and branch orientation in rain forest trees.” Annals of Botany 82, pp. 17 (cit. on p. 423).CrossRefGoogle Scholar
King, D. A. and Maindonald, J. H. (1999). “Tree architecture in relation to leaf dimensions and tree stature in temperate and tropical rain forests.” Journal of Ecology 87, pp. 10121024 (cit. on p. 423).CrossRefGoogle Scholar
Klein, J. P. and Moeschberger, M. L. (2003). Survival Analysis: Techniques for Censored and Truncated Data. Vol. 2. Springer (cit. on p. 282).CrossRefGoogle Scholar
Klein, R. A. et al. (2014). “Investigating variation in replicability: A ‘many labs’ replication project.” Social Psychology 45.3, p. 142. https://psycnet.apa.org/fulltext/2014-20922-002.html (cit. on p. 77).CrossRefGoogle Scholar
Krzanowski, W. J. (2000). Principles of Multivariate Analysis. A User’s Perspective. Revised. Clarendon Press. 612 pp. (cit. on p. 462).CrossRefGoogle Scholar
Lalonde, R. (1986). “Evaluating the economic evaluations of training programs.” American Economic Review 76, pp. 604620 (cit. on pp. 22, 445, 449).Google Scholar
Latter, O. H. (1902). “The egg of Cuculus canorus. An inquiry into the dimensions of the cuckoo’s egg and the relation of the variations to the size of the eggs of the foster-parent, with notes on coloration, &c.” Biometrika 1, pp. 164176 (cit. on pp. 4, 5).Google Scholar
Law, C. W. et al. (2014). “voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.” Genome Biol 15.2, R29 (cit. on p. 126).CrossRefGoogle ScholarPubMed
Lazer, D. et al. (2014). “The parable of Google Flu: Traps in big data analysis.” Science 343.6176, pp. 12031205 (cit. on p. 74).CrossRefGoogle ScholarPubMed
Lee, J. S. et al. (2017). “A local-EM algorithm for spatio-temporal disease mapping with aggregated data.” Spatial Statistics 21, pp. 7595. https://doi.org/10.1016/j.spasta.2017.05.001. www.sciencedirect.com/science/article/pii/S2211675317300064 (cit. on p. 26).CrossRefGoogle Scholar
Leek, J. T. and Storey, J. D. (2007). “Capturing heterogeneity in gene expression studies by surrogate variable analysis.” PLoS Genetics 3.9, e161 (cit. on p. 462).CrossRefGoogle ScholarPubMed
Levitt, S. D. and Dubner, S. J. (2005). Freakonomics. A Rogue Economist Explores the Hidden Side of Everything. William Morrow. 242 pp. http://freakonomics.com/books/ (cit. on p. 202).Google Scholar
Lichtenstein, A. H. et al. (2021). “2021 Dietary guidance to improve cardiovascular health: A scientific statement from the American Heart Association.” Circulation 144.23, e472e487 (cit. on p. 159).CrossRefGoogle Scholar
Lim, T.-S. and Loh, W.-Y. (2000). “A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms.” Machine Learning 40, pp. 203228 (cit. on p. 396).CrossRefGoogle Scholar
Linacre, E. (1992). Climate Data and Resources. A Reference and Guide. Routledge (cit. on p. 233).Google Scholar
Linacre, E. and Geerts, B. (1997). Climates and Weather Explained. Routledge (cit. on p. 233).CrossRefGoogle Scholar
Linde, K. et al. (2005). “Acupuncture for patients with migraine. A randomized controlled trial.” Journal of the American Medical Association 293, pp. 21182125 (cit. on p. 137).CrossRefGoogle ScholarPubMed
Linde, M. et al. (2021). “Decisions about equivalence: A comparison of TOST, HDI-ROPE, and the Bayes factor.” Psychological Methods 28, pp. 740755 (cit. on p. 62).CrossRefGoogle ScholarPubMed
Lindenmayer, D. B. et al. (1995). “Morphological variation among columns of the, mountain brushtail possum, Trichosurus caninus Ogilby (Phalangeridae: Marsupiala).” Australian Journal of Zoology 43, pp. 449458 (cit. on p. 427).CrossRefGoogle Scholar
Maindonald, J. H. (1984). Statistical Computation. Wiley. 370 pp. (cit. on p. 240).Google Scholar
Maindonald, J. H. (1992). “Statistical design, analysis and presentation issues.” New Zealand Journal of Agricultural Research 35, pp. 121141 (cit. on pp. 79, 335).CrossRefGoogle Scholar
(2003). The Role of Models in Predictive Validation. Invited Paper. ISI (cit. on p. 121).Google Scholar
(2006). “Data mining methodology weaknesses and suggested fixes.” In Fifth Australasian Data Mining Conference (AusDM2006). Ed. by Christen, P. Vol. 61. CRPIT. Sydney, Australia: ACS, pp. 916 (cit. on p. 462).Google Scholar
Maindonald, J. and Braun, J. (2003). Data Analysis and Graphics Using R. Cambridge University Press. 386 pp. (cit. on p. xviii).Google Scholar
Maindonald, J. and Braun, W. J. (2010). Data Analysis and Graphics Using R. 3rd ed. Cambridge University Press. 549 pp. (cit. on p. xxi).Google Scholar
Maindonald, J. H. and Burden, C. J. (2005). “Selection bias in plots of microarray or other data that have been sampled from a high-dimensional space.” In Proceedings of 12th Computational Techniques and Applications Conference CTAC-2004. Vol. 46, pp. C59C74 (cit. on pp. 434, 441).Google Scholar
Maindonald, J. H., Waddell, B. C., and Petry, R. J. (2001). “Apple cultivar effects on codling moth (Lepidoptera: Tortricidae) egg mortality following fumigation with methyl bromide.” Postharvest Biology and Technology 22, pp. 99110 (cit. on p. 276).CrossRefGoogle Scholar
Marriott, F. (1974). The Interpretation of Multiple Observations. Academic Press (cit. on p. 417).Google Scholar
Matloff, N. (2011). The Art of R Programming. No Starch Inc. XXIV + 374 pp. (cit. on p. 79).Google Scholar
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models. 2nd ed. Chapman and Hall. 532 pp. (cit. on pp. 247, 276, 277, 290).CrossRefGoogle Scholar
McLachlan, G. J. (2011). “Commentary on Steinley and Brusco (2011): Recommendations and cautions.” Psychological Methods 16.1, pp. 8081 (cit. on p. 422).CrossRefGoogle ScholarPubMed
McLachlan, G. and Krishnan, T. (2008). The EM Algorithm and Extensions. 2nd ed. John Wiley & Sons (cit. on p. 418).CrossRefGoogle Scholar
McLellan, E. A., Medline, A., and Bird, R. P. (1991). “Dose response and proliferative characteristics of aberrant crypt foci: putative preneoplastic lesions in rat colon.” Carcinogenesis 12, pp. 20932098 (cit. on p. 261).CrossRefGoogle ScholarPubMed
McLeod, C. C. (1982). “Effect of rates of seeding on barley grown for grain.” New Zealand Journal of Agriculture 10, pp. 133136 (cit. on p. 221).CrossRefGoogle Scholar
McNicholas, P. D. (2016). Mixture Model-Based Classification. Chapman and Hall/CRC Press (cit. on p. 418).CrossRefGoogle Scholar
Meyer, D. (2001). “Support Vector Machines.” R News 1.3, pp. 2326 (cit. on p. 442).Google Scholar
Meyer, M. (2006). “Commentary on ‘Another look at Meyer and Finney’s “Who wants airbags?”’Chance 19, pp. 2324 (cit. on p. 23).CrossRefGoogle Scholar
Meyer, M. and Finney, T. (2005). “Who wants airbags?Chance 18, pp. 316 (cit. on p. 23).CrossRefGoogle Scholar
Miller, R. G. (1986). Beyond ANOVA, Basics of Applied Statistics. Wiley. 318 pp. (cit. on pp. 93, 102, 137, 296).Google Scholar
Mitchell, B. R. (1988). British Historical Statistics. Cambridge University Press (cit. on p. 15).Google Scholar
Mogil, J. S. and Macleod, M. R. (2017). “No publication without confirmation.” Nature 542.7642, pp. 409411. https://doi.org/10.1038/542409a (cit. on p. 79).CrossRefGoogle ScholarPubMed
Mokdad, A. H. et al. (2018). “The state of US health, 1990–2016: burden of diseases, injuries, and risk factors among US states.” AMA 319.14, pp. 14441472 (cit. on p. 160).Google ScholarPubMed
Morgan, B. J. T. (1992). Analysis of Quantal Response Data. Chapman & Hall (cit. on pp. 100, 353).CrossRefGoogle Scholar
Morgan, B. J. T. and Ridout, M. S. (2008). “A new mixture model for capture heterogeneity.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 57.4, pp. 433446. https://doi.org/10.1111/j.1467-9876.2008.00620.x (cit. on pp. 100, 349).Google Scholar
Morgan, S. L. and Winship, C. (2015). Counterfactuals and Causal Inference: Methods and Principles for Social Research. 2nd ed. Cambridge University Press (cit. on p. 444).Google Scholar
Murrell, P. (2011). R Graphics. 2nd ed. Chapman and Hall/CRC. 546 pp. www.e-reading.org.ua/bookreader.php/137370/Murrell_-_R_Graphics.pdf (cit. on p. 80).Google Scholar
Myers, R. H. (1990). Classical and Modern Regression with Applications. 2nd ed. Brooks Cole. 488 pp. (cit. on p. 203).Google Scholar
Nadel, E. and Bussolari, S. (1988). “The Daedalus project: physiological problems and solutions.” American Scientist 76, pp. 351360 (cit. on p. 360).Google Scholar
Neath, A. A. and Cavanaugh, J. E. (2012). “The Bayesian information criterion: background, derivation, and applications.” Wiley Interdisciplinary Reviews: Computational Statistics 4.2, pp. 199203 (cit. on p. 133).CrossRefGoogle Scholar
Newton, A. and Gadow, H. (1896). A Dictionary of Birds. A. and C. Black (cit. on pp. 4, 5).Google Scholar
Nicholls, N. et al. (1996). “Recent apparent changes in relationships between the El Niño – southern oscillation and Australian rainfall and temperature.” Geophysical Research Letters 23, pp. 33573360 (cit. on p. 304).CrossRefGoogle Scholar
Nosek, B. A. and Errington, T. M. (2020). “The best time to argue about what a replication means? Before you do it.” Nature 583, pp. 518520 (cit. on p. 77).CrossRefGoogle ScholarPubMed
Nosek, B. A. et al. (2015). “Promoting an open research culture.” Science 348.6242, pp. 14221425 (cit. on p. 79).CrossRefGoogle ScholarPubMed
O’Neil, C. (2016). Weapons of Math Destruction. 1st ed. Crown (cit. on pp. 73, 79).Google Scholar
Open Science Collaboration (2015). “Estimating the reproducibility of psychological science.” Science 349.6251. Open Science Collaboration (Nosek, B. A. and others), ‘aac4716-1’–‘aac4716-7’. https://osf.io/k9rnd/ (cit. on pp. 76, 207).Google Scholar
Ord, J. K., Koehler, A. B., and Snyder, R. D. (1997). “Estimation and prediction for a class of dynamic nonlinear statistical models.” Journal of the American Statistical Association 92, pp. 16211629 (cit. on p. 314).CrossRefGoogle Scholar
Paluch, A. E. et al. (2021). “Steps per day and all-cause mortality in middle-aged adults in the Coronary Artery Risk Development in Young Adults study.” JAMA Network Open 4.9, e2124516-e2124516 (cit. on p. 160).CrossRefGoogle ScholarPubMed
Payne, R. W. et al. (1997). Genstat 5 Release 3 Reference Manual. Oxford University Press (cit. on pp. 369, 371).Google Scholar
Perrine, F. M. et al. (2001). “Rhizobium plasmids are involved in the inhibition or stimulation of rice growth and development.” Australian Journal of Plant Physiology 28, pp. 923927 (cit. on p. 129).Google Scholar
Phipson, B. et al. (2016). “Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression.” The Annals of Applied Statistics 10.2, p. 946 (cit. on p. 433).CrossRefGoogle ScholarPubMed
Pinheiro, J. C. and Bates, D. M. (2000). Mixed Effects Models in S and S-PLUS. Springer (cit. on pp. 359, 371).CrossRefGoogle Scholar
Prinz, F., Schlange, T., and Asadullah, K. (2011). “Believe it or not: how much can we rely on published data on potential drug targets?Nature Reviews Drug Discovery 10.9, pp. 712712 (cit. on pp. 55, 76).CrossRefGoogle ScholarPubMed
Ramsay, J. and Silverman, B. (2002). Applied Functional Data Analysis. Springer. 191 pp. (cit. on p. 370).Google Scholar
Ridley, M. and Pierpoint, G. (2003). Nature via Nurture: Genes, Experience, and What Makes Us Human. Vol. 19. HarperCollins (cit. on p. 368).Google Scholar
Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press. 416 pp. (cit. on pp. 383, 395, 425, 462).CrossRefGoogle Scholar
Ritchie, M. E. et al. (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research 43.7, e47 (cit. on p. 430).CrossRefGoogle ScholarPubMed
Robbins, N. (2012). Creating More Effective Graphs. Wiley. (cit. on p. 80).Google Scholar
Robinson, G. K. (2000). Practical Strategies for Experimenting. Wiley. (cit. on p. 79).Google Scholar
Rodgers, P. and Collings, A. (2021). “Reproducibility in cancer biology: What have we learned?Elife 10, e75830 (cit. on p. 77).CrossRefGoogle ScholarPubMed
Rosenbaum, P. R. (2002). Observational Studies. 2nd ed. Springer. 377 pp. (cit. on pp. 202, 462).CrossRefGoogle Scholar
Rosenbaum, P. and Rubin, D. (1983). “The central role of the propensity score in observational studies for causal effects.” Biometrika 70, pp. 4155 (cit. on p. 444).CrossRefGoogle Scholar
Rouder, J. N. et al. (2009). “Bayesian t tests for accepting and rejecting the null hypothesis.” Psychonomic Bulletin & Review 16.2, pp. 225237. https://link.springer.com/content/pdf/10.3758/PBR.16.2.225.pdf (cit. on pp. 61, 133).CrossRefGoogle Scholar
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley-Interscience (cit. on p. 463).CrossRefGoogle Scholar
Rudin, C. et al. (2022). “Interpretable machine learning: Fundamental principles and 10 grand challenges.” Statistics Surveys 16, pp. 185 (cit. on p. 73).CrossRefGoogle Scholar
Sangari, S. and Ray, H. E. (2021). “Evaluation of imputation techniques with varying percentage of missing data.” arXiv preprint arXiv:2109.04227. https://arxiv.org/pdf/2109.04227 (cit. on pp. 75, 463).Google Scholar
Schatzkin, A. et al. (2003). “A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: results from the biomarker-based Observing Protein and Energy Nutrition (OPEN) study.” International Journal of Epidemiology 32, pp. 10541062 (cit. on pp. 191, 192).CrossRefGoogle Scholar
Scheel, A. M., Schijen, M. R., and Lakens, D. (2021). “An excess of positive results: Comparing the standard psychology literature with registered reports.” Advances in Methods and Practices in Psychological Science 4.2, p. 25152459211007467. https://doi.org/10.1177/25152459211007467 (cit. on p. 78).CrossRefGoogle Scholar
Schmidt-Nielsen, K. (1984). Scaling. Why Is Animal Size So Important? Cambridge University Press. 256 pp. (cit. on p. 115).CrossRefGoogle Scholar
Science Museum (2019). Cholera in Victorian London. www.sciencemuseum.org.uk/objects-and-stories/medicine/cholera-victorian-london (visited on 01/01/2022) (cit. on p. 257).Google Scholar
Scott, A. J. and Symons, M. J. (1971). “Clustering methods based on likelihood ratio criteria.” Biometrics, pp. 387397 (cit. on p. 422).CrossRefGoogle Scholar
Sellke, T., Bayarri, M., and Berger, J. O. (2001). “Calibration of ρ values for testing precise null hypotheses.” The American Statistician 55.1, pp. 6271 (cit. on pp. 63, 132, 133).CrossRefGoogle Scholar
Senn, S. (2003). Dicing with Death: Chance, Risk and Health. Cambridge University Press. 261 pp. (cit. on p. 15).CrossRefGoogle Scholar
Shanklin, J. (2001). Ozone at Halley, Rothera and Vernadsky/Faraday. www.antarctica.ac.uk/met/jds/ozone/ (cit. on p. 243).Google Scholar
Sharp, S. J., Thompson, S. G., and Altman, D. G. (1996). “The relation between treatment benefit and underlying risk in meta-analysis.” British Medical Journal 313, pp. 735738 (cit. on p. 195).CrossRefGoogle ScholarPubMed
Shoesmith, G. L. (2017). “Crime, teenage abortion, and unwantedness.” Crime & Delinquency 63.11, pp. 14581490 (cit. on p. 202).CrossRefGoogle ScholarPubMed
Shumway, R. and Stoffer, D. (2006). Time Series Analysis and Its Applications. Springer (cit. on p. 314).Google Scholar
Simpson, E. H. (1951). “The interpretation of interaction in contingency tables.” Journal of the Royal Statistical Society, Series B 13, pp. 238241 (cit. on p. 90).CrossRefGoogle Scholar
Singmann, H. and Kellen, D. (2019). “An introduction to mixed models for experimental psychology.” New Methods in Cognitive Psychology 28.4, pp. 431 (cit. on p. 366).CrossRefGoogle Scholar
Smith, G. (2014). Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics. Duckworth Overlook. 304 pp. (cit. on pp. 75, 137).Google Scholar
Smith, G. D. (2002). “Commentary: Behind the Broad Street pump: aetiology, epidemiology and prevention of cholera in mid-19th century Britain.” International Journal of Epidemiology 31.5, pp. 920932 (cit. on p. 257).CrossRefGoogle ScholarPubMed
Smyth, G. K. (2004). “Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.” Statistical Applications in Genetics and Molecular Biology 3. No. 1, Article 3 (cit. on p. 126).CrossRefGoogle ScholarPubMed
Snelgar, W. P., Manson, P. J., and Martin, P. J. (1992). “Influence of time of shading on flowering and yield of kiwifruit vines.” Journal of Horticultural Science 67, pp. 481487 (cit. on p. 335).CrossRefGoogle Scholar
Snijders, T. and Bosker, R. (2011). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. 2nd ed. Sage Publications. 354 pp. (cit. on p. 371).Google Scholar
Snow, J. (1855). On the Mode of Communication of Cholera 2nd ed. John Churchill. www.ph.ucla.edu/epi/snow/snowbook.html (cit. on pp. 259, 260).Google Scholar
Soyer, E. and Hogarth, R. M. (2012). “The illusion of predictability: How regression statistics mislead experts.” International Journal of Forecasting 28.3, pp. 695711. https://doi.org/10.1016/j.ijforecast.2012.02.002 (cit. on p. 181).CrossRefGoogle Scholar
Spiegelhalter, D. J. et al. (2000). “Bayesian methods in health technology assessment: a review.” Health Technology Assessment 4.38. https://leicester.figshare.com/articles/report/Bayesian_methods_in_health_technology_assessment_a_review/10077032/files/18168374.pdf (cit. on p. 137).Google Scholar
Spiegelhalter, D. J. et al. (2002). “Bayesian measures of model complexity and fit.” Journal of the Royal Statistical Society, Series B 64. With following discussion, pp. 616639, pp. 583–616 (cit. on p. 347).CrossRefGoogle Scholar
Sprent, P. (1966). “A generalized least squares approach to linear functional relationships.” Journal of the Royal Statistical Society, Series B 28. With following discussion, pp. 288297, pp. 278–288 (cit. on p. 112).CrossRefGoogle Scholar
Stewardson, C. L. et al. (1999). “Gross and microscopic visceral anatomy of the male Cape fur seal, Arctocephalus pusillus pusillus (Pinnipedia: Otariidae), with reference to organ size and growth.” Journal of Anatomy (Cambridge) 195 (WWF project ZA-348), pp. 235255 (cit. on p. 114).CrossRefGoogle ScholarPubMed
Stewart, K. M., Van Toor, R. F., and Crosbie, S. F. (1988). “Control of grass grub (Coleoptera: Scarabaeidae) with rollers of different design.” New Zealand Journal of Experimental Agriculture 16, pp. 141150 (cit. on p. 43).CrossRefGoogle Scholar
Stidd, C. K. (1953). “Cube-root-normal precipitation distributions.” Transactions of the American Geophysical Union 34, pp. 3135 (cit. on p. 304).Google Scholar
Stiell, I. G. et al. (2001). “The Canadian CT head rule for patients with minor head injury.” The Lancet 357, pp. 13911396 (cit. on p. 290).CrossRefGoogle ScholarPubMed
Stocks, P. (1942). “Measles and whooping cough during the dispersal of 1939–1940.” Journal of the Royal Statistical Society 105, pp. 259291 (cit. on p. 15).CrossRefGoogle Scholar
Streiner, D. L., Norman, G. R., and Cairney, J. (2014). Health Measurement Scales: A Practical Guide to their Development and Use. Oxford University Press. xiii + 399 pp. (cit. on pp. 8, 408, 462).Google Scholar
Talbot, M. (1984). “Yield variability of crop varieties in the U.K.” Journal of the Agricultural Society of Cambridge 102, pp. 315321 (cit. on pp. 323, 371).CrossRefGoogle Scholar
Thaler, R. H. (2015). Misbehaving. Allen Lane (cit. on p. 79).Google Scholar
Thall, P. F. and Vail, S. C. (1990). “Some covariance models for longitudinal count data.” Biometrics 46, pp. 657671 (cit. on p. 358).CrossRefGoogle ScholarPubMed
Therneau, T. M. and Atkinson, E. J. (1997). An Introduction to Recursive Partitioning Using the RPART Routines. Tech. rep. 61. Department of Health Science Research, Mayo Clinic, Rochester, MN (cit. on pp. 373, 395, 396).Google Scholar
Therneau, T. M. and Grambsch, P. M. (2001). Modeling Survival Data: Extending the Cox Model. Springer. 350 pp. (cit. on p. 290).Google Scholar
Tu, Y. and Gilthorpe, M. S. (2011). Statistical Thinking in Epidemiology. CRC Press. 231 pp. (cit. on p. 202).Google Scholar
Tukey, J. W. (1953). “The growth of experimental design in a research laboratory.” In Research Operations in Industry. Ed. by Hertz, D. B. Vol. 3. King’s Crown Press, pp. 303313 (cit. on p. xiv).Google Scholar
Tukey, J. W. (1992). Exploratory Data Analysis. Note the comment in www.sumsar.net/blog/2013/09/a-bayesian-twist-on-tukeys-flogs/ on Tukey’s flog (folded logarithm) transformation. Addison-Wesley (cit. on p. 289).Google Scholar
Tukey, J. W. (1997). “More honest foundations for data analysis.” Journal of Statistical Planning and Inference 57.1, pp. 2128 (cit. on pp. 9, 90).CrossRefGoogle Scholar
Turner, R. M. et al. (2009). “Bias modelling in evidence synthesis.” Journal Of The Royal Statistical Society Series A 172.1, pp. 2147 (cit. on pp. 370, 371).CrossRefGoogle ScholarPubMed
Vaida, F. and Blanchard, S. (2005). “Conditional Akaike information for mixed-effects models.” Biometrika 92, pp. 351370 (cit. on p. 347).CrossRefGoogle Scholar
Van Buuren, S. (2018). Flexible Imputation of Missing Data. CRC press (cit. on p. 461).CrossRefGoogle Scholar
Venables, W. N. (1998). “Exegeses on linear models.” In Proceedings of the 1998 International S-PLUS User Conference (cit. on pp. 203, 213).Google Scholar
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. 4th ed. (cit. on pp. 183, 202, 237, 290, 314, 371, 395, 408). (See also R Complements to Modern Applied Statistics with S. Springer.)CrossRefGoogle Scholar
Vermunt, J. K. (2011). “K-Means may perform as well as mixture model clustering but may also be much worse: Comment on Steinley and Brusco (2011).” Psychological Methods 16.1, pp. 8288 (cit. on p. 422).CrossRefGoogle Scholar
Wainright, P., Pelkman, C., and Wahlsten, D. (1989). “The quantitative relationship between nutritional effects on preweaning growth and behavioral development in mice.” Developmental Psychobiology 22, pp. 183193 (cit. on p. 157).CrossRefGoogle Scholar
Wang, Z. et al. (2021). “Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison.” arXiv preprint arXiv:2103.09316 (cit. on p. 74).Google Scholar
Welch, B. L. (1949). “Further note on Mrs. Aspin’s tables and on certain approximations to the tabled function.” Biometrika 36, pp. 293296 (cit. on p. 93).Google Scholar
Welham, S. et al. (2014). Statistical Methods in Biology: Design and Analysis of Experiments and Regression. CRC Press. 608 pp. (cit. on p. 216).CrossRefGoogle Scholar
Wickham, H. (2015). Advanced R. CRC Press. 476 pp. (cit. on p. 79).Google Scholar
Wickham, H. (2016). R for Data Science. O’Reilly (cit. on p. 79).Google Scholar
Wilcox, A. J. (2006). “Invited commentary: the perils of birth weight – a lesson from directed acyclic graphs.” American Journal of Epidemiology 164.11, pp. 11211123 (cit. on p. 161).CrossRefGoogle ScholarPubMed
Wilkinson, L. and Task Force on Statistical Inference (1999). “Statistical methods in psychology journals: guidelines and, explanation.” American Psychologist 54, pp. 594604 (cit. on p. 79).CrossRefGoogle Scholar
Williams, E. R., Matheson, A. C., and Harwood, C. E. (2002). Experimental Design and Analysis for Use in Tree Improvement. Revised. CSIRO Information Services. 220 pp. (cit. on p. 371).CrossRefGoogle Scholar
Williams, G. P. (1983). “Improper use of regression equations in the earth sciences.” Geology 11, pp. 195197 (cit. on p. 195).2.0.CO;2>CrossRefGoogle Scholar
Wonnacott, T. H. and Wonnacott, R. (1990). Introductory Statistics. 5th ed. Wiley. 736 pp. (cit. on p. 80).Google Scholar
Wood, S. N. (2010). More advanced use of mgcv. www.maths.ed.ac.uk/-swood34/mgcv/tampere/mgcv-advanced.pdf (visited on 01/25/2022) (cit. on p. 229).Google Scholar
Wood, S. N. (2017). Generalized Additive Models. An Introduction with R. 2nd ed. Chapman and Hall/CRC. 410 pp. (cit. on pp. 224, 240, 289).CrossRefGoogle Scholar
Würtz, D. (2004). Rmetrics: An Environment for Teaching Financial Engineering and Computational Finance with R. Rmetrics, ITP, ETH Zürich. Zürich, Switzerland (cit. on p. 314).Google Scholar
Xie, Y. and Cheng, X. (Oct. 2008). “animation: A package for statistical animations.” R News 8.2, pp. 2327 (cit. on p. 40).Google Scholar
Yong, E. (2012). “Nobel laureate challenges psychologists to clean up their act.” Nat. News 490 (cit. on p. 77).Google Scholar
Zeger, S. L. et al. (2000). “Exposure measurement error in time-series studies of air pollution: concepts and consequences.” Environmental Health Perspectives 108. See also vol. 109, p. A517, pp. 419426 (cit. on p. 194).CrossRefGoogle ScholarPubMed
Zhang, H. and Singer, B. (1999). Recursive Partitioning in the Health Sciences. Springer (cit. on p. 396).CrossRefGoogle Scholar
Zhu, X., Ambroise, C., and McLachlan, G. J. (2006). “Selection bias in working with the top genes in supervised classification of tissue samples.” Statistical Methodology 3, pp. 2941 (cit. on p. 434).CrossRefGoogle Scholar
Zimmer, C. (2019). She Has Her Mother’s Laugh: The Powers, Perversions, and Potential of Heredity. Dutton (cit. on p. 368).Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • John H. Maindonald, Statistics Research Associates, Wellington, New Zealand, W. John Braun, University of British Columbia, Okanagan, Jeffrey L. Andrews, University of British Columbia, Okanagan
  • Book: A Practical Guide to Data Analysis Using R
  • Online publication: 11 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009282284.012
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • John H. Maindonald, Statistics Research Associates, Wellington, New Zealand, W. John Braun, University of British Columbia, Okanagan, Jeffrey L. Andrews, University of British Columbia, Okanagan
  • Book: A Practical Guide to Data Analysis Using R
  • Online publication: 11 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009282284.012
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • John H. Maindonald, Statistics Research Associates, Wellington, New Zealand, W. John Braun, University of British Columbia, Okanagan, Jeffrey L. Andrews, University of British Columbia, Okanagan
  • Book: A Practical Guide to Data Analysis Using R
  • Online publication: 11 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009282284.012
Available formats
×