Their goal was to strike a balance between how analyses are done versus how to use them correctly, and I think they have achieved that balance. Quinn and Keough cover all the standard material found in introductory books on univariate statistics and provide several chapters on multivariate methods.
I found the 19 chapters well organized and informative. More technical information is covered in boxes outside of the main body of text and each chapter ends with a section of general issues and hints for analysis, which are short and listed with bullet points. Data for the examples come from real experiments, and the raw data are available on the web as text and Excel files so the reader can run their data in his or her favourite statistical package.
While Quinn and Keough suggest their target audience is biologists, nearly all of the examples are from population and community ecology. The book begins with four chapters that introduce the scientific method, probability distributions, estimation, hypothesis testing and graphical exploration of data. These chapters go well beyond the basics, and Quinn and Keough discuss a wide range of topic including Popperian falsification, maximum likelihood estimation, jackknife and bootstrap methods, Bayesian inference and hypothesis testing, decision errors, significance levels for multiple testing, meta-analysis and censored data.
The book closes with a wonderful chapter on presentation of results. The two chapters on regression cover linear and multiple regression, provide a discussion of diagnostics, transformations and stepwise methods and introduce a number of less well-known methods such as smoothing with splines and locally weighted regression i.
And, just to top things off, there is a chapter on logistic regression and generalized linear models and a chapter on the analysis of frequencies. The four chapters on analysis of variance cover not only all of the familiar ground such as factorial designs and repeated measures but also provide good discussions of the use of Type I versus III sums of squares and the pros and cons of restricted versus unrestricted approaches to mixed models.
Restricted and unrestricted models give different estimates of the expected mean squares for mixed models. This is a source of confusion because nearly all introductory textbooks assume the restricted model, yet most modern statistical packages e. Citation Type. Has PDF. Publication Type. More Filters. Preface 1. Doing science: hypotheses, experiments and disproof 3.
Collecting and displaying data 4. Introductory concepts of experimental design 5. Doing science responsibly and … Expand. View 1 excerpt, cites background. Exploratory and inferential multivariate statistical techniques for multidimensional count and binary data with applications in R. Dealing with heterogeneous regression slopes in analysis of covariance: new methodology applied to environmental effects monitoring fish survey data. Analysing Ecological Data. Plotting partial correlation and regression in ecological studies.
The widespread use of graphs that include fitted regression lines to … Expand. Simple means to improve the interpretability of regression coefficients: Interpretation of regression coefficients. Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection.
Highly Influenced. View 10 excerpts, cites background and methods. Statistical methods in research. Introduction to Regression Analysis 2. Populations, Samples and Probability Distributions 3. Basic Statistical Inference 4. The Simple Linear Regression Model 5. Inference in Linear Regression 6. Phone: in Conrinenral U. All other loc1tiom: , FAX: 5! Defining the Experimental Program Like many ideas, the decision to turn the course into a book came about after a little too much wine with dinner-a dinner with fellow scientists Dan Finley, Fred Goldberg, and Allan Weissman-at which we were discussing both the odd fact that experimental design was not commonly taught to prospective biologists in graduate school, and the obvious discontinuities between the demands of Critical Rationalism as written and the way that science was actually practiced.
Of course, this book still would not have been produced were it not accepted by a publisher. Sian Curtis then edited the manuscript in an extremely able fashion, with the help of Ginger Peschke and Maria Smit. Thanks very much to Sian and Jan tor their steady feedback and enthusiasm. Thanks so much also to Rena Steuer for expert production guidance, to Susan Schaefer tor typesetting, and to Denise Weiss for her design expertise.
A great deal of help in writing this was lent by Kumar Dharmarajan. Kumar is a former student and intern in my laboratory, who was a medical student at Columbia when much of the book was written. He was thus able to rake on the role of rhe "prospective audience" and gave invaluable feedback on each chapter, identifYing passages rhat were unclear and asking questions rhat helped in the rewriting.
Thanks very much to him fur spending so much time on this project. Brian Clarke in my lab also read a large chunk of the manuscript and highlighted some sections that needed clarification. Woody Fu is a former student who now has many jobs, including artist and carmonist. He provided three cartoons for this book, and they came out so well that I am sorry we did not make greater use of his talents.
Thank you very much to the fulks at. Novartis, where I now work, tor supporting this project. Novartis a great emphasis on continuing education, and it was a ix x Preface comfort to find that this project invoked enthusiasm in the company. It would be great to hear from readers, including feedback regarding problems with the text so that these could be corrected in fumre editions. Feel free to drop me a line. Now here's an amazing fact: At this writing, in graduate schools throughout the United States, budding Ph.
A quick survey of the curricula for Ph.
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