G —test of independence. Small numbers in chi-square and G —tests. Repeated G —tests of goodness-of-fit. Cochran—Mantel— Haenszel test. One-sample t —test. Two-sample t —test. Data transformations. Kruskal—Wallis test. Paired t —test.
Wilcoxon signed-rank test. Linear regression and correlation. Spearman rank correlation. Polynomial regression. Analysis of covariance. Multiple regression. Simple logistic regression. Multiple logistic regression. Multiple comparisons. Using spreadsheets for statistics. Displaying results in graphs. Displaying results in tables. Choosing the right test. Welcome to the third edition of the Handbook of Biological Statistics! This online textbook evolved from a set of notes for my Biological Data Analysis class at the University of Delaware.
My main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In my class and in this textbook, I spend relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens.
Biologists in very statistics-intensive fields, such as ecology, epidemiology, and systematics, may find this handbook to be a bit superficial for their needs, just as a biologist using the latest techniques in 4-D, 3-photon confocal microscopy needs to know more about their microscope than someone who's just counting the hairs on a fly's back.
But I hope that biologists in many fields will find this to be a useful introduction to statistics. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches.
Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences. Introduction Todd Little 2.
Haig 3. Quantitative Methods and Ethics Ralph L. Rosnow and Robert Rosenthal 4. Special Populations Keith F. Early, and Rand D.
Conger 5. Teaching Quantitative Psychology Lisa L. Harlow 7. Modern Test Theory R. McDonald 8. Spector Kingston and Laura B. Kramer Cook Matching and Propensity Scores Peter M.
Steiner and David Cook Townsend Observational Methods Jamie M. Ostrov and Emily J. Hart An excellent and easy to understand introduction to biostatistics. A humorous and easy-to-understand supplement to a textbook on statistics. An excellent book for the professional who needs to brush up on statistics and as a supplement to the textbook in a college course. This textbook sets the standard for introductory statistics books. Extremely well written with lots of examples and exercises. Used frequently in college courses and AP statistics courses.
HyperStat Online Statistics Textbook. Related Material.
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