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The Analysis of Biological Data
Third Edition| ©2020 Michael C. Whitlock; Dolph Schluter
Now available with Macmillan’s new online learning platform Achieve, Analysis of Biological Data provides a practical foundation of statistics for biology students. Every chapter has several biological or medical examples related to key statistics concepts, and each example is prefaced b...
Now available with Macmillan’s new online learning platform Achieve, Analysis of Biological Data provides a practical foundation of statistics for biology students. Every chapter has several biological or medical examples related to key statistics concepts, and each example is prefaced by a substantial description of the biological setting. The emphasis on real and interesting examples carries into the problem sets where students have a wealth of practice problems based on real data.
The third edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of examples and topics that come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use, and is easier than ever to access through Achieve.
Achieve for Analysis of Biological Data connects the problem-solving approach and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.
ISBN:9781319374433
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ISBN:9781319226299
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ISBN:9781319226237
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ISBN:9781319411077
This package includes Achieve and Hardcover.
Practical data analysis using real biological examples
Now available with Macmillan’s new online learning platform Achieve, Analysis of Biological Data provides a practical foundation of statistics for biology students. Every chapter has several biological or medical examples related to key statistics concepts, and each example is prefaced by a substantial description of the biological setting. The emphasis on real and interesting examples carries into the problem sets where students have a wealth of practice problems based on real data.
The third edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of examples and topics that come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use, and is easier than ever to access through Achieve.
Achieve for Analysis of Biological Data connects the problem-solving approach and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.
Features
Achieve Online Homework
- Over 3,000 homework questions of varying difficulty, Bloom’s level, and question type. Every homework question includes a hint, answer-specific feedback, and a fully worked solution. Question types in Achieve include
- Multiple choice
- Ranking
- Sorting
- Numeric entry
- Multi-part questions
- Questions with algorithmically regenerating values
- LearningCurve adaptive quizzing puts the concept of "testing to learn" into action, motivating students to engage with the text's content to identify areas of proficiency. Easy-to-use reporting tools help teachers pinpoint areas to focus on in class.
\ - A mobile, interactive e-book, powered by VitalSource, allows students to highlight and take notes, print select pages, and have the text read aloud to them.
- Over 140 StatTutors--multimedia tutorials that explore important concepts and procedures in a presentation that combines video, audio, and interactive features--are assignable, gradeable, and organized by chapter.
- Applet Activities are visual interactives that allow students to manipulate data and variables in calculations and see the results graphically. Applets also contain assessment questions to test students’ comprehension.
- Videos provide additional exposure to key concepts and examples. Videos are narrated and close-captioned. Video types include
- Whiteboard-style problem-solving videos
- StatTutor video lessons
- Animated lectures and documentary-style videos that illustrate real world scenarios involving statistics
- EESEE (Electronic Encyclopedia of Statistical Examples and Exercises) Case Studies, developed by the Ohio State University Statistics Department, teach students to apply their statistical skills by exploring actual case studies using real data.
- Video Technology Manuals are brief instructional videos that provide basic introductions for working with CrunchIt!, Excel, SPSS, TI-83/84 calculators, JMP, Minitab, R, and RCmdr.
Statistical software options
- CrunchIt!, Macmillan’s proprietary online statistical software powered by R, handles every computation and graphing function an introductory statistics student needs. CrunchIt! is preloaded with data sets, and it allows editing and importing additional data.
- Students also receive access to JMP Student Edition (developed by SAS). With the student edition of JMP, students handle large data, visualizations, and analysis for which the professional version is renowned. Additionally, text-specific data sets are included for download.
- For other statistical software, Achieve includes data sets, including those for
- Excel
- Minitab
- R & RCmdr
- SPSS
- TI Calculators
- Mac-text & PC-text
- CSV file export
New to This Edition
Achieve online homework. Based on research from Macmillan’s Learning Science team, Achieve marries the powerful, tutorial-style assessment of Sapling Learning with rich book-specific resources in one easy-to-use, accessible platform.
New practice and assignment problems to every chapter covering all major concepts and skills.
Integrated online activities with the text for learning the R statistical software environment.
New chapter added on survival analysis, a vital topic in biostatistics.
New instructor resources, including answers to assignment problems and R Code labs, are available at whitlockschluter3e.zoology.ubc.ca.


The Analysis of Biological Data
Third Edition| ©2020
Michael C. Whitlock; Dolph Schluter
Digital Options

Achieve
Achieve is a comprehensive set of interconnected teaching and assessment tools that incorporate the most effective elements from Macmillan Learning's market leading solutions in a single, easy-to-use platform.

E-book
Read online (or offline) with all the highlighting and notetaking tools you need to be successful in this course.
Table of Contents
PART 1 INTRODUCTION TO STATISTICS1.0 Statistics and samples
1.1 What is statistics?
1.2 Sampling populations
1.3 Types of data and variables
1.4 Frequency distributions and probability distributions
1.5 Types of studies
1.6 Summary
Interleaf 1 Correlation does not require causation
2.0 Displaying data
2.1 Guidelines for effective graphs
2.2 Showing data for one variable
2.3 Showing association between two variables and differences between groups
2.4 Showing trends in time and space
2.5 How to make good tables
2.6 How to make data files
2.7 Summary
3.0 Describing data
3.1 Arithmetic mean and standard deviation
3.2 Median and interquartile range
3.3 How measures of location and spread compare
3.4 Cumulative frequency distribution
3.5 Proportions
3.6 Summary
3.7 Quick Formula Summary
4.0 Estimating with uncertainty
4.1 The sampling distribution of an estimate
4.2 Measuring the uncertainty of an estimate
4.3 Confidence intervals
4.4 Error bars
4.5 Summary
4.6 Quick Formula Summary
Interleaf 2 Pseudoreplication
5.0 Probability
5.1 The probability of an event
5.2 Venn Diagrams
5.3 Mutually exclusive events
5.4 Probability distributions
5.5 Either this or that: adding probabilities
5.6 Independence and the multiplication rule
5.7 Probability trees
5.8 Dependent events
5.9 Conditional probability and Bayes' theorem
5.10 Summary
6.0 Hypothesis testing
6.1 Making and using hypotheses
6.2 Hypothesis testing: an example
6.3 Errors in hypothesis testing
6.4 When the null hypothesis is not rejected
6.5 One-sided tests
6.6 Hypothesis testing versus confidence intervals
6.7 Summary
Intereaf 3 Why statistical significance is not the same as biological importance
PART 2 PROPORTIONS AND FREQUENCIES
7.0 Analyzing proportions
7.1 The binomial distribution
7.2 Testing a proportion: the binomial test
7.3 Estimating proportions
7.4 Deriving the binomial distribution
7.5 Summary
7.6 Quick Formula Summary
Interleaf 4 Biology and the history of statistics
8.0 Fitting probability models to frequency data
8.1 X^2 goodness-of-fit test: the proportional model
8.2 Assumptions of the X^2 goodness-of-fit test
8.3 Goodness-of-fit tests when there are only two categories
8.4 Random in space or time: the Poisson distribution
8.5 Summary
8.6 Quick Formula Summary
Interleaf 5 Making a plan
9.0 Contingency analysis: Associations between categorical variables
9.1 Associating two categorical variables
9.2 Estimating association in 2 × 2 tables: relative risk
9.3 Estimating association in 2x2 tables: the odds ratio
9.4 The x^2 contingency test
9.5 Fisher's exact test
9.6 Summary
9.7 Quick Formula Summary
PR1 Review Problems 1
PART 3 COMPARING NUMERICAL VALUES
10.0 The normal distribution
10.1 Bell-shaped curves and the normal distribution
10.2 The formula for the normal distribution
10.3 Properties of the normal distribution
10.4 The standard normal distribution and statistical tables
10.5 The normal distribution of sample means
10.6 Central limit theorem
10.7 Normal approximation to the binomial distribution
10.8 Summary
10.9 Quick Formula Summary
Interleaf 6 Controls in medical studies
11.0 Inference for a normal population
11.1 The t-distribution for sample means
11.2 The confidence interval for the mean of a sample distribution
11.3 The one-sample t-test
11.4 Assumptions of the one-sample t-test
11.5 Estimating the standard deviation and variance of a normal population
11.6 Summary
11.7 Quick Formula Summary
12.0 Comparing two means
12.1 Paired sample versus two independent samples
12.2 Paired comparison of means
12.3 Two-sample comparison of means
12.4 Using the correct sampling units
12.5 The fallacy of indirect comparison
12.6 Interpreting overlap of confidence intervals
12.7 Comparing variances
12.8 Summary
12.9 Quick Formula Summary
Interleaf 7 Which test should I use?
13.0 Handling violations of assumptions
13.1 Detecting deviations from normality
13.2 When to ignore violations of assumptions
13.3 Data transformations
13.4 Nonparametric alternatives to one-sample and paired t-tests
13.5 Comparing two groups: the Mann-Whitney U-test
13.6 Assumptions of nonparametric tests
13.7 Type I and Type II error rates of nonparametric methods
13.8 Permutation tests
13.9 Summary
13.10 Quick Formula Summary
RP2 Review Problems 2
14.0 Designing experiments
14.1 Lessons from clinical trials
14.2 How to reduce bias
14.3 How to reduce the influence of sampling error
14.4 Experiments with more than one factor
14.5 What if you can't do experiments?
14.6 Choosing a sample size
14.7 Summary
14.8 Quick Formula Summary
Interleaf 8 Data dredging
15.0 Comparing means of more than two groups
15.1 The analysis of variance
15.2 Assumptions and alternatives
15.3 Planned comparisons
15.4 Unplanned comparisons
15.5 Fixed and random effects
15.6 ANOVA with randomly chosen groups
15.7 Summary
15.8 Quick Formula Summary
Interleaf 9 Experimental and statistical mistakes
PART 4 REGRESSION AND CORRELATION
16.0 Correlation between numerical variables
16.1 Estimating a linear correlation coefficient
16.2 Testing the null hypothesis of zero correlation
16.3 Assumptions
16.4 The correlation coefficient depends on the range
16.5 Spearman's rank correlation
16.6 The effects of measurement error on correlation
16.7 Summary
16.8 Quick Formula Summary
Interleaf 10 Publication bias
17.0 Regression
17.1 Linear Regression
17.2 Confidence in predictions
17.3 Testing hypotheses about a slope
17.4 Regression toward the mean
17.5 Assumptions of regression
17.6 Transformations
17.7 The effects of measurement error on regression
17.8 Regression with nonlinear relationships
17.9 Logistic regression: fitting a binary response variable
17.10 Summary
17.11 Quick Formula Summary
Interleaf 11 Meta-analysis
RP3 Review Problems 3
PART 5 MODERN STATISTICAL METHODS
18.0 Multiple explanatory variables
18.1 ANOVA and linear regression are linear models
18.2 Analyzing experiments with blocking
18.3 Analyzing factorial designs
18.4 Adjusting for the effects of a covariate
18.5 Assumptions of general linear models
18.6 Summary
Interleaf 12 Using species as data points
19.0 Computer-intensive methods
19.1 Hypothesis testing using simulation
19.2 Bootstrap standard errors and confidence intervals
19.3 Summary
20.0 Likelihood
20.1 What is the likelihood?
20.2 Two uses of likelihood in biology
20.3 Maximum likelihood estimation
20.4 Versatility of maximum likelihood estimation
20.5 Log-likelihood ratio test
20.6 Summary
20.7 Quick Formula Summary
21.0 Survivorship analysis
21.1 Survival curves
21.2 Comparing two survival curves
21.3 Summary
21.4 Quick Formula Summary
BACK MATTER
Statistical tables
Literature cited
Answers to practice problems
Index
Authors

Michael C. Whitlock
Michael Whitlock is an evolutionary biologist and population geneticist. He is a professor of zoology at the University of British Columbia, where he has taught statistics to biology students since 1995. Whitlock is known for his work on the spatial structure of biological populations, genetic drift, and the genetics of adaptation. He has worked with fungus beetles, rhinos, and fruit flies; mathematical theory; and statistical genetics. He is a fellow of the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science. He has been Editor-in-Chief of The American Naturalist and on the editorial boards of nine scientific journals.

Dolph Schluter
Dolph Schluter is a Professor and Canada Research Chair in the Zoology Department and Biodiversity Research Center at the University of British Columbia. He is known for his research on the ecology and evolution of Galapagos finches and threespine stickleback. He is a fellow of the Royal Societies of Canada and London and a foreign associate of the Academy of Arts and Sciences.
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Michael C. Whitlock; Dolph Schluter | Third Edition | ©2020 | ISBN:9781319226268
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