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Experimental design and data analysis for biologists / Gerry P. Quinn, Michael J. Keough. -- Second edition. -- Cambridge, United Kingdom : Cambridge University Press, 2024. – (58.105/Q7/2nd ed.) |
Contents
Preface
List of Acronyms
1 Introduction
1.1 Almost Every Biological Theory or Hypothesis Is a Model of How Nature Works
1.2 We Use Data to Separate Wrong Models from (Possibly) Correct Ones (or Bad Models from Less Bad Ones)
1.3 There Are Many Ways to Reach Wrong Conclusions!
1.4 There Are Also Many Ways to be Right
1.5 Our Philosophy in This Book
1.6 How This Book Is Structured
1.7 A Bit of Housekeeping
2 Things to Know before Proceeding
2.1 Samples, Populations, and Statistical Inference
2.2 Probability
2.3 Probability Distributions
2.4 Frequentist ("Classical") Estimation
2.5 Hypothesis Testing
2.6 Comments on Frequentist Inference
2.7 Bayesian Statistical Inference
3 Sampling and Experimental Design
3.1 Sampling Design
3.2 Experimental Design
3.3 Sample Size for Detecting Differences: Power Analysis
3.4 Key Points
4 Introduction to Linear Models
4.1 What Is a Linear Model?
4.2 Components of Linear Models
4.3 Assembling our Linear Model
4.4 Estimation for Linear Models
4.5 How Well Does a Model Fit?
4.6 Assumptions for Linear Model Inference
4.7 Types of Linear Models
4.8 Key Points
5 Exploratory Data Analysis
5.1 Basic Graphical Tools
5.2 Outliers
5.3 Am I Fitting the Right Model?
5.4 Is It Normal to Transform Data?
5.5 Standardizations
5.6 Missing Data
5.7 Key Points
6 Simple Linear Models with One Predictor
6.1 Linear Model for a Single Continuous Predictor: Linear Regression
6.2 Linear Model for a Single Categorical Predictor (Factor)
6.3 Predictor Effects
6.4 Assumptions
6.5 Model Diagnostics
6.6 Robust Linear Models
6.7 Power of Single-Predictor Linear Models
6.8 Key Points
7 Linear Models for Crossed (Factorial) Designs
7.1 Two-Factor Fully Crossed (Factorial) Designs
7.2 Complex Factorial Designs
7.3 Power and Sample Size in Factorial Designs
7.4 Key Points
8 Multiple Regression Models
8.1 Linear Model for Multiple Continuous Predictors: Multiple Regression
8.2 Analysis of Covariance
8.3 Key Points
9 Predictor Importance and Model Selection in Multiple Regression Models
9.1 Relative Predictor Importance
9.2 Model Selection
9.3 Regression Trees
9.4 Key Points
10 Random Factors in Factorial and Nested Designs
10.1 Fixed vs. Random Effects and Mixed Models
10.2 Fitting Linear Models with Fixed and Random Factors
10.3 Simple Random Factor Designs
10.4 Multilevel Regressions
10.5 Nested (Hierarchical) Designs
10.6 Crossed (Factorial) Mixed Designs
10.7 Key Points
11 Split-Plot (Split-Unit) Designs: Partly Nested Models
11.1 Simple Split-Plot Designs
11.2 More Complex Designs
11.3 Key Points
12 Repeated Measures Designs
12.1 Simple Repeated Measures Designs
12.2 More Complex Repeated Measures Designs
12.3 Key Points
13 Generalized Linear Models for Categorical Responses
13.1 Logistic Regression
13.2 Count Responses: Poisson Regression
13.3 Goodness-of-Fit for GLMs
13.4 Inference for Parameters in GLMs
13.5 Assumptions and Diagnostics for Binomial and Poisson GLMs
13.6 Overdispersed Data
13.7 Contingency Tables
13.8 Generalized Linear Mixed Models
13.9 Generalized Additive Models
13.10 Key Points
14 Introduction to Multivariate Analyses
14.1 Distributions and Associations
14.2 Linear Combinations, Eigenvectors, and Eigenvalues
14.3 Multivariate Distance and Dissimilarity Measures
14.4 Data Transformation and Standardization
14.5 Standardization, Association, and Dissimilarity
14.6 Screening Multivariate Datasets
14.7 Introduction to Multivariate Analyses
14.8 Key Points
15 Multivariate Analyses Based on Eigenanalyses
15.1 Principal Components Analysis
15.2 Correspondence Analysis
15.3 Use of PCA and CA with Ecological Abundance (Count) Data
15.4 Constrained (Canonical) Multivariate Analysis
15.5 Linear Discriminant Function Analysis
15.6 Key Points
16 Multivariate Analyses Based on (Dis)similarities or Distances
16.1 Multidimensional Scaling or Ordination
16.2 Cluster Analysis
16.3 Analyses Based on Dissimilarities
16.4 Key Points
17 Telling Stories with Data
17.1 Research Doesn't Exist Until You Tell Someone
17.2 Summarizing Data Analyses
17.3 Visualizing Data
17.4 Graphical Summaries of the Data
17.5 Error Bars: Visualizing Variation and Precision
17.6 Horses for Courses: What You Present Depends on Who's Listening
17.7 Software and Other Sources
17.8 Key Points
Glossary
References
Index
Color plates can be found between pages 364 and 365
