CHAPTER 1 INTRODUCTION
1.1 Some Definitions
1.2 The Development of Biometry
1.3 The Statistical Frame of Mind
CHAPTER 2 DATA IN BIOLOGY
2.1 Samples and Populations
2.2 Variables in Biology
2.3 Accuracy and Precision of Data
2.4 Derived Variables
2.5 Frequency Distributions
CHAPTER 3 COMPUTERS AND DATA ANALYSIS
3.1 Computers
3.2 Software
3.3 Efficiency and Economy in Data Processing
CHAPTER 4 DESCRIPTIVE STATISTICS
4.1 The Arithmetic Mean
4.2 Other Means
4.3 The Median
4.4 The Mode
4.5 Sample Statistics and Parameters
4.6 The Range
4.7 The Standard Deviation
4.8 Coding Data Before Computation
4.9 The Coefficient of Variation
CHAPTER 5 INTRODUCTION TO PROBABILITY DISTRIBUTION: BINOMIAL AND POISSON
5.1 Probability, Random Sampling, and Hypothesis Testing
5.2 The Binomial Distribution
5.3 The Poisson Distribution
5.4 Other Discrete Probability Distributions
CHAPTER 6 THE NORMAL PROBABILITY DISTRIBUTION
6.1 Frequency Distributions of Continuous Variables
6.2 Properties of the Normal Distribution
6.3 A Model for the Normal Distribution
6.4 Applications of the Normal Distribution
6.5 Fitting a Normal Distribution to Observed Data
6.6 Skewness and Kurtosis
6.7 Graphic Methods
6.8 Other Continuous Distributions
CHAPTER 7 ESTIMATION AND HYPOTHESIS TESTING
7.1 Introduction to hypothesis testing: randomization approaches
7.2 Distribution and Variance of Means
7.3 Distribution and Variance of Other Statistics
7.4 The t-Distribution
7.5 More on hypothesis testing: normally distributed data
7.6 Power of a test
7.7 Tests of Simple Hypotheses Using the Normal and t-Distributions
7.8 The Chi-Square Distribution
7.9 Testing the Hypothesis H0:
7.10 Introduction to interval estimation (Confidence Limits)
7.11 Confidence Limits Using Sample Standard Deviations
7.12 Confidence Limits for Variances
7.13 The Jackknife and the Bootstrap
CHAPTER 8 INTRODUCTION TO THE ANALYSIS OF VARIANCE
8.1 Variances of Samples and Their Means
8.2 The F-Distribution
8.3 The Hypothesis H0
8.4 Heterogeneity Among Sample Means
8.5 Partitioning the Total Sum of Squares and Degrees of Freedom
8.6 Model I Anova
8.7 Model II Anova
CHAPTER 9 SINGLE-CLASSIFICATION ANALYSIS OF VARIANCE
9.1 Computational Formulas
9.2 General Case: Unequal and Equal n
9.3 Special Case: Two Groups
9.4 Comparisons Among Means in a Model I anova: Essential Background
9.5 Comparisons Among Means: Special Methods
CHAPTER 10 NESTED ANALYSIS OF VARIANCE
10.1 Nested Anova: Design
10.2 Nested Anova: Computation
10.3 Nested Anovas With Unequal Sample Sizes
CHAPTER 11 TWO-WAY AND MULTIWAY ANALYSIS OF VARIANCE
11.1 Two-Way Anova: Design
11.2 Two-Way Anova With Equal Replication: Computation
11.3 Two-Way Anova: Hypothesis Testing
11.4 Two-Way Anova Without Replication
11.5 Paired Comparisons
11.6 The Factorial Design
11.7 A Three-way Factorial Design
11.8 Higher-order Factorial anovas
11.9 Other Designs
11.10 Anova by Computer
CHAPTER 12 STATISTICAL POWER AND SAMPLE SIZE IN THE ANALYSIS OF VARIANCE
12.1 Effect Size
12.2 Noncental t and F-distributions and Confidence Limits for Effect Sizes
12.3 Power in an anova
12.4 Sample size in an anova
12.5 Minimum Detectable Difference
12.6 Post Hoc Power Analysis
12.7 Optimal Allocation of Resources in a Nested Design
12.8 Randomized Blocks and Other Two-way and Multi-way Designs
CHAPTER 13 ASSUMPTIONS OF ANALYSIS OF VARIANCE
13.1 A Fundamental Assumption
13.2 Independence
13.3 Homogeneity of Variances
13.4 Normality
13.5 Transformations
13.6 The Logarithmic Transformation
13.7 The Square-Root Transformation
13.8 The Box--Cox Transformation
13.9 The Arcsine Transformation
13.10 Nonparametric Methods in Lieu of Single-Classification Anovas
13.11 Nonparametric Methods in Lieu of Two-Way Anova
CHAPTER 14 LINEAR REGRESSION
14.1 Introduction to Regression
14.2 Models in Regression
14.3 The Linear Regression Equation
14.4 Tests of Significance in Regression
14.5 More Than One Value of Y for Each Value of X
14.6 The Uses of Regression
14.7 Estimating X from Y
14.8 Comparing two Regression Lines
14.9 Linear Comparisons in Anovas
14.10 Examining Residuals and Transformations in Regression
14.11 Nonparametric Tests for Regression
14.12 Model II Regression
14.13 Effect Size, Power, and Sample Size in Regression
CHAPTER 15 CORRELATION
15.1 Correlation versus Regression
15.2 The Product--Moment Correlation Coefficient
15.3 Computing the Product--Moment Correlation Coefficient
15.4 The Variance of Sums and Differences
15.5 Significance Tests in Correlation
15.6 Applications of Correlation
15.7 Nonparametric Tests for Association
15.8 Major Axes and Confidence Regions
15.9 Effect Size, Power, and Sample Size in Correlation
CHAPTER 16 MULTIPLE AND CURVILINEAR REGRESSION
16.1 Multiple Regression: Computation
16.2 Multiple Regression: Significance Tests
16.3 Path Analysis and structural equation modeling
16.4 Partial and Multiple Correlation
16.5 Selection of Independent Variables
16.6 Computation of Multiple Regression by Matrix Methods
16.7 Solving anovas as Regression Problems: General Linear Models
16.8 Analysis of covariance (ancova)
16.9 Curvilinear Regression
16.10 Effect Size, Power, and Sample Size in Multiple Regression
16.11 Advanced Topics in Regression and Correlation
CHAPTER 17 ANALYSIS OF FREQUENCIES
17.1 Introduction to Tests for Goodness of Fit
17.2 Single-Classification Tests for Goodness of Fit
17.3 Replicated Tests of Goodness of Fit
17.4 Tests of Independence: Two-Way Tables
17.5 Analysis of Three-Way and Multiway Tables
17.6 Analysis of Proportions and Logistic Regression
17.7 Randomized Blocks for Frequency Data
17.8 Effect Sizes, Power, and Sample Sizes for frequency data
CHAPTER 18 MISCELLANEOUS METHODS
18.1 Synthesis of prior research results -- Meta-analysis
18.2 Tests for Randomness of Nominal Data: Runs Tests
18.3 Isotonic Regression
18.4 Application of randomization tests to unconventional statistics
18.5 The Mantel test of association between two distance matrices
18.6 The Future of Biometry: Data Analysis