Statistical methods in the biological and health sciences / J. Susan Milton.

Includes index : p. 587-588

Contents
1 Descriptive Methods
1.1 Distribution Tables: Discrete Data
Bar Graphs
Bivariate Data: Two-Way Tables
1.2 A Quick Look at Distribution: Stem and Leaf
Constructing a Simple Stem-and-Leaf Diagram
1.3 Frequency Distributions: Histograms
Rules for Breaking Data into Classes
Cumulative Distribution
1.4 Measures of Location or Central Tendency
Sample Mean
Sample Median
1.5 Measures of Variability or Dispersion
Sample Variance
Sample Standard Deviation
Sample Range
Interquartile Range
Finding the Sample Interquartile Range
Multiple Data Sets
1.6 Box Plots
Constructing a Box Plot
1.7 Handling Grouped Data
2 Introduction to Probability and Counting
2.1 Interpreting Probablilities
2.2 Tree Diagrams and Elementary Genetics
Elementary Genetics
2.3 Permutations and Combinations
2.4 Multiplication Principle
Guidelines for Using the Multiplication Principle
2.5 Permutations of Indistinguishable Objects
2.6 Combinations
3 Probability and Problem Solving
3.1 Venn Diagrams and the Axioms of Probability
Venn Diagrams
Axioms of Probability
3.2 General Addition Rule
3.3 Conditional Probability
3.4 Diagnostic Tests and Relative Risk
Relative Risk
3.5 Independence
3.6 The Multiplication Rule
3.7 Bayes' Theorem
4 Discrete Random Variables
4.1 Discrete and Continuous Variables
4.2 Discrete Density Functions and Expectation
Expectation
4.3 Cumulative Distribution Function
4.4 Binomial Distribution
Expected Value and Variance: Binomial
Calculating Binomial Probabilities: Cumulative Distribution
4.5 Poisson Distribution
5 Continuous Random Variables
5.1 Continuous Random Variables
Expectation
5.2 Cumulative Distribution Function
5.3 Normal Distribution
Properties of Normal Curves
Standard Normal Distribution
Standardization
5.4 Normal Probability Rule and Medical Tables
6 Inferences on the Mean
6.1 Random Sampling and Randomization
Simple Random Sampling
Randomization
6.2 Point Estimation of the Mean and Introduction to Interval Estimation:
Central Limit Theorem
Interval Estimation
Central Limit Theorem
6.3 Confidence Interval on the Population Mean and the T Distribution
Properties of T Random Variables
6.4 Introduction to Hypothesis Testing
6.5 Testing Hypotheses on the Population
Mean: T Test
Preset Alpha Values
6.6 Sample Size: Confidence Intervals and Power
Sample Size: Hypothesis Testing
7 Chi-Squared Distribution and Inferences on the Variance
7.1 Chi-Squared Distribution and Interval
Estimation of the Population Variance
Confidence Interval on s2
7.2 Testing Hypotheses on the Population Variance
8 Inferences on Proportions
8.1 Point Estimation
8.2 Interval Estimation of p
8.3 Sample Size for Estimating p
8.4 Hypothesis Testing on p
8.5 Comparing Two Proportions: Estimation
Confidence Interval on the Difference in Two Proportions
8.6 Comparing Two Proportions: Hypothesis Testing
Testing That the Null Value Is Zero:
Pooled Test
9 Comparing Two Means and Two Variances
9.1 Comparing Two Means and Two Variances
9.2 Comparing Variances: F Distribution
Rule of Thumb Variance Comparison
F Test for Comparing Variances: F Distribution
9.3 Inferences on m1 - m2: Pooled T
Interval Estimation of m1 - m2
Pooled T Tests
9.4 Inferences on m1 - m2: Unequal Variances
9.5 Inferences on m1 - m2: Paired T
Paired T Test
10 k-Sample Procedures: Introduction to Design
10.1 One-Way Classification, Completely Random Design with Fixed Effects
Data Format and Notation
10.2 Paired and Multiple Comparisons
Bonferroni T Tests: Paired Comparisons
Duncan's Multiple Range Test
A Note on Computing
10.3 Random Effects
10.4 Randomized Complete Blocks
Data Format and Notation
Testing HO: m1. = m2. = @ @ @ = mk.
Effectiveness of Blocking
Paired and Multiple Comparisons
A Note on Computing
10.5 Factorial Experiments
Data Format and Notation
Testing Main Effects and Interaction
Multiple and Paired Comparisons
A Note on Computing
11 Regression and Correlation
11.1 Introduction To Simple Linear Regression
11.2 Method of Least Squares
Estimating an Individual Response
A Note on Computing
11.3 Introduction to Correlation
Estimating r
11.4 Evaluating the Strength of the Linear Relationship
Coefficient of Determination
Analysis of Variance
A Note on Computing
11.5 Confidence Interval Estimation
11.6 Multiple Regression
12 Categorical Data
12.1 2 ´ 2 Contingency Tables
Test of Independence
Test of Homogeneity
12.2 r ´ c Contingency Tables
13 Some Additional Procedures and Distribution-Free Alternatives
13.1 Testing for Normality: The Lilliefors Test
13.2 Tests of Location: One Sample
Sign Test for Median
Wilcoxon Signed-Rank Test
13.3 Tests of Location: Paired Data
Sign Test for Median Difference
Wilcoxon Signed-Rank Test: Paired Data
13.4 Tests of Location: Unmatched Data
Wilcoxon Rank-Sum Test
13.5 Kruskal-Wallis k-Sample Test for
Location: Unmatched Data
Kruskal-Wallis k-Sample Test
13.6 Friedman k-Sample Test for Location: Matched Data
Friedman Test
13.7 Correlation
Spearman's Rank Correlation Coefficient
13.8 Bartlett's Test for Equality of Variances
13.9 Normal Approximations
13.10 A Small Sample Test on Proportions