Textbook
Biostatistics: A Foundation for Analysis in the Health Sciences, 10th EditionJanuary 2013, ©2013

Description
This 10th edition of Biostatistics: A Foundation for Analysis in the Health Sciences, 10th Edition should appeal to the same audience for which the first nine editions were written: advanced undergraduate students, beginning graduate students, and health professionals in need of a reference book on statistical methodology. Like its predecessors, this edition requires few mathematical prerequisites. Only reasonable proficiency in algebra is required for an understanding of the concepts and methods underlying the calculations. The emphasis continues to be on an intuitive understanding of principles rather than an understanding based on mathematical sophistication. For most of the statistical techniques covered in this edition, we discuss the capabilities of one or more software packages (MINITAB, SAS, SPSS, and NCSS) that may be used to perform the calculations needed for their application. Resulting screen displays are also shown.
Table of Contents
CHAPTER 1: INTRODUCTION TO BIOSTATISTICS
1.1 Introduction
1.2 Some Basic Concepts
1.3 Measurement and Measurement Scales
1.4 Sampling and Statistical Inference
1.5 The Scientific Method and the Design of Experiments
1.6 Computers and Biostatistical Analysis
1.7 Summary
CHAPTER 2: DESCRIPTIVE STATISTICS
2.1 Introduction
2.2 The Ordered Array
2.3 Grouped Data: The Frequency Distribution
2.4 Descriptive Statistics: Measures of Central Tendency
2.5 Descriptive Statistics: Measures of Dispersion
2.6 Summary
CHAPTER 3: SOME BASIC PROBABILITY CONCEPTS
3.1 Introduction
3.2 Two Views of Probability: Objective and Subjective
3.3 Elementary Properties of Probability
3.4 Calculating the Probability of an Event
3.5 Bayes' Theorem, Screening Tests, Sensitivity, Specificity, and Predictive Value Positive and Negative
CHAPTER 4: PROBABILITY DISTRIBUTIONS
4.1 Introduction
4.2 Probability Distribution of Discrete Variables
4.3 The Binomial Distribution
4.4 The Poisson Distribution
4.5 Continuous Probability Distributions
4.6 The Normal Distribution
4.7 Normal Distribution Applications
4.8 Summary
CHAPTER 5: SOME IMPORTANT SAMPLING DISTRIBUTIONS
5.1 Introduction
5.2 Sampling Distributions
5.3 Distribution of the Sample Mean
5.4 Distribution of the Difference Between Two Sample Means
5.5 Distribution of the Sample Proportion
5.6 Distribution of the Difference Between Two Summary
5.7 Summary
CHAPTER 6: ESTIMATION
6.1 Introduction
6.2 Confidence Interval for a Population Mean
6.3 The t Distribution
6.4 Confidence Interval for the Difference Between Two Population Means
6.5 Confidence Interval for a Population Proportion
6.6 Confidence Interval for the Difference Between Two Population Proportions
6.7 Determination of Sample Size for Estimating Means
6.8 Determination of Sample Size for Estimating Proportions
6.9 Confidence Interval for the Variance of a Normally Distributed Population
6.10 Confidence Interval for the Ratio of the Variances of Two Normally Distributed Populations
6.11 Summary
CHAPTER 7: HYPOTHESIS TESTING
7.1 Introduction
7.2 Hypothesis Testing: A Single Population Mean
7.3 Hypothesis Testing: The Difference Between Two Population Means
7.4 Paired Comparisons
7.5 Hypothesis Testing: A Single Population Proportion
7.6 Hypothesis Testing: The Difference Between Two Population Proportions
7.7 Hypothesis Testing: A Single Population Variance
7.8 Hypothesis Testing: The Ratio of Two Population Variances
7.9 The Type II Error and the Power of a Test
7.10 Determining Sample Size to Control Type II Errors
7.11 Summary
CHAPTER 8: ANALYSIS OF VARIANCE
8.1 Introduction
8.2 The Completely Randomized Design
8.3 The Randomized Complete Block Design
8.4 The Repeated Measures Design
8.5 The Factorial Experiment
8.6 Summary
CHAPTER 9: SIMPLE LINEAR REGRESSION AND CORRELATION
9.1 Introduction
9.2 The Regression Model
9.3 The Sample Regression Equation
9.4 Evaluating the Regression Equation
9.5 Using the Regression Equation
9.6 The Correlation Model
9.7 The Correlation Coefficient
9.8 Some Precautions
9.9 Summary
CHAPTER 10: MULTIPLE REGRESSION AND CORRELATION
10.1 Introduction
10.2 The Multiple Linear Regression Model
10.3 Obtaining the Multiple Regression Equation
10.4 Evaluating the Multiple Regression Equation
10.5 Using the Multiple Regression Equation
10.6 The Multiple Correlation Model
10.7 Summary
CHAPTER 11: REGRESSION ANALYSIS: SOME ADDITIONAL TECHNIQUES
11.1 Introduction
11.2 Qualitative Independent Variables
11.3 Variable Selection Procedures
11.4 Logistic Regression
11.5 Summary
CHAPTER 12: THE CHISQUARE DISTRIBUTION AND THE ANALYSIS OF FREQUENCIES
12.1 Introduction
12.2 The Mathematical Properties of the ChiSquare Distribution
12.3 Tests of GoodnessofFit
12.4 Tests of Independence
12.5 Tests of Homogeneity
12.6 The Fisher Exact Test
12.7 Relative Risk, Odds Ratio, and the MantelHaenszel Statistics
12.8 Summary
CHAPTER 13: NONPARAMETRIC AND DISTRIBUTIONFREE STATISTICS
13.1 Introduction
13.2 Measurement Scales
13.3 The Sign Test
13.4 The Wilcoxon SignedRank Test for Location
13.5 The Median Test
13.6 The MannWhitney Test
13.7 The KolmogorovSmirnov GoodnessofFit Test
13.8 The KruskalWallis OneWay Analysis of Variance by Ranks
13.9 The Friedman TwoWay Analysis of Variance by Ranks
13.10 The Spearman Rank Correlation Coefficient
13.11 Nonparametric Regression Analysis
13.12 Summary
CHAPTER 14: SURVIVAL ANALYSIS
14.1 Introduction
14.2 TimetoEvent Data and Censoring
14.3 The KaplanMeier Procedure
14.4 Comparing Survival Curves
14.5 Cox Regression: The Proportional Hazards Model
14.6 Summary
CHAPTER 15: VITAL STATISTICS (ONLINE)
15.1 Introduction
15.2 Death Rates and Ratios
15.3 Measures of Fertility
15.4 Measure of Morbidity
15.5 Summary
APPENDIX: STATISTICAL TABLES
New To This Edition
 ANOVA/Logistic Regression: Chapter 8 has been revised to expand coverage of ANOVA and linear regression, as well as strengthening the ties between it and Chapters 911.
 Survival Analysis/Vital Statistics: Survival analysis coverage has been expanded to an entire chapter.
 Solutions: Increased numbers of full writeups of the results obtained in examples and problems to provide instructors and students with more complete information.
 Online Examples: Online examples using several technologies (SPSS, SAS, MINITAB, R) to show instructors and students how each technology can be used to solve problems.
The Wiley Advantage
 Realworld practice through examples and exercises using data from actual research projects and reports.
 Highlighting of main ideas with bulleted objectives at the start of each chapter.
 Screen captures and technology boxes with stepbystep help.
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