Fundamentals of Biostatistics 8th edition by Bernard Rosner 130526892X 9798214344201

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ISBN 10: 130526892X
ISBN 13: 9798214344201
Author: Bernard Rosner

Bernard Rosner’s FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. Most methods are illustrated with specific instructions as to

Fundamentals of Biostatistics 8th Table of contents:

Chapter 1. General Overview
Section Content

Chapter 2. Descriptive Statistics
2.1. Introduction
2.2. Measures of Location

• The Arithmetic Mean
• The Median
• Comparison of the Arithmetic Mean and the Median
• The Mode
• The Geometric Mean
  2.3. Some Properties of the Arithmetic Mean
  2.4. Measures Of Spread
• The Range
• Quantiles
• The Variance and Standard Deviation
  2.5. Some Properties of the Variance and Standard Deviation
  2.6. The Coefficient of Variation
  2.7. Grouped Data
  2.8. Graphic Methods
• Bar Graphs
• Stem-and-Leaf Plots
• Box Plots
  2.9. Case Study 1: Effects of Lead Exposure on Neurological and Psychological Function in Children
  2.10. Case Study 2: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women
  2.11. Obtaining Descriptive Statistics on the Computer
  2.12. Summary
  Problems

Chapter 3. Probability
3.1. Introduction
3.2. Definition of Probability
3.3. Some Useful Probabilistic Notation
3.4. The Multiplication Law of Probability
3.5. The Addition Law of Probability
3.6. Conditional Probability

• Total-Probability Rule
  3.7. Bayes’ Rule and Screening Tests
• Bayes’ Rule
  3.8. Bayesian Inference
  3.9. ROC Curves
  3.10. Prevalence and Incidence
  3.11. Summary
  Problems

Chapter 4. Discrete Probability Distributions
4.1. Introduction
4.2. Random Variables
4.3. The Probability-Mass Function for a Discrete Random Variable

• Relationship of Probability Distributions to Frequency Distributions
  4.4. The Expected Value of a Discrete Random Variable
  4.5. The Variance of a Discrete Random Variable
  4.6. The Cumulative-Distribution Function of a Discrete Random Variable
  4.7. Permutations and Combinations
  4.8. The Binomial Distribution
• Using Binomial Tables
• Using “Electronic” Tables
  4.9. Expected Value and Variance of the Binomial Distribution
  4.10. The Poisson Distribution
• Computation of Poisson Probabilities
• Using Poisson Tables
• Electronic Tables for the Poisson Distribution
  4.11. Expected Value and Variance of the Poisson Distribution
  4.12. Poisson Approximation to the Binomial Distribution
  4.13. Summary
  Problems

Chapter 5. Continuous Probability Distributions
5.1. Introduction
5.2. General Concepts
5.3. The Normal Distribution
5.4. Properties of the Standard Normal Distribution

• Using Normal Tables
• Using Electronic Tables for the Normal Distribution
  5.5. Conversion from an N(μ, σ²) Distribution to an N(0, 1) Distribution
  5.6. Linear Combinations of Random Variables
  5.7. Normal Approximation to the Binomial Distribution
  5.8. Normal Approximation to the Poisson Distribution
  5.9. Summary
  Problems

Chapter 6. Estimation
6.1. Introduction
6.2. The Relationship Between Population and Sample
6.3. Random-Number Tables
6.4. Randomized Clinical Trials

• Design Features of Randomized Clinical Trials
  6.5. Estimation of the Mean of a Distribution
• Point Estimation
• Standard Error of the Mean
• Central-Limit Theorem
• Interval Estimation
• t Distribution
  6.6. Case Study: Effects of Tobacco Use on Bone-Mineral Density (BMD) in Middle-Aged Women
  6.7. Estimation of the Variance of a Distribution
• Point Estimation
• The Chi-Square Distribution
• Interval Estimation
  6.8. Estimation for the Binomial Distribution
• Point Estimation
• Interval Estimation—Normal-Theory Methods
• Interval Estimation—Exact Methods
  6.9. Estimation for the Poisson Distribution
• Point Estimation
• Interval Estimation
  6.10. One-Sided Confidence Intervals
  6.11. The Bootstrap
  6.12. Summary
  Problems

Chapter 7. Hypothesis Testing: One-Sample Inference
7.1. Introduction
7.2. General Concepts
7.3. One-Sample Test for the Mean of a Normal Distribution: One-Sided Alternatives
7.4. One-Sample Test for the Mean of a Normal Distribution: Two-Sided Alternatives

• Using the Computer to Perform the One-Sample t Test
• One-Sample z Test
  7.5. The Relationship Between Hypothesis Testing and Confidence Intervals
  7.6. The Power of a Test
• One-Sided Alternatives
• Two-Sided Alternatives
• Using the Computer to Estimate Power
  7.7. Sample-Size Determination
• One-Sided Alternatives
• Sample-Size Determination (Two-Sided Alternatives)
• Using the Computer to Estimate Sample Size
• Sample-Size Estimation Based on CI Width
  7.8. One-Sample χ² Test for the Variance of a Normal Distribution
  7.9. One-Sample Inference for the Binomial Distribution
• Normal-Theory Methods
• Using the Computer to Perform the One-Sample Binomial Test (Normal Theory Method)
• Exact Methods
• Using the Computer to Perform the One-Sample Binomial Test (Exact Version)
• Power and Sample-Size Estimation
• Using the Computer to Estimate Power and Sample Size for the One-Sample Binomial Test
  7.10. One-Sample Inference for the Poisson Distribution
  7.11. Case Study: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women
  7.12. Derivation of Selected Formulas
• Derivation of Equation 7.23
  7.13. Summary
  Problems

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