Fundamentals of Biostatistics 8th edition

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• Chapter 1: General Overview
  • 1: Concept Explorations
  
  
• Chapter 2: Descriptive Statistics
  • 2: Concept Explorations
  • 2.1: Introduction
  • 2.2: Measures of Location
  • 2.3: Some Properties of the Arithmetic Mean
  • 2.4: Measures of Spread
  • 2.5: Some Properties of the Variance and Standard Deviation
  • 2.6: The Coefficient of Variation
  • 2.7: Grouped Data
  • 2.8: Graphic Methods
  • 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
  • 2: Problems
  • 2: Labs
  
  
• Chapter 3: Probability
  • 3: Concept Explorations
  • 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
  • 3.7: Bayes' Rule and Screening Tests
  • 3.8: Bayesian Inference
  • 3.9: ROC Curves
  • 3.10: Prevalence and Incidence
  • 3.11: Summary
  • 3: Problems
  
  
• Chapter 4: Discrete Probability Distributions
  • 4: Concept Explorations
  • 4.1: Introduction
  • 4.2: Random Variables
  • 4.3: The Probability-Mass Function for a Discrete Random Variable
  • 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
  • 4.9: Expected Value and Variance of the Binomial Distribution
  • 4.10: The Poisson Distribution
  • 4.11: Computation of Poisson Probabilities
  • 4.12: Expected Value and Variance of the Poisson Distribution
  • 4.13: Poisson Approximation to the Binomial Distribution
  • 4.14: Summary
  • 4: Problems
  • 4: Labs
  
  
• Chapter 5: Continuous Probability Distributions
  • 5: Concept Explorations
  • 5.1: Introduction
  • 5.2: General Concepts
  • 5.3: The Normal Distribution
  • 5.4: Properties of the Standard 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
  • 5: Problems
  
  
• Chapter 6: Estimation
  • 6: Concept Explorations
  • 6.1: Introduction
  • 6.2: The Relationship Between Population and Sample
  • 6.3: Random-Number Tables
  • 6.4: Randomized Clinical Trials
  • 6.5: Estimation of the Mean of a 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
  • 6.8: Estimation for the Binomial Distribution
  • 6.9: Estimation for the Poisson Distribution
  • 6.10: One-Sided Confidence Intervals
  • 6.11: The Bootstrap
  • 6.12: Summary
  • 6: Problems
  • 6: Labs
  
  
• Chapter 7: Hypothesis Testing: One-Sample Inference
  • 7: Concept Explorations
  • 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
  • 7.5: The Relationship Between Hypothesis Testing and Confidence Intervals
  • 7.6: The Power of a Test
  • 7.7: Sample-Size Determination
  • 7.8: One-Sample Χ² Test for the Variance of a Normal Distribution
  • 7.9: One-Sample Inference for the Binomial Distribution
  • 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
  • 7.13: Summary
  • 7: Problems
  • 7: Labs
  
  
• Chapter 8: Hypothesis Testing: Two-Sample Inference
  • 8: Concept Explorations
  • 8.1: Introduction
  • 8.2: The Paired t Test
  • 8.3: Interval Estimation for the Comparison of Means from Two Paired Samples
  • 8.4: Two-Sample t Test for Independent Samples with Equal Variances
  • 8.5: Interval Estimation for the Comparison of Means from Two Independent Samples (Equal Variance Case)
  • 8.6: Testing for the Equality of Two Variances
  • 8.7: Two-Sample t Test for Independent Samples with Unequal Variances
  • 8.8: Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children
  • 8.9: Estimation of Sample Size and Power for Comparing Two Means
  • 8.10: The Treatment of Outliers
  • 8.11: Derivation of Equation 8.13
  • 8.12: Summary
  • 8: Problems
  • 8: Labs
  
  
• Chapter 9: Nonparametric Methods
  • 9: Concept Explorations
  • 9.1: Introduction
  • 9.2: The Sign Test
  • 9.3: The Wilcoxon Signed-Rank Test
  • 9.4: The Wilcoxon Rank-Sum Test
  • 9.5: Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children
  • 9.6: Permutation Tests
  • 9.7: Summary
  • 9: Problems
  
  
• Chapter 10: Hypothesis Testing: Categorical Data
  • 10: Concept Explorations
  • 10.1: Introduction
  • 10.2: Two-Sample Test for Binomial Proportions
  • 10.3: Fisher's Exact Test
  • 10.4: Two-Sample Test for Binomial Proportions for Matched-Pair Data (McNemar's Test)
  • 10.5: Estimation of Sample Size and Power for Comparing Two Binomial Proportions
  • 10.6: R × C Contingency Tables
  • 10.7: Chi-Square Goodness-of-Fit Test
  • 10.8: The Kappa Statistic
  • 10.9: Derivation of Selected Formulas
  • 10.10: Summary
  • 10: Problems
  • 10: Labs
  
  
• Chapter 11: Regression and Correlation Methods
  • 11: Concept Explorations
  • 11.1: Introduction
  • 11.2: General Concepts
  • 11.3: Fitting Regression Lines—The Method of Least Squares
  • 11.4: Inferences About Parameters from Regression Lines
  • 11.5: Interval Estimation for Linear Regression
  • 11.6: Assessing the Goodness of Fit of Regression Lines
  • 11.7: The Correlation Coefficient
  • 11.8: Statistical Inference for Correlation Coefficients
  • 11.9: Multiple Regression
  • 11.10: Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children
  • 11.11: Partial and Multiple Correlation
  • 11.12: Rank Correlation
  • 11.13: Interval Estimation for Rank-Correlation Coefficients
  • 11.14: Derivation of Equation 11.26
  • 11.15: Summary
  • 11: Problems
  • 11: Labs
  
  
• Chapter 12: Multisample Inference
  • 12: Concept Explorations
  • 12.1: Introduction to the One-Way Analysis of Variance
  • 12.2: One-Way ANOVA—Fixed-Effects Model
  • 12.3: Hypothesis Testing in One-Way ANOVA—Fixed-Effects Model
  • 12.4: Comparisons of Specific Groups in One-Way ANOVA
  • 12.5: Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children
  • 12.6: Two-Way ANOVA
  • 12.7: The Kruskal-Wallis Test
  • 12.8: One-Way ANOVA—The Random-Effects Model
  • 12.9: The Intraclass Correlation Coefficient
  • 12.10: Mixed Models
  • 12.11: Derivation of Equation 12.30
  • 12.12: Summary
  • 12: Problems
  • 12: Labs
  
  
• Chapter 13: Design and Analysis Techniques for Epidemiologic Studies
  • 13: Concept Explorations
  • 13.1: Introduction
  • 13.2: Study Design
  • 13.3: Measures of Effect for Categorical Data
  • 13.4: Attributable Risk
  • 13.5: Confounding and Standardization
  • 13.6: Methods of Inference for Stratified Categorical Data—The Mantel-Haenszel Test
  • 13.7: Multiple Logistic Regression
  • 13.8: Extensions to Logistic Regression
  • 13.9: Sample Size Estimation for Logistic Regression
  • 13.10: Meta-Analysis
  • 13.11: Equivalence Studies
  • 13.12: The Cross-Over Design
  • 13.13: Clustered Binary Data
  • 13.14: Longitudinal Data Analysis
  • 13.15: Measurement-Error Methods
  • 13.16: Missing Data
  • 13.17: Derivation of 100% × (1 − α) CI for the Risk Difference
  • 13.18: Summary
  • 13: Problems
  
  
• Chapter 14: Hypothesis Testing: Person-Time Data
  • 14.1: Measure of Effect for Person-Time Data
  • 14.2: One-Sample Inference for Incidence-Rate Data
  • 14.3: Two-Sample Inference for Incidence-Rate Data
  • 14.4: Power and Sample-Size Estimation for Person-Time Data
  • 14.5: Inference for Stratified Person-Time Data
  • 14.6: Power and Sample-Size Estimation for Stratified Person-Time Data
  • 14.7: Testing for Trend: Incidence-Rate Data
  • 14.8: Introduction to Survival Analysis
  • 14.9: Estimation of Survival Curves: The Kaplan-Meier Estimator
  • 14.10: The Log-Rank Test
  • 14.11: The Proportional-Hazards Model
  • 14.12: Power and Sample-Size Estimation under the Proportional-Hazards Model
  • 14.13: Parametric Survival Analysis
  • 14.14: Parametric Regression Models for Survival Data
  • 14.15: Derivation of Selected Formulas
  • 14.16: Summary
  • 14: Problems
  
  
• Chapter PJT: Project
  • PJT.1: Project