Description
Principles of Biostatistics (2nd Ed.)
Authors: Pagano Marcello, Gauvreau Kimberlee
Language: EnglishSubjects for Principles of Biostatistics:
Keywords
Serum Cholesterol Levels; epidemiology; Null Hypothesis; Health statistics; Dichotomous Random Variable; health sciences; Head Circumference; analysis of variance; Relative Odds; noparametric methods; Scatter Plot; Kimberlee Gauvreau; Low Birth Weight Infants; Confidence Interval; Gestational Age; PDI Score; MDI Score; Data Set; Systolic Blood Pressure; Younger Men; Age Specific Death Rates; Mantel Haenszel Method; Gestational Age Increases; Standard Normal Curve; Binomial Random Variable; Life Table Method; Population O
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Description
/li>Contents
/li>Biography
/li>
This edition is a reprint of the second edition published in 2000 by Brooks/Cole and then Cengage Learning.
Principles of Biostatistics is aimed at students in the biological and health sciences who wish to learn modern research methods. It is based on a required course offered at the Harvard School of Public Health. In addition to these graduate students, many health professionals from the Harvard medical area attend as well.
The book is divided into three parts. The first five chapters deal with collections of numbers and ways in which to summarize, explore, and explain them. The next two chapters focus on probability and introduce the tools needed for the subsequent investigation of uncertainty. It is only in the eighth chapter and thereafter that the authors distinguish between populations and samples and begin to investigate the inherent variability introduced by sampling, thus progressing to inference. Postponing the slightly more difficult concepts until a solid foundation has been established makes it easier for the reader to comprehend them.
The supplements include a manual for students with solutions for odd-numbered exercises, a manual for instructors with solutions to all exercises, and selected data sets.
Marcello Pagano is Professor of Statistical Computing in the Department of Biostatistics at the Harvard School of Public Health. His research in biostatistics is on computer intensive inference and surveillance methods that involve screening methodologies, with their associated laboratory tests, and in obtaining more accurate testing results that use existing technologies.
Kimberlee Gauvreau is Associate Professor in the Department of Biostatistics and Associate Professor of Pediatrics at Harvard Medical School. Dr. Gauvreau?s research focuses on biostatistical issues arising in the field of pediatric cardiology. She also works on the development and validation of methods of adjustment for case mix complexity.
1. Introduction
2. Data Presentation
Types of Numerical Data
Nominal Data
Ordinal Data
Ranked Data
Discrete Data
Continuous Data
Tables
Frequency Distributions
Relative Frequency
Graphs
Bar Charts
Histograms
Frequency Polygons
One-Way Scatter Plots
Box Plots
Two-Way Scatter Plots
Line Graphs
Further Applications
Review Exercises
3. Numerical Summary Measures
Measures of Central Tendency
Mean
Median
Mode
Measures of Dispersion
Range
Interquartile Range
Variance and Standard Deviation
Coefficient of Variation
Grouped Data
Grouped Mean
Grouped Variance
Chebychev's Inequality
Further Applications
Review Exercises
4. Rates and Standardization
Rates
Standardization of Rates
Direct Method of Standardization
Indirect Method of Standardization
Use of Standardized Rates
Further Applications
Direct Method of Standardization
Indirect Method of Standardization
Review Exercises
5. Life Tables
Computation of the Life Table
Applications of the Life Table
Years of Potential Life Lost
Further Applications
Review Exercises
6. Probability
Operations on Events and Probability
Conditional Probability
Bayes' Theorem
Diagnostic Tests
Sensitivity and Specificity
Applications of Bayes' Theorem
ROC Curves
Calculation of Prevalence
The Relative Risk and the Odds Ratio
Further Applications
Review Exercises
7. Theoretical Probability Distributions
Probability Distributions
The Binomial Distribution
The Poisson Distribution
The Normal Distribution
Further Applications
Review Exercises
8. B Sampling Distribution of the Mean
B Sampling Distributions
The Central Limit Theorem
Applications of the Central Limit Theorem
Further Applications
Review Exercises
9. Confidence Intervals
Two-Sided Confidence Intervals
One-Sided Confidence Intervals
Student's t Distribution
Further Applications
Review Exercises
10. Hypothesis Testing
General Concepts
Two-Sided Tests of Hypotheses
One-Sided Tests of Hypotheses
Types of Error
Power
Sample Size Estimation
Further Applications
Review Exercises
11. Comparison of Two Means
Paired Samples
Independent Samples
Equal Variances
Unequal Variances
Further Applications
Review Exercises
12. Analysis of Variance
One-Way Analysis of Variance
The Problem
Sources of Variation
Multiple Comparisons Procedures
Further Applications
Review Exercises
13. Nonparametric Methods
The Sign Test
The Wilcoxon Signed-Rank Test
The Wilcoxon Rank Sum Test
Advantages and Disadvantages of
Nonparametric Methods
Further Applications
Review Exercises
14. Inference on Proportions
Normal Approximation to the Binomial Distribution
Sampling Distribution of a Proportion
Confidence Intervals
Hypothesis Testing
Sample Size Estimation
Comparison of Two Proportions
Further Applications
Review Exercises
15. Contingency Tables
The Chi-Square Test
X Tables
r X c Tables
McNemar's Test
The Odds Ratio
Berkson's Fallacy
Further Applications
Review Exercises
16. Multiple x Tables
Simpsons Paradox
The Mantei-Haenszel Method
Test of Homogeneity
Summary Odds Ratio
Test of Association
Further Applications
Review Exercises
17. Correlation
The Two-Way Scatter Plot
Pearson's Correlation Coefficient
Spearman's Rank Correlation Coefficient
Further Applications
Review Exercises
18. Simple Linear Regression
Regression Concepts
The Model
The Population Regression Line
The Method of Least Squares
Inference for Regression Coefficients
Inference for Predicted Values
Evaluation of the Model
The Coefficient of Determination
Residual Plots
Transformations
Further Applications
Review Exercises
19. Multiple Regression
The Model
The Least-Squares Regression Equation
Inference for Regression Coefficients
Evaluation of the Model
Indicator Variables
Interaction Terms
Model Selection
Further Applications
Review Exercises
20. Logistic Regression
The Model
The Logistic Function
The Fitted Equation
Multiple Logistic Regression
Indicator Variables
Further Applications
Review Exercises
21. Survival Analysis
The Life Table Method
The Product-Limit Method
The Log-Rank Test
Further Applications
Review Exercises
22. Sampling Theory
Sampling Schemes
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Nonprobability Sampling
Sources of Bias
Further Applications
Review Exercises
Marcello Pagano is Professor of Statistical Computing in the Department of Biostatistics at the Harvard School of Public Health. His research in biostatistics is on computer intensive inference and surveillance methods that involve screening methodologies, with their associated laboratory tests, and in obtaining more accurate testing results that use existing technologies.
Kimberlee Gauvreau is Associate Professor in the Department of Biostatistics and Associate Professor of Pediatrics at Harvard Medical School. Dr. Gauvreau’s research focuses on biostatistical issues arising in the field of pediatric cardiology. She also works on the development and validation of methods of adjustment for case mix complexity.
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