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Fundamentals of biostatistics / Bernard Rosner.
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Title:Fundamentals of biostatistics / Bernard Rosner.
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Author/Creator:Rosner, Bernard (Bernard A.)
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Published/Created:Boston, MA : Brooks/Cole Cengage Learning, 2016.
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Holdings
Holdings Record Display
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Location:BMB LIBRARY (VGH) stacksWhere is this?
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Call Number: QH323.5 .R67 2016
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Number of Items:1
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Status:Available
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Location:WOODWARD LIBRARY stacksWhere is this?
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Call Number: QH323.5 .R67 2016
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Number of Items:1
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Status:Available
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Location:BMB LIBRARY (VGH) stacksWhere is this?
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Library of Congress Subjects:Biometry.
Medical statistics.
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Medical Subjects: Biometry.
Statistics as Topic.
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Genre/Form:Textbooks.
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Edition:8th ed.
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Description:xix, 927 pages : illustrations ; 26 cm
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Summary:FUNDAMENTALS OF BIOSTATISTICS leads you through the methods, techniques, and computations of statistics necessary for success in the medical field. Every new concept is developed systematically through completely worked out examples from current medical research problems.
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Notes:Includes index.
Includes bibliographical references and index.
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ISBN:9781305268920
130526892X
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Contents:Machine generated contents note: 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. 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
Problems
3.1. Introduction
3.2. Definition of Probability
3.3. Some Useful Probabilistic Notation
3.4. Multiplication Law of Probability
3.5. 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
Problems
4.1. Introduction
4.2. Random Variables
4.3. Probability-Mass Function for a Discrete Random Variable
4.4. Expected Value of a Discrete Random Variable
4.5. Variance of a Discrete Random Variable
4.6. Cumulative-Distribution Function of a Discrete Random Variable
4.7. Permutations and Combinations
4.8. Binomial Distribution
4.9. Expected Value and Variance of the Binomial Distribution
4.10. 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
Problems
5.1. Introduction
5.2. General Concepts
5.3. 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
Problems
6.1. Introduction
6.2. 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. Bootstrap
6.12. Summary
Problems
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. Relationship Between Hypothesis Testing and Confidence Intervals
7.6. Power of a Test
7.7. Sample-Size Determination
7.8. One-Sample x2 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
Problems
8.1. Introduction
8.2. 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. Treatment of Outliers
8.11. Derivation of Equation
8.12. Summary
Problems
9.1. Introduction
9.2. Sign Test
9.3. Wilcoxon Signed-Rank Test
9.4. 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
Problems
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 x C Contingency Tables
10.7. Chi-Square Goodness-of-Fit Test
10.8. Kappa Statistic
10.9. Derivation of Selected Formulas
10.10. Summary
Problems
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. 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
Problems
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. Kruskal-Wallis Test
12.8. One-Way ANOVA-The Random-Effects Model
12.9. Intraclass Correlation Coefficient
12.10. Mixed Models
12.11. Derivation of Equation 12.30
12.12. Summary
Problems
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. 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% x (1-α) CI for the Risk Difference
13.18. Summary
Problems
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. Log-Rank Test
14.11. 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
Problems
1. Exact binomial probabilities Pr(X = k) = (nk)pkqn=k
2. Exact Poisson probabilities Pr(X = k) = e=[µ][µ]k/k!
3. normal distribution
4. Table of 1000 random digits
5. Percentage points of the t distribution (td,u)a
6. Percentage points of the chi-square distribution (x2du)a
7. Confidence limits for the expectation of a Poisson variable ([µ])
8. Percentage points of the F distribution (Fd1,d2,p)
9. Critical values for the ESD (Extreme Studentized Deviate) outlier statistic (ESDn,1-α,α=.05,.01))
10. Two-tailed critical values for the Wilcoxon signed-rank test
11. Two-tailed critical values for the Wilcoxon rank-sum test
12. Fisher's z transformation
13. Two-tailed upper critical values for the Spearman rank-correlation coefficient (rs)
14. Critical values for the Kruskal-Wallis test statistic (H) for selected sample sizes for k = 3
15. Critical values for the studentized range statistic q*, α = .05.