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    Fundamentals of biostatistics / Bernard Rosner.

    • Title:Fundamentals of biostatistics / Bernard Rosner.
    •    
    • Author/Creator:Rosner, Bernard (Bernard A.)
    • Published/Created:Boston, MA : Brooks/Cole Cengage Learning, 2016.
    • Holdings

       
    • Library of Congress Subjects:Biometry.
      Medical statistics.
    • Medical Subjects: Biometry.
      Statistics as Topic.
    • Genre/Form:Textbooks.
    • Edition:8th ed.
    • Description:xix, 927 pages : illustrations ; 26 cm
    • 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.
    • Notes:Includes index.
      Includes bibliographical references and index.
    • ISBN:9781305268920
      130526892X
    • 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.
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