Fundamentals of Biostatistics / Edition 7

Fundamentals of Biostatistics / Edition 7

5.0 1
by Bernard Rosner
     
 

ISBN-10: 0538733497

ISBN-13: 9780538733496

Pub. Date: 08/20/2010

Publisher: Cengage Learning

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…  See more details below

Overview

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.

Product Details

ISBN-13:
9780538733496
Publisher:
Cengage Learning
Publication date:
08/20/2010
Edition description:
Older Edition
Pages:
888
Sales rank:
441,501
Product dimensions:
8.20(w) x 10.10(h) x 1.50(d)

Table of Contents

1. GENERAL OVERVIEW. References. 2. DESCRIPTIVE STATISTICS. Introduction. Measures of Location. Some Properties of the Arithmetic Mean. Measures of Spread. Some Properties of the Variance and Standard Deviation. The Coefficient of Variation. Grouped Data. Graphic Methods. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. Case Study: Effects of Tobacco Use of Bone Mineral Density in Middle-Aged Women. Summary. Problems. References. 3. PROBABILITY. Introduction. Definition of Probability. Some Useful Probabilistic Notation. The Multiplication Law of Probability. The Addition Law of Probability. Conditional Probability. Bayes' Rule and Screening Tests. Prevalence and Incidence. Summary. Problems. References. 4. DISCRETE PROBABILITY DISTRIBUTIONS. Introduction. Random Variables. The Probability Mass Function for a Discrete Random Variable. The Expected Value of a Discrete Random Variable. The Variance of a Discrete Random Variable. The Cumulative-Distribution Function of a Discrete Random Variable. Permutations and Combinations. The Binomial Distribution. Expected Value and Variance of the Binomial Distribution. The Poisson Distribution. Computation of Poisson Probabilities. Expected Value and Variance of the Poisson Distribution. Poisson Approximation to the Binomial Distribution. Summary. Problems. References. 5. CONTINUOUS PROBABILITY DISTRIBUTIONS. Introduction. General Concepts. The Normal Distribution. Properties of the Standard Normal Distribution. Conversion from an N([mean], [variance]) Distribution to an N(0, 1) Distribution. Linear Combinations of Random Variables. Normal Approximation to the Binomial Distribution. NormalApproximation to the Poisson Distribution. Summary. Problems. References. 6. ESTIMATION. Introduction. The Relationship Between Population and Sample. Random-Number Tables. Randomized Clinical Trials. Estimation of the Mean of a Distribution. Case Study: Relationship of Cigarette Smoking to Bone Mineral Density (BMD) Among Middle-Aged Women. Estimation of the Variance of a Distribution. Estimation for the Binomial Distribution. Estimation for the Poisson Distribution. One-Sided Confidence Intervals. Summary. Problems. References. 7. HYPOTHESIS TESTING: ONE-SAMPLE INFERENCE. Introduction. General Concepts. One-Sample Test for the Mean of a Normal Distribution: One-Sided Alternatives. One-Sample Test for the Mean of a Normal Distribution: Two-Sided Alternatives. The Power of a Test. Sample-Size Determination. The Relationship Between Hypothesis Testing and Confidence Intervals. One-Sample Chi-Squared Test for the Variance of a Normal Distribution. One-Sample Test for a Binomial Proportion. One-Sample Inference for the Poisson Distribution. Case Study: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women. Summary. Problems. References. 8. HYPOTHESIS TESTING: TWO-SAMPLE INFERENCE. Introduction. The Paired t Test. Interval Estimation for the Comparison of Means from Two Paired Samples. Two-Sample t Test for Independent Samples with Equal Variances. Interval Estimation for the Comparison of Means from Two Independent Samples (Equal Variance Case). Testing for the Equality of Two Variances. Two-Sample t Test for Independent Samples with Unequal Variances. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. The Treatment of Outliers. Estimation of Sample Size and Power for Comparing Two Means. Sample-Size Estimation for Longitudinal Studies. Summary. Problems. References. 9. NONPARAMETRIC METHODS. Introduction. The Sign Test. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. Summary. Problems. References. 10. HYPOTHESIS TESTING: CATEGORICAL DATA. Introduction. Two-Sample Test for Binomial Proportions. Fisher's Exact Test. Two-Sample Test for Binomial Proportions for Matched-Pair Data (McNemar's Test). Estimation of Sample Size and Power for Comparing Two Binomial Proportions. R x C Contingency Tables. Chi-Square Goodness-of-Fit Test. The Kappa Statistic. Summary. Problems. References. 11. REGRESSION AND CORRELATION METHODS. Introduction. General Concepts. Fitting Regression Lines-The Method of Least Squares. Inferences About Parameters from Regression Lines. Interval Estimation for Linear Regression. Assessing the Goodness-of-Fit of Regression Lines. The Correlation Coefficient. Statistical Inference for Correlation Coefficients. Multiple Regression. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. Partial and Multiple Correlation. Rank Correlation. Summary. Problems. References. 12. MULTISAMPLE INFERENCE. Introduction to the One-Way Analysis of Variance. One-Way Analysis of Variance-Fixed-Effects Model. Hypothesis Testing in One-Way ANOVA-Fixed-Effects Model. Comparisons of Specific Groups in One-Way ANOVA. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. Two-Way Analysis of Variance. The Kruskal-Wallis Test. One-Way ANOVA-The Random-Effects Model. The Interclass Correlation Coefficient. Summary. Problems. References. 13. DESIGN AND ANALYSIS TECHNIQUES FOR EPIDEMIOLOGIC STUDIES. Introduction. Study Design. Measures of Effect for Categorical Data. Confounding and Standardization. Methods of Inference for Stratified Categoric Data-The Mantel-Haenszel Test. Power and Sample-Size Estimations for Stratified Categorical Data. Multiple Logistic Regression. Meta-Analysis. Equivalence Studies. The Cross-Over Design. Clustered Binary Data. Measurement-Error Methods. Summary. Problems. References. 14. HYPOTHESIS TESTING: PERSON-TIME DATA. Measure of Effect for Person-Time Data. One-Sample Inference for Incidence-Rate Data. Two-Sample Inference for Incidence-Rate Data. Power and Sample Size Estimation for Person-Time Data. Inference for Stratified Person-Time Data. Power and Sample-Size Estimation for Stratified Person-Time Data. Testing for Trend: Incidence-Rate Data. Introduction to Survival Analysis. Estimation of Survival Curves: The Kaplan-Meier Estimator. The Log-Rank Test. The Proportional-Hazards Model. Power and Sample Size Estimation under the Proportional Hazards Model. Summary. Problems. References. APPENDIX: TABLES. ANSWERS TO SELECTED PROBLEMS. FLOWCHART: METHODS OF STATISTICAL INFERENCE. INDEX OF DATA SETS. INDEX.

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