Biostatistics: A Methodology for the Health SciencesThis versatile textbook allows students and teachers to fashion an instructional package that meets diverse learning needs. It provides a wide-ranging look at basic and advanced biostatistical concepts and methods in a format calibrated to individual interests and levels of proficiency. Each topic presentation features introductory comments, real-life examples, a step-by-step outline of the statistical procedure under discussion, an explanation of applications, and numerous practice exercises. Advanced material-which may be included in coursework at the discretion of the instructor-has been noted throughout the text with asterisks, and notes at the end of each chapter extend and enrich the primary material. Early chapters discuss the design of medical studies, descriptive statistics, and introductory ideas of probability theory and statistical inference. Later chapters explore more advanced statistical methods and illustrate important current uses of biostatistics. Statistical methods discussed include * Robustness and nonparametric statistics * Analysis of variance and covariance * Multiple comparisons * Discrimination and classification * Principal component analysis and factor analysis * Survival analysis (including life tables, product-limit estimates, and Cox proportional hazards regression) * Sample sizes for observational studies With more than 390 practice exercises, clear illustrations and graphics, and more than 130 examples, Biostatistics provides a complete detailed seminar, which encourages steady, incremental growth while acting as a catalyst for creative analysis. |
Contents
Introduction to Biostatistics | 1 |
Biostatistical Design of Medical Studies | 17 |
Descriptive Statistics | 35 |
Copyright | |
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Biostatistics: A Methodology for the Health Sciences Gerald van Belle,Lloyd D. Fisher No preview available - 1996 |
Common terms and phrases
adjusted analysis of variance ANOVA approximately associated assumptions average B₁ binomial blood pressure calculated cell Chapter column confidence interval consider contingency table correlation coefficient covariate critical value Definition degrees of freedom DF SS difference discussed disease duration equal estimate example F statistic F-distribution F-Ratio factor Figure frequency given independent interaction linear regression log-linear model logistic mean square measure method multiple correlation multiple regression multivariate n₁ nonparametric normal distribution Note null hypothesis number of observations odds ratio p-value parameters patients plot Poisson population possible predictive predictor variables principal component probability problem procedure quantity random variable ranks regression coefficients reject the null relationship residual Section significance level SSREG standard deviation standard error statistically independent statistically significant subset sum of squares Suppose t-test Total transformation treatment VO2 MAX weight X₁ Y₁ zero σ²