Basic Biostatistics: .Basic Biostatistics is a concise, introductory text that covers biostatistical principles and focuses on the common types of data encountered in public health and biomedical fields. The text puts equal emphasis on exploratory and confirmatory statistical methods. Sampling, exploratory data analysis, estimation, hypothesis testing, and power and precision are covered through detailed, illustrative examples. The book is organized into three parts: Part I addresses basic concepts and techniques; Part II covers analytic techniques for quantitative response variables; and Part III covers techniques for categorical responses. The Second Edition offers many new exercises as well as an all new chapter on "Poisson Random Variables and the Analysis of Rates." With language, examples, and exercises that are accessible to students with modest mathematical backgrounds, this is the perfect introductory biostatistics text for undergraduates and graduates in various fields of public health. Features: Illustrative, relevant examples and exercises incorporated throughout the book. Answers to odd-numbered exercises provided in the back of the book. (Instructors may requests answers to even-numbered exercises from the publisher. Chapters are intentionally brief and limited in scope to allow for flexibility in the order of coverage. Equal attention is given to manual calculations as well as the use of statistical software such as StaTable, SPSS, and WinPepi. Comprehensive Companion Website with Student and Instructor's Resources. |
Other editions - View all
Basic Biostatistics: Statistics for Public Health Practice B. Burt Gerstman No preview available - 2008 |
Common terms and phrases
ANOVA association best response boxplots Calculate case-control chapter chi-square statistic cholesterol compared confidence interval confounding coronary cross tabulation cumulative probability curve data points data set degrees of freedom disease equal variance esophageal cancer estimate explanatory variable Figure formula frequency hypothesis testing illustrative data illustrative example independent individuals linear margin of error mean difference mean square measure median method multiple Normal approximation Normal distribution null hypothesis number of successes observed odds ratio outliers P-value p₁ pairs parameter patients plot population mean Pr(A Pr(B Pr(X Pr(z prevalence procedure random variable regression model relative risk residual response variable sample mean sample size requirements sampling distribution Select the best Significance level simple random sample skew smoking SPSS standard deviation standard error Standard Normal statistically significant stem values stemplot stored online summary t-test Table Test statistic Total treatment trial tstat two-sided type I error WinPepi zstat