Biostatistical Analysis Zar's Biostatistical Analysis, Fifth Edition, is the ideal book for readers seeking practical coverage of statistical analysis methods used by researchers to collect, summarize, analyze and draw conclusions from biological research. The latest edition of this best-selling textbook is both comprehensive and easy to read. It is suitable as an introduction for beginners and as a comprehensive reference book for biological researchers and other advanced users. Introduction; Populations and Samples; Measures of Central Tendency; Measures of Dispersion and Variability; Probabilities; The Normal Distribution; One-Sample Hypotheses; Two-Sample Hypotheses; Paired-Sample Hypotheses; Multisample Hypotheses: The Analysis of Variance; Multiple Comparisons; Two-Factor Analysis of Variance; Data Transformations; Multiway Factorial Analysis of Variance; Nested (Hierarchical) Analysis of Variance; Multivariate Analysis of Variance; Simple Linear Regression; Comparing Simple Linear Regression Equations; Simple Linear Correlation; Multiple Regression and Correlation; Polynomial Regression; Testing for Goodness of Fit; Contingency Tables; More on Dichotomous Variables; Testing for Randomness; Circular Distributions: Descriptive Statistics; Circular Distributions: Hypothesis Testing For all readers interested in biostatistics. |
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analysis of variance ANOVA Appendix Table binomial calculated cells Chapter chi-square coefficient of variation conclude confidence interval confidence limits contingency table correlation coefficient critical value data of Example datum degrees of freedom demonstrated in Example detectable difference determine drug effect employed equal Equation estimate experimental design females Figure frequency graph groups hypothesis testing interaction levels of factor males Mann-Whitney test mean square measurements median MULTIPLE COMPARISONS n₁ nonparametric normal distribution null hypothesis number of data observed obtained one-tailed test parameters performed population mean population variance prediction probability procedure proportion R. A. Fisher random ranks rejected replication residual sample sizes sampled population shown in Example species specified standard deviation standard error sum of squares test statistic TESTING FOR DIFFERENCE total number total SS transformation two-factor two-sample t test two-tailed test Type I error weight within-cells X₁ zero ΣΧ