Biostatistics (Third Edition)

Author: Li Chunxi
Publisher:
Publish Date: 2008-02-01
Features: This book systematically introduces the basic principles and methods of biostatistics. It briefly describes the origin, development, research objects, and functions of biostatistics, as well as the organization and calculation of experimental data in biological research, descriptive statistics, probability, probability distributions, and sampling distributions. It then focuses on statistical inference for means, chi-square tests, analysis of variance, linear regression and correlation analysis, linearizable nonlinear regression analysis, covariance analysis, multiple regression and multiple correlation analysis, stepwise regression and path analysis, and polynomial regression analysis. Additionally, it provides detailed explanations of sampling principles and methods, the principles of experimental design, and common designs such as comparative designs, randomized complete block designs, balanced incomplete block designs, split-plot designs, Latin square designs, and orthogonal designs, along with their statistical analyses. Building on this foundation, the book briefly introduces multivariate analysis techniques such as cluster analysis, discriminant analysis, principal component analysis, factor analysis, canonical correlation, and time series analysis. This book is suitable as a textbook for undergraduate students in biology and related fields at comprehensive universities and normal universities. It can also serve as a reference for researchers, teachers, and graduate students in life sciences, agricultural sciences, forestry sciences, medicine, veterinary science, aquaculture, and other related fields.
This book systematically introduces the basic principles and methods of biostatistics. It briefly describes the concept, origin, development, and functions of biostatistics, the organization and calculation of experimental data in biological research, descriptive statistics, probability, probability distributions, and sampling distributions. It then focuses on hypothesis testing for means and frequencies, chi-square tests, analysis of variance, linear regression and correlation analysis, linearizable nonlinear regression analysis, covariance analysis, the principles of experimental design, common experimental designs and their statistical analyses, multiple regression and multiple correlation analysis, and polynomial regression analysis. Additionally, it briefly introduces multivariate analysis techniques such as cluster analysis, discriminant analysis, principal component analysis, factor analysis, canonical correlation, and time series analysis.