Jolicoeur P. Introduction to Biometry

N.-Y.: Springer, 1999. - 517p.Statistical methods are becoming more important in all biological fields of study. Biometry deals with the application of mathematical techniques to the quantitative study of varying characteristics of organisms, populations, species, etc. This book uses examples based on genuine data carefully chosen by the author for their special biological significance. The chapters cover a broad spectrum of topics and bridge the gap between introductory biological statistics and advanced approaches such as multivariate techniques and nonlinear models. A set of statistical tables most frequently used in biometry completes the book.Looking at quantitative biological data through scatter diagrams
Samples and populations, estimates and parameters
Frequencies and probabilities
Measures of central tendency and of dispersion
The normal distribution
The distribution of Student’s t
The distribution of χ (chi squared)
Hypothesis testing and confidence intervals concerning one or two means
Hypothesis testing and confidence intervals concerning one variance
Hypothesis testing and confidence intervals concerning a variance ratio
The analysis of variance or ANOVA (oneway, type I)
The skewness and peakedness indices, g and g
The lognormal distribution
Testing hypotheses concerning frequency tables using the χ distribution
Tests of goodness of fit
The binomial distribution
The Poisson distribution
The bivariate normal distribution and the correlation coefficient, r
Estimation lines (the socalled regression lines)
The analysis of covariance or ANCOVA: comparing estimation lines
The orthogonal estimation line or major axis
The trivariate normal distribution: partial and multiple correlations and regressions
Elementary linear calculations (vectors and matrices)
Partial and multiple correlations and regressions: matrix calculations
Oneway type I analysis of variance with contrasts
Oneway type II analysis of variance with variance components
Twoway type I analysis of variance with interaction
The multivariate normal distribution
The distribution of Hotelling’s T
Principal components or principal axes
Fisher’s linear discriminant function
Multiple discriminant analysis
Canonical correlations
Growth curves and other nonlinear relationships