Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years, the number and variety of distributions to choose from-all with the…
"In A Primer on Statistical Distributions, you'll find a valuable review of often-overlooked distributions, including geometric, negative binomial, hypergeometric, Pareto, beta, gamma, chi-square, logistic, Cauchy, Laplace, extreme value, multinomial, Dirichlet, and multivariate…
This accessible introductory textbook provides a straightforward, practical explanation of how statistical analysis and error measurements should be applied in biological research.
This accessible introductory textbook provides a straightforward, practical explanation of how statistical analysis and error measurements should be applied in biological research.
This accessible introductory textbook provides a straightforward, practical explanation of how statistical analysis and error measurements should be applied in biological research. Understanding Statistical Error - A Primer for Biologists: Introduces the essential topic of error…
What do you do when you realize that the data set from the study that you have just completed violates the sample size or other requirements needed to apply parametric statistics? Nonparametric Statistics for Health Care Research was developed for such scenarios--research undert…
A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression, this volume bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares …
A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression, this volume bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares …
Construction of various models of objects under uncertainty is one of the most important problems in modern decision making theory. Regression models are some of the most prevalent tools for modeling under uncertainty and are widely applied in different branches of science such …
This essential desk reference will meet the demand for a broad and convenient collection of normative data in child neuropsychology. In a clearly written, well-organized manner, it compiles published and previously unpublished normative data for the neuropsychological tests that…
