Kategoria BISAC: Mathematics >> Probability & Statistics - Multivariate Analysis

On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a...

The text itself:
In this second edition, master expositor Sheldon Ross has produced a unique work in introductory statistics. The text's main merits are the clarity of presentation, examples and applications from diverse areas, and most importantly, an explanation of intuition and ideas behind the statistical methods. To quote from the preface, "it is only when a student develops a feel or intuition for statistics that she or he is...

Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formulas behind attractive user interfaces. This book bridges that gap, demonstrating theory put into practice.

Concepts presented in a theoretical linear models course are often trivialized in applied linear models courses by the facility of high-level SAS procedures like PROC MIXED and PROC REG...

A comprehensive overview of multivariate data and analysis

Providing an introduction to the subject of multivariate data analysis without delving deeply into underlying theory and concepts, An Introduction to Multivariate Data is an excellent resource for undergraduate statistics courses as well as for professionals who require an understanding of statistical techniques for analyzing their own data sets. Focused on real-world application, the book includes sample exercises in each chapter to firmly cement the concepts covered. Exercises range from simple problems that...

Find the right algorithm for your image processing application

Exploring the recent achievements that have occurred since the mid-1990s, Circular and Linear Regression: Fitting Circles and Lines by Least Squares explains how to use modern algorithms to fit geometric contours (circles and circular arcs) to observed data in image processing and computer vision. The author covers all facets geometric, statistical, and computational of the methods. He looks at how the numerical algorithms relate to one another through underlying ideas, compares the strengths...

Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and...

This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.
Real life examples are used...

Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and...