Monday, June 2, 2008

1. Applying Contemporary Statistical Techniques

Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible.

* Assumes no previous training in statistics
* Explains how and why modern statistical methods provide more accurate results than conventional methods
* Covers the latest developments on multiple comparisons
* Includes recent advances in risk-based methods
* Features many illustrations and examples using data from real studies
* Describes and illustrates easy-to-use s-plus functions for applying cutting-edge techniques
* Covers many contemporary ANOVA (analysis of variance) and regression methods not found in other books

2. The Bayesian Choice: From Decision-theoretic Foundations to Computational Implementation

This graduate-level textbook presents an introduction to Bayesian statistics and decision theory. Its scope covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, hierarchical and empirical Bayes modeling, Monte Carlo integration, and Gibbs sampling.

3. Intermediate Statistics For Dummies

Packed with examples, steps for problem-solving, and computer output
The fun and easy way to analyze data, build models, and get ahead in your class

Take your statistics skills to the next level! This easy-to-follow guide walks you through second-semester topics, explaining everything from t-tests and regression to ANOVA, sign tests, and fitting curves to your data. You'll see how to identify and work through problems, avoid common errors, understand the jargon — and even improve your grade with savvy strategies.

*Discover how to
*Review and extend intro stat methods
*Make predictions with multiple regression
*Compare many means by using ANOVA
*Use Chi-square to test for independence
*Understand and use nonparametric statistics

4. Statistical Methods for Categorical Data Analysis

Statistical Methods for Categorical Data Analysis is designed as an accessible reference work and textbook about categorical data (that is, data arising from counts instead of measurement. Examples include data about birth, death, marriage, and so forth). It integrates statistical and econometric approaches to the analysis of limited and categorical dependent variables, thereby offering a practical, mathematically uncomplicated approach to the topics of modern data analysis. The volume offers a comprehensive presentation of many different models in a one-volume format (with website).

Two features distinguish this book from other analyses of categorical data. First, the authors present both the transformational and latent variable approaches and so synthesize similar methods in statistical and econometric literatures. Second, the book has an applied orientation and features actual examples from social science research. The authors keep discussions of theory to a minimum.

Key Features
* Exercises and examples utilize popular data already familiar to many social scientists
* Examples of the use of various popular software packages
* Includes non-standard applications of existing software for estimating models which cannot be handled directly using existing pre-programmed software

5. Handbook of Parametric and Nonparametric Statistical Procedures, Third Edition

6. Business Statistics Demystified

7. Algebraic Statistics for Computational Biology

8. Measure Theory and Probability Theory (Springer Texts in Statistics)

9. Common Errors in Statistics (and How to Avoid Them)

10. Handbook of Applied Economic Statistics

Posted by Raldoz at 4:42 AM  


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