StatisticsNewS Fit linear models without intercepts in SAS or R Routines for linear models in SAS and R incorporate an intercept by default. But, what's the syntax for fitting a model without an intercept? This entry from SAS and R: Data Management, Statistical Analysis, and Graphics (July 2009) will tell you. The book is designed as a bilingual SAS/R dictionary: If you know SAS, you can look up the noint option in the SAS index. If you know R, you can look up the 0 operator, or if you're unaware of how to do it in either, you can look up no intercept in the subject index.
SAS proc glm data=ds; model y = x1 ... xk / noint; run; Note: The noint option works with many model statements.
R mod1 < –lm(y ~ 0 + x1 + ... + xk, data=ds) or mod1 < –lm(y ~ x1 + ... + xk –1, data=ds)
Nexus
Making the Most Out of those Complex Datasets in the Social and Behavioral Sciences Large and complex datasets are becoming increasingly prevalent in the social and behavioral sciences. The Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences Series makes the lives of statisticians working in the applied sciences easier by exploring the effective and efficient use of statistical, econometric, and psychometric methods. Two new books in the series explore the latest methods and tools in this rapidly growing area. A. Colin Cameron joins the distinguished panel of series editors that include Andrew Gelman, J. Scott Long, Sophia Rabe-Hesketh, and Anders Skrondal. Dr. Cameron is a professor in the Department of Economics at the University of California, Davis and has served as the Director of the U.C. Davis Center on Quantitative Social Science Research.
Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations (2009), by Stanley A. Mulaik, integrates a general philosophical theory of causation with structural equation modeling that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. In Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences (2009) Brian S. Everitt goes beyond the usual coverage of hypothesis testing to provide a full explanation of the most useful methods for comparing and examining data in psychology, the social sciences, and law. He includes real data sets that provide a sampling of the issues commonly encountered when applying statistics in practice. In partnership with Stata Press, Chapman Hall/CRC offers Microeconometrics Using Stata (2009), co-authored by A. Colin Cameron and Pravin K. Trivedi, provides an outstanding introduction to microeconometrics and microeconometric research using Stata. Cameron and Trivedi cover specialized topics often left out of microeconometrics texts and basic introductions to Stata. They address each topic with an in-depth Stata example and demonstrate how to use Stata’s programming features to implement methods for which Stata does not have a specific command. Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Rounding out the collection, Introduction to Spatial Econometrics (January 2009), presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. James LeSage and R. Kelley Pace explore recent advances in spatial econometric models—including some previously unpublished results.
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Joint Statistical Meetings 2009
JSM 2009 – A Behind the Scenes Look It was several weeks before JSM 2007 when I got the email – “Was I interested in being the JSM 2009 Program Chair?” I immediately replied with a resounding YES! My name and others were then submitted as potential chairs to the Committee on Meetings. The committee chose me as the 2009 Program Chair, and my election was subsequently approved by Sally Morton, our current ASA President, and the ASA Executive Board. One of my first tasks in late 2007 was to appoint two General Wendy L. Martinez JSM 2009 Methods Chairs and the Poster Chair. These folks are very Program Chair important members of the program committee because they help me in many ways, such as with organizing the program, filling in for other committee members, and more. The remaining members of the program committee are either appointed by a member society (e.g., IMS, SSC, etc.) or elected by their section. Around this same time, ASA President Morton picked the theme for our meeting, and she chose an exciting one – Statistics: From Evidence to Policy. While sessions do not have to address it, the theme is very important, as it tends to drive the content of the invited and topic-contributed sessions. To gain experience in the process, the JSM 2009 Program Committee had to arrange some events for the previous year’s program. For example, the committee was responsible for organizing and scheduling the roundtable coffees and lunches for the 2008 meetings. We also started working on the invited program during this same time period – one year before our JSM! Then, the hard (but exciting and fun!) work begins. The partner societies, the ASA sections, and other entities have a certain number of invited sessions allocated to them. The appropriate committee members can organize the sessions or solicit ideas from their members. The representatives for the ASA sections, committees, and interest groups are allowed to submit two invited sessions for the competition slots, and the entire program committee votes on the ones they think should be included in the program. This was certainly a challenging and important part of the process because the invited sessions are the backbone of the meetings. JSM 2009 - Washington, DC From Evidence to Policy
Summer 2009
story continued on pg. 7 (JSM 2009)
Visit Chapman & Hall/CRC at Booth 304 Every year at JSM Chapman & Hall/ CRC offers attendees the chance to save on the best statistics books available. This year is no exception. You can build your library without breaking your bank. Save 50% on these classic books: Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Julian J. Faraway Handbook of Statistical Distributions with Applications, K. Krishnamoorthy Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood, Youngjo Lee, John A. Nedler, and Yudi Pawitan R Graphics, Paul Murrell Handbook of Parametric and Nonparametric Statistical Procedures, David J. Sheskin Linear Mixed Models: A Practical Guide Using Statistical Software, Brady West, Kathleen B. Welch, and Andrzej T. Galecki Generalized Additive Models: An Introduction with R, Simon Wood
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