No, please don't. ,br>Three little words, innocuous in their definitions. No great allegories, no ethical dilemmas. No, please don't. ,br>The words echoed through his brain as he sawed, their rhythm spurring his own. When a local girl falls prey to a sadistic serial killer, Nashville homicide lieutenant Taylor Jackson finds herself pursuing the vicious murderer. The Southern Strangler is slaughtering his way through the Southeast, leaving a gruesome memento at each crime scene - the prior victim's severed hand. Ambitious TV reporter Whitney Connolly is certain the Southern Strangler is her ticket out of Nashville; she's got a scoop that could break the case, but has no idea how close to this story she really is. As the killer spirals out of control, everyone involved must face a horrible truth: that the purest evil is born of secrets and lies. "A taut, striking debut. Mystery fiction has a new name to watch. " John Connolly

I want very much to set down my thoughts and my experiences of life. I want to do so now that I have come to middle age and now that my attitudes are all defined and my personal drama worked out I feel that the toil of writing and reconsideration may help to clear and fix many things that remain a little uncertain in my thoughts because they have never been fully stated, and I want to discover any lurking inconsistencies and unsuspected gaps. And I have a story. I have lived through things that have searched me. I want to tell that story as well as I can while I am still a clear-headed and active man, and while many details that may presently become blurred and altered are still rawly fresh in my mind. And to one person in particular do I wish to think I am writing, and that is to you, my only son. I want to write my story not indeed to the child you are now, but to the man you are going to be. You are half[Pg 2] my blood and temperamentally altogether mine. A day will come when you will realize this, and want to know how life has gone with me, and then it may be altogether too late for me to answer your enquiries. I may have become inaccessible as old people are sometimes inaccessible. And so I think of leaving this book for you—at any rate, I shall write it as if I meant to leave it for you. Afterwards I can consider whether I will indeed leave it....

This best-seller introduces readers to structural equation modeling (SEM) so they can conduct their own analysis and critique related research. Noted for its accessible, applied approach, chapters cover basic concepts and practices and computer input/output from the free student version of Lisrel 8.8 in the examples. Each chapter features an outline, key concepts, a summary, numerous examples from a variety of disciplines, tables, and figures, including path diagrams, to assist with conceptual understanding. The book first reviews the basics of SEM, data entry/editing, and correlation. Next the authors highlight the basic steps of SEM: model specification, identification, estimation, testing, and modification, followed by issues related to model fit and power and sample size. Chapters 6 through 10 follow the steps of modeling using regression, path, confirmatory factor, and structural equation models. Next readers find a chapter on reporting SEM research including a checklist to guide decision-making, followed by one on model validation. Chapters 13 through 16 provide examples of various SEM model applications. The book concludes with the matrix approach to SEM using examples from previous chapters. Highlights of the new edition include: A website with raw data sets for the book's examples and exercises so they can be used with any SEM program, all of the book's exercises, hotlinks to related websites, and answers to all of the exercises for Instructor’s only New troubleshooting tips on how to address the most frequently encountered problems Examples now reference the free student version of Lisrel 8.8 Expanded coverage of advanced models with more on multiple-group, multi-level, & mixture modeling (Chs. 13 & 15), second-order and dynamic factor models (Ch. 14), and Monte Carlo methods (Ch. 16) Increased coverage of sample size and power (Ch. 5) and reporting research (Ch. 11) New journal article references help readers better understand published research (Chs. 13 – 17) and 25 % new exercises with answers to half in the book for student review. Designed for introductory graduate level courses in structural equation modeling or factor analysis taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. An understanding of correlation is assumed. To access the website visit the book page or the Textbook Resource page at http://www.psypress.com/textbook-resources/ for more details.

Product Description: A timely collection of advanced, original material in the area of statistical methodology motivated by geometric problems, dedicated to the influential work of Kanti V. Mardia This volume celebrates Kanti V. Mardia's long and influential career in statistics...read more

A traditional approach to developing multivariate statistical theory is algebraic. Sets of observations are represented by matrices, linear combinations are formed from these matrices by multiplying them by coefficient matrices, and useful statistics are found by imposing various criteria of optimization on these combinations. Matrix algebra is the vehicle for these calculations. A second approach is computational. Since many users find that they do not need to know the mathematical basis of the techniques as long as they have a way to transform data into results, the computation can be done by a package of computer programs that somebody else has written. An approach from this perspective emphasizes how the computer packages are used, and is usually coupled with rules that allow one to extract the most important numbers from the output and interpret them. Useful as both approaches are--particularly when combined--they can overlook an important aspect of multivariate analysis. To apply it correctly, one needs a way to conceptualize the multivariate relationships that exist among variables. This book is designed to help the reader develop a way of thinking about multivariate statistics, as well as to understand in a broader and more intuitive sense what the procedures do and how their results are interpreted. Presenting important procedures of multivariate statistical theory geometrically, the author hopes that this emphasis on the geometry will give the reader a coherent picture into which all the multivariate techniques fit.

Product Description: Create your own book!Black-paged blank book offers plenty of space for dramatic drawings, doodles, design projects, or for pasting in photos or scrapbooking.For kids and adults alike.Pages are black throughout.Premium paper works well with gel pens, white pens, chalk, pastels, and other opaque media...read more

Product Description: Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA)...read more

Product Description: Increasingly, researchers need to perform multivariate statistical analyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of these advanced techniques, in part, because books focus on the theory and neglect to explain how to perform and interpret multivariate analyses on real-life data...read more

Product Description: This book collects together 45 papers covering various aspects of the analysis of multiway data arrays. Mathematical properties of three-way and multiway arrays are investigated and their utilization for the statistical interpretation of complex data sets is emphasized...read more

Product Description: High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens...read more