Warner′s Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in. Applied Statistics: From Bivariate through Multivariate Techniques Warner, Rebecca M. ISBN-13: 723 Table of Contents Preface Acknowledgments. Applied Statistics: From Bivariate Through Multivariate Techniques. From Bivariate Through Multivariate Techniques Rebecca M. Warner No preview available - 2012. Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression.

Product Description Rebecca M. Warner′s Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations.

Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

I am a research specialist for an online graduate school and we are in the process of adopting this book for our students. It really is the most comprehensive, user-friendly text on the market. Actually, this book is extremely helpful for graduate students as well as those of us who have already graduated:-) I think statistics is similar to a foreign language: use it or lose it.

Of course I'll probably always remember 'donde esta el bano?' , just like I remember t-tests, correlations, ANOVA, regression, etc. However, the water can get murky when it comes to power analyses in certain situations, moderation/mediation, differences between discriminant analysis and binary logistic regression, and other complicated or less used tests. Warner's text is an excellent, user-friendly resource in such situations as well as when my doctoral students have random questions about their analyses. She provides relatable examples, professional write-ups, and helpful SPSS screenshots.

I would highly recommend this text! Blah blah blah. The links to the samples did not work with my mac. Author took 10 pages to make simple points. The redeeming part was that she did not use inappropriate examples when describing stats models.

Between this and the instructor's presentations, this book was almost useless except for writing essays indicating it had been read. Associated open book quizzes were nearly impossible. Instructor was incredibly scattered and hard to follow. Eeked out a B in this doctoral level course when I'd had all A's since my jr year as an undergrad. Why do authors make it so unreasonably complicated?

Because professors are in cahootz with them to buy require their books regardless. This was my textbook for a series of 4 intermediate to advanced statistics courses in my Ph.D program. Boot Camp Drivers Windows 7 32 Bit. For univariate and bivariate methods, the book is outstanding with each chapter well written and easy to follow. Each chapter first walks you through the theory of the method, then you get a logical tour of the assumptions, maths and relevant equations, followed by exemplars, and finally an SPSS analysis.

The coverage is deep with a focus on application over theoretical development, which is perfect for those who will depend on these statistical tools in their dissertations and research. Where the text starts to fall short, in my opinion, is in its coverage of multivariate methods. Here the chapters are more cryptic and difficult to follow and the logic sometimes counterintuitive and hard to follow. The multivariate chapters appeared to struggle to identify the key elements of each analysis and left me often confused. Worked examples were also harder to follow. What I and many of my fellow students in the multivariate courses did was supplement this text with Tabachnick & Fidell, which is exclusively devoted to multivariate statistics and the text of choice for those methods. It does a much better job of explaining multivariate methods than Warner.