SPSS Statistics For Dummies, 4th Edition
SPSS Statistics For Dummies - 4th edition will be released on September 9, 2020. We're offering a special bonus for anyone who preorders the book -- weekly help calls with Jesus and me! Check out this link for more details on how to access the pre-order bonus.
There are tons of introductory books on SPSS Statistics, but I truly think that this title is unique. The For Dummies format is by its very nature an introduction. To do that well, every page has to be thoughtfully used, with no waste. My coauthor, Jesus Salcedo, and I made every page count. Between us, we have taught thousands and thousands of folks how to use SPSS Statistics. With the help of our technical contributor Aaron Poh, with made sure that every step and every screen shot was up to date for the current version. We made sure that all the basics were represented, that the new version got appropriate attention, and that everything that was covered belonged in an introductory book. No introductory book can cover everything as SPSS Statistics is a vast world of options and settings, but I truly believe that there is no better way to start short of the luxury of private instruction. For further training on any of the topics covered in SPSS Statistics For Dummies, or for consulting support in any area of SPSS, please contact me.
IBM SPSS Modeler Cookbook
The IBM SPSS Modeler Cookbook was the collaboration of PACKT publishing and five authors: Keith McCormick, Dean Abbott, Meta Brown, Tom Khabaza, Scott Mutchler. Colin Shearer, the creator of the original software, was kind enough to write the Forward, and we had a fantastic team of technical reviewers: Fabrice Leroy, Terry Taerum, Jesus Salcedo, Bob Nisbet, Matt Brooks, and David Young Oh. You could spend an afternoon googling the names of this great team of collaborators. Collectively, they represent much of the history of SPSS Modeler since its creation in the early 90s.
It has gotten great reviews and we are committed to keeping this book current over the upcoming years. I wrote a blog post announcing the Cookbook when its release was first announced. I also recommend this post about the free sampler page, which PACKT had me put together.
The reviews have been very favorable on Amazon, and Goodreads. Please reach out to me if you have questions about the supporting materials which can be found here: PACKT’s IBM SPSS Modeler Cookbook page. For further training on any of the topics covered in the Cookbook, or for consulting support with SPSS Modeler, please contact me.
SPSS Statistics for Data Analysis and Visualization
SPSS Statistics for Data Analysis and Visualization is set firmly in the intermediate to advanced level of SPSS Statistics. It also take the reader in a different direction than the handful of books that attempt to cover SPSS Statistics beyond the basics. We focus on techniques that allow you to address common situations with greater sophistication, making occasional use of the SPSS Statistics modules. There is also plenty of content on SPSS Base. As the book release approaches I will post a chapter level overview.
Some examples of chapters that we are working on. Rather than try to get away with using OLS regression with ordinal variables, we show PLUM and Optimal scaling. Something that I am particularly excited about is that with all the talk around chart types and chart editing, we forget that we can bring our statistical sophistication to bear. So, we introduce both Multidimensional Scaling and Correspondence Analysis as ways to preprocess complex relationships to reduce the data into a form that can be more easily displayed.
If you have questions about the complex world of the SPSS Modules, or you want to know if a topic of interest requires a module please contact me.
IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey.
This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler’s easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices.
This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model’s performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models.