JMP® is statistical discovery software. JMP helps you explore data, fit models, discover patterns, and discover points that don’t fit patterns. This book is a guide to statistics using JMP.

Software’s job is to create a virtual workplace. The software has facilities and platforms where the tools are located and the work is performed. JMP provides the workplace that we think is best for the job of analyzing data. With the right software workplace, researchers embrace computers and statistics, rather than avoid them.

JMP aims to present a graph with every statistic. You should always see the analysis in both ways, with statistical text and graphics, without having to ask for it. The text and graphs stay together.

JMP is controlled largely through point-and-click mouse manipulation. If you hover the mouse over a point, JMP identifies it. If you click on a point in a plot, JMP highlights the point in the plot, and highlights the point in the data table. In fact, JMP highlights the point everywhere it is represented.

JMP has a progressive organization. You begin with a simple report at the top, and as you analyze, more and more depth is revealed. The analysis is alive, and as you dig deeper into the data, more and more options are offered according to the context of the analysis.

In JMP, completeness is not measured by the “feature count,” but by the range of possible applications, and the orthogonality of the tools. In JMP, you get a feeling of being in more control despite less awareness of the control surface. You also get a feeling that statistics is an orderly discipline that makes sense, rather than an unorganized collection of methods.

A statistical software package is often the point of entry into the practice of statistics. JMP strives to offer fulfillment rather than frustration, empowerment rather than intimidation.

If you give someone a large truck, they will find someone to drive it for them. But if you give them a sports car, they will learn to drive it themselves. Believe that statistics can be interesting and reachable so that people will want to drive that vehicle.

JMP Start Statistics has been updated and revised to feature JMP 10. Major enhancements have been made to the product, including new platforms for design (Split Plots, Computer Designs), analysis (Generalized Linear Models, Time Series, Gaussian Processes), and graphics (Tree Maps, Bubble Plots, and interactive Graph Builder) as well as more report options (such as the Tabulate platform, Data Filter, Phase and T2 control charts) unavailable in previous versions. The chapter on Design of Experiments (DOE) has been completely rewritten to reflect the popularity and utility of optimal designs. In addition, JMP has a new interface to SAS that makes using the products together much easier.

JMP 10 also focuses on enhancing the user experience with the product. Tutorials, a series of daily tips, and an extensive use of tool tips on menus and reports make using JMP easier than ever.

Building on the comments from teachers on the fourth edition, chapters have been rearranged to streamline their pedagogy, and new sections and chapters have been added where needed.

This book is a mix of software manual and statistics text. It is designed to be a complete and orderly introduction to analyzing data. It is a teaching text, but is especially useful when used in conjunction with a standard statistical textbook.

A few of the techniques in this book are not found in most introductory statistics courses, but are accessible in basic form using JMP. These techniques include logistic regression, correspondence analysis, principal components with biplots, leverage plots, and density estimation. All these techniques are used in the service of understanding other, more basic methods. Where appropriate, supplemental material is labeled as “Special Topics” so that it is recognized as optional material that is not on the main track.

JMP also includes several advanced methods not covered in this book, such as nonlinear regression, multivariate analysis of variance, and some advanced design of experiments capabilities. If you are planning to use these features extensively, it is recommended that you refer to the help system or the documentation for the professional version of JMP.

Most examples are real-world applications. A few simulations are included too, so that the difference between a true value and its estimate can be discussed, along with the variability in the estimates. Some examples are unusual, calculated to surprise you in the service of emphasizing an important concept. The data for the examples are installed with JMP, with step-by-step instructions in the text. The same data are also available on the internet at www.jmp.com. JMP can also import data from files distributed with other textbooks. See Chapter 3, "Data Tables, Reports, and Scripts" for details on importing various kinds of data.

Thank you to the JMP testers as well as the contributors and reviewers of JMP Start Statistics: Sheila Loring, Bradley Jones, Chris Gotwalt, Lou Valente, and Tom Donnelly. Thanks to Michael Benson, Avignor Cahaner, Howard Yetter, David Ikle, Robert Stine, Andy Mauromoustkos, Al Best, Jacques Goupy, Chris Olsen for contributions to earlier versions of the book. Special thanks to Curt Hinrichs for invaluable support to the JMP Start Statistics project. Further acknowledgements are in the JMP documentation, found in JMP Help.