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xTable of ContentsThe Case of the Hidden Leverage Point 366Mining Data with Stepwise RegressionExercises 37314 Fitting Linear ModelsOverview 377The General Linear Model369377379Types of Effects in Linear Models 380Coding Scheme to Fit a One-Way ANOVA as a Linear Model 381Regressor Construction 384Interpretation of Parameters 385Predictions Are the Means 385Parameters and Means 385Analysis of Covariance: Continuous and Categorical Terms in the Same Model 386The Prediction Equation 389The Whole-Model Test and Leverage Plot 390Effect Tests and Leverage Plots 391Least Squares Means 393Lack of Fit 394Separate Slopes: When the Covariate Interacts with a Categorical Effect 396Two-Way Analysis of Variance and Interactions 400Optional Topic: Random Effects and Nested Effects 406Nesting 407Repeated Measures 409Method 1: Random Effects-Mixed Model 409Method 2: Reduction to the Experimental Unit 414Method 3: Correlated Measurements-Multivariate Model 416Varieties of Analysis 418Closing Thoughts 418Exercises41915 Design of Experiments421Overview 421Introduction 424Key Concepts 424JMP DOE 425A Simple Design426The Experiment 426Enter the Response and Factors 427Define the Model 429Is the Design Balanced? 432Perform Experiment and Enter Data 432

Table of ContentsAnalyze the Model 433Flour Paste Conclusions 439Details of the Design: Confounding StructureUsing the Custom Designer439440How the Custom Designer Works 440Choices in the Custom Designer 441An Interaction Model: The Reactor Data 442Analyzing the Reactor Data 444Where Do We Go from Here? 450Some Routine Screening Examples452Main Effects Only (a Review) 452All Two-Factor Interactions Involving a Single Factor 453Alias Optimal Designs 455Response Surface Designs456The Odor Experiment 456Response Surface Designs in JMP 456Analyzing the Odor Response Surface Design 458Plotting Surface Effects 461Specifying Response Surface Effects Manually 462The Custom Designer versus the Response Surface Design Platform 463Split-Plot Designs464The Box Corrosion Split-Plot ExperimentDesigning the Experiment 465Analysis of Split-Plot Designs 467Design Strategies 471DOE Glossary of Key TermsExercises 47646547216 Bivariate and Multivariate Relationships479Overview 479Bivariate Distributions 481Density Estimation 481Bivariate Density Estimation 482Mixtures, Modes, and Clusters 484The Elliptical Contours of the Normal DistributionCorrelations and the Bivariate Normal485487Simulating Bivariate Correlations 487Correlations across Many Variables 490Bivariate Outliers 491Outliers in Three and More Dimensions 494Identify Variation with Principal Components AnalysisPrincipal Components for Six Variables499496xi

xiiTable of ContentsHow Many Principal Components?Discriminant Analysis501502Canonical Plot 503Discriminant Scores 504Stepwise Discriminant Variable Selection 507Cluster Analysis508Hierarchical Clustering: How Does It Work?A Real-World Example 511Some Final ThoughtsExercises 51550851417 Exploratory Modeling517Overview 517Recursive Partitioning (Decision Trees)519Growing Trees 521Exploratory Modeling with Partition 528Saving Columns and Formulas 530Neural Nets531A Simple Example 532Modeling with Neural Networks 535Saving Columns 535Profiles in Neural 537Exercises54118 Control Charts and CapabilityOverview 545What Does a Control Chart Look LikeTypes of Control Charts 549545548Variables Charts 550Attributes Charts 551Specialty Charts 551Control Chart Basics 551Control Charts for Variables Data 552Variables Charts Using Control Chart BuilderThe Control Chart Builder Work SpaceControl Chart Builder Examples 554Control Charts for Attributes DataSpecialty Charts 560553553557Presummarize Charts 560Levey-Jennings Charts 561Uniformly Weighted Moving Average (UWMA) Charts 561

Table of ContentsExponentially Weighted Moving Average (EWMA) ChartCapability Analysis563564What Is Process Capability? 564Capability for One Process Measurement 567Capability for Many Process Measurements 569Capability for Time-Ordered Data 572A Few Words about Measurement SystemsExercises 57419 Mechanics of Statistics574577Overview 577Springs for Continuous Responses579Fitting a Mean 579Testing a Hypothesis 580One-Way Layout 581Effect of Sample Size Significance 581Effect of Error Variance on Significance 582Experimental Design’s Effect on Significance 583Simple Regression 584Leverage 585Multiple Regression 586Summary: Significance and Power 586Mechanics of Fit for Categorical Responses586How Do Pressure Cylinders Behave? 587Estimating Probabilities 588One-Way Layout for Categorical Data 589Logistic Regression 591AAnswers to Selected Exercises593Chapter 4, “Formula Editor” 593Chapter 7, “Univariate Distributions: One Variable, One Sample” 596Chapter 8, “The Difference Between Two Means” 600Chapter 9, “Comparing Many Means: One-Way Analysis of Variance” 602Chapter 10, “Fitting Curves through Points: Regression” 606Chapter 11, “Categorical Distributions” 609Chapter 12, “Categorical Models” 610Chapter 13, “Multiple Regression” 612Chapter 14, “Fitting Linear Models” 613Chapter 15, “Design of Experiments” 614Chapter 16, “Bivariate and Multivariate Relationships” 615Chapter 17, “Exploratory Modeling” 617Chapter 18, “Control Charts and Capability” 617xiii

xivTable of ContentsBReferences and Data SourcesTechnology License NoticesIndex627625619

JMP Start Statistics: A Guide to Statistics and DataAnalysis Using JMP , Sixth Edition. Full book availablefor purchase here.PreliminariesWhat You Need to Know about statisticsThis book is designed to help you learn about statistics. Even though JMP has manyadvanced features, you do not need a background of formal statistical training touse it. All analysis platforms include graphical displays with options that help youreview and interpret the results. Each platform also includes access to Help thatoffers general guidance and appropriate statistical details.Learning about JMP on your own with JMP HelpIf you are familiar with Macintosh or Microsoft Windows software, you might wantto proceed on your own. After you install JMP, you can open any of the JMP sampledata files and experiment with analysis tools. Help is available for most menus,options, and reports.There are several ways to access JMP Help: Select JMP Help from the Help menu. You can click the Help button in launch windows whenever you launch ananalysis or graph platform. After you generate a report, click the Help tool ( ) on the Tools menu ortoolbar and click the report surface. Context-sensitive help tells about the itemsthat you click.1

2Chapter 1 Preliminaries hands-on examplesThis book describes JMP features and is reinforced with hands-on examples. Byfollowing these step-by-step examples, you can quickly become familiar with JMPmenus, options, and report windows. Steps for example analyses begin with the mouse symbol in the margin, likethis paragraph. using TutorialsTutorials interactively guide you through some common tasks in JMP and areaccessible from the Help Tutorials menu. We recommend that you complete theBeginners Tutorial as a quick introduction to the report features found in JMP. reading about JMPJMP is accompanied by a series of built-in reference manuals, a menu referencecard and a quick reference card. The newest in the series of guides, DiscoveringJMP, provides a general introduction to JMP. It contains basic examples anddescriptions that give you a feel for JMP and can get you started.Discovering JMP is followed by Using JMP, which helps new users understandJMP data tables and how to perform basic operations. Using JMP is followed byseveral books that document all of the JMP analysis and graph platforms. Inaddition, there are specialty books for design of experiments and the JMPscripting language. These references cover all the commands and options in JMPand have extensive examples of the Analyze, Graph, and DOE platforms.The documentation is available in the following formats: In-product help (Select the Help JMP Help menu.) PDF files (Select the Help Books menu.) e-books (Amazon.com, Google Books, the Apple App Store, or the SASBookstore) Help at http://jmp.com/support/help Print books (Amazon.com or the SAS Bookstore)

Chapter 1 Preliminaries3Chapter OrganizationThe chapters of this book are supported by guided actions that you can take tobecome familiar with JMP.The first five chapters get you quickly started with information about JMP tables,how to use the JMP formula editor, and give an overview of how to obtain resultsfrom the Analyze and Graph menus. Chapter 1, “Preliminaries,” is this introductory material. Chapter 2, “Getting Started with JMP,” tells you how to start and stop JMP,how to open data tables, and takes you on a short guided tour. You areintroduced to the general personality of JMP. You see how data is handled byJMP. There is an overview of all analysis and graph commands; informationabout how to navigate a platform of results; and a description of the tools andoptions available for all analyses. Chapter 3, “Data Tables, Reports, and Scripts,” focuses on using the JMP datatable. It shows how to create tables, subset, sort, and manipulate them withbuilt-in menu commands, and how to get data and results out of JMP andinto a report. Chapter 4, “Formula Editor,” covers the formula editor and quick ways tocreate formulas and derived variables. There is a description of the formulaeditor components and an overview of the extensive functions available forcalculating column values. Chapter 5, “What Are Statistics?,” gives you some things to ponder about thenature and use of statistics. It also attempts to dispel statistical fears andphobias that are prevalent among students and professionals alike.Chapters 6–19 cover the array of analysis techniques offered by JMP. Chaptersbegin with simple-to-use techniques and gradually work toward more complexmethods. Emphasis is on learning to think about these techniques and on how tovisualize data analysis at work. JMP offers a graph for almost every statistic andsupporting tables for every graph. Using highly interactive methods, you canlearn more quickly and discover what your data has to say. Chapter 6, “Simulations,” introduces you to some probability topics by usingthe JMP scripting language. You learn how to open and execute these scriptsand to see other ways of simulating data in JMP.

4Chapter 1 Preliminaries Chapter 7, “Univariate Distributions: One Variable, One Sample,” coversdistributions of continuous and categorical variables and statistics to testunivariate distributions. Chapter 8, “The Difference Between Two Means,” covers t tests ofindependent groups and tells how to handle paired data. The nonparametricapproach to testing related pairs is also shown. Chapter 9, “Comparing Many Means: One-Way Analysis of Variance,” coversone-way analysis of variance, with standard statistics and a variety ofgraphical techniques. Chapter 10, “Fitting Curves through Points: Regression,” shows how to fit aregression model for a single factor. Chapter 11, “Categorical Distributions,” discusses how to think about thevariability in single batches of categorical data. It covers estimating andtesting probabilities in categorical distributions, shows Monte Carlomethods, and introduces the Pearson and Likelihood ratio chi-squarestatistics. Chapter 12, “Categorical Models,” covers fitting categorical responses to amodel, starting with the usual tests of independence in a two-way table, andcontinuing with graphical techniques and logistic regression. Chapter 13, “Multiple Regression,” describes the parts of a linear model withcontinuous factors, talks about fitting models with multiple numeric effects,and shows a variety of examples, including the use of stepwise regression tofind active effects. Chapter 14, “Fitting Linear Models,” is an advanced chapter that continuesthe discussion of Chapter 12. The chapter moves on to categorical effects andcomplex effects, such as interactions and nesting. Chapter 15, “Design of Experiments,” looks at the built-in commands in JMPused to generate specified experimental designs. It also looks at examples ofhow to analyze common screening and response-level designs are covered. Chapter 16, “Bivariate and Multivariate Relationships,” looks at ways toexamine two or more response variables using correlations, scatterplotmatrices, three-dimensional plots, principal components, and othertechniques. Discriminant and Cluster Analysis discuss methods that groupdata into clumps. Outliers are discussed. Chapter 17, “Exploratory Modeling,” illustrates common data miningtechniques—Neural Nets and Recursive Partitioning.

Chapter 1 Preliminaries5 Chapter 18, “Control Charts and Capability,” discusses common types ofcontrol charts for both continuous and attribute data, and introduces processcapability studies. Chapter 19, “Mechanics of Statistics,” is an essay about statistical fitting thatmight prove enlightening to those who enjoy mechanics.Typographical ConventionsThe following conventions help you relate written material in this book toinformation that you see on your screen. Reference to menu names (File menu) or menu items (Save command), andbuttons on windows (OK), appear in the Helvetica bold font. When you are asked to select a command from a submenu, such as File SaveAs, go to the File menu and select the Save As command. Likewise, items on menus in reports are shown in Helvetica bold, but you aregiven a more detailed instruction about where to find the command oroption. For example, you might be asked to select the Show Points optionfrom the red triangle menu on the analysis title bar. You might select the SavePredicted command from the Fitting menu on the scatterplot title bar. Eachmenu is always visible as a small red triangle on the platform or on its outlinetitle bars, as circled below. References to variable names, data table names, and some items in reportsappear in Helvetica but can appear in illustrations in either a plain or boldfacefont. These items show on your screen as you have specified in your JMPPreferences. Words or phrases that are important, new, or have definitions specific to JMPare in italics the first time you see them.

6Chapter 1 Preliminaries When there is an action statement, you can do the example yourself byfollowing the instructions. These statements are preceded by a mouse symbol( ) in the margin. An example of an action statement is: Highlight the Month column by clicking the area above the column name,and then select Cols Column Info. Occasionally, special information is in a boxed side bar in Helvetica to helpdistinguish them from the text flow.

PrefaceJMP 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 tostatistics using JMP.The SoftwareAs statistical discovery software, JMP emphasizes working interactively with dataand graphics in a progressive structure to make discoveries. With graphics, you are more likely to make discoveries. You are also morelikely to understand the results. With interactivity, you are encouraged to dig deeper and try out more thingsthat might improve your chances of discovering something important. Withinteractivity, one analysis leads to a refinement, and one discovery leads toanother discovery. With a progressive structure, you build a context that maintains a live analysis.You don’t have to redo analyses and plots to make changes in them, so detailscome to attention at the right time.The purpose of JMP software is to create a virtual workplace. The software hasfacilities and platforms where the tools are located and the work is performed. JMPprovides the workplace that we think is best for the job of analyzing data. With theright software workplace, researchers embrace computers and statistics, rather thanavoid them.xv

xviPrefaceJMP aims to present a graph with every statistic. You should always see theanalysis in both ways, with statistical text and graphics, without having to ask forit. The text and graphs stay together.JMP is controlled largely through point-and-click mouse manipulation. If youplace the pointer 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. Infact, 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 asyou dig deeper into the data, more and more options are offered according to thecontext of the analysis.In JMP, completeness is not measured by the “feature count,” but by the range ofpossible applications, and the orthogonality of the tools. In JMP, you get a feelingof being in more control despite your having 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 application is often the point of entry into the practice ofstatistics. JMP strives to offer fulfillment rather than frustration, empowermentrather than intimidation.If you give someone a large truck, they will find someone to drive it for them. Butif you give them a sports car, they will learn to drive it themselves. We believe thatstatistics can be interesting and reachable so that people will want to drive thatvehicle.How to Get JMPThere are several ways to get JMP: JMP is available through department or campus licenses at most colleges anduniversities and through site licenses in many organizations. See yoursoftware IT administrator for availability and download information. Individual copies of JMP for academic use are also available from http://onthehub.com/jmp. If you would like more information about academiclicensing or would like to request an evaluation copy of JMP for classroomuse, email academic@jmp.com.

Preface xviiIf you do not qualify for an academic license, a trial version of JMP isavailable at http://jmp.com/trial. Read license information at http://jmp.com/buy.JMP Start Statistics, Sixth EditionJMP Start Statistics has been updated and revised to feature JMP 13. Majorenhancements have been made to JMP since the fifth edition, which was based onJMP 10. The new enhancements include DOE (Design Evaluation, new CustomDesign options, and Definitive Screening designs), analysis and modeling(Generalized Regression, Partition enhancements, Model Comparison, andFormula Depot), data preparation (handling missing values and outliers, andmodel validation), and graphics (continued development of the interactive GraphBuilder), most of which are c

The correct bibliographic citation for this manu al is as follows: John Sall, Ann Lehman, Mia Stephens, and Sheila Loring. JMP Start Statistics: A Guide to Statistics and Data Analysis Using JMP , Sixth Editio