Sas DISCOVERING JMP RELEASE 9 User Manual

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Release 9

Discovering JMP

“The real voyage of discovery consists not in seeking new

landscapes, but in having new eyes.”

Marcel Proust

JMP, A Business Unit of SAS SAS Campus Drive Cary, NC 27513

The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2010. JMP® 9 Discovering JMP. Cary, NC: SAS Institute Inc.

JMP

9 Discovering JMP

ISBN 978-1-60764-600-6

For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc.

For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication.

U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).

SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513.

1st printing, September 2010

JMP

, SAS® and all other SAS Institute Inc. product or service names are registered trademarks or

trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

Other brand and product names are registered trademarks or trademarks of their respective companies.

Gallery of JMP Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

About This Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1 Introducing JMP

Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Concepts You Should Know . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

How Do I Get Started? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Starting JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Using Sample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Understanding Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Understanding the JMP Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Step 1: Launching a Platform and Viewing Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Step 2: Removing the Box Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Step 3: Requesting Additional Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Step 4: Interacting with Platform Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

How is JMP Different from Excel? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Columns Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Tables and Worksheets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

The Data Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Analysis and Graph Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Contents

Discovering JMP

How Do I Find Help? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2 Working with Your Data

Preparing Your Data for Graphing and Analyzing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Getting Your Data Into JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Copying and Pasting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Importing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Entering Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Working with Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Editing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Selecting, Deselecting, and Finding Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Viewing or Changing Column Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Calculating Values With Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Filtering Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Managing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Requesting Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Creating Subsets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Joining Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Sorting Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3 Visualizing Your Data

Graphing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Looking at Single Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Bar Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Comparing Multiple Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Scatterplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Scatterplot Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Side-by-Side Box Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Overlay Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Variability Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Bubble Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4 Analyzing Your Data

Distributions, Relationships, and Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

About This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

The Importance of Graphing Your Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Understanding Modeling Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Example: Modeling Type Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Changing the Modeling Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Analyzing Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Distributions of Continuous Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Distributions of Categorical Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Analyzing Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Using Regression with One Predictor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Comparing Averages for One Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Comparing Proportions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Comparing Averages for Multiple Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Using Regression with Multiple Predictors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5 Saving and Sharing Your Work

Saving and Recreating Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Saving Platform Results in Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Example: Creating a Journal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Adding Additional Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Creating Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Using Scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Example: Saving and Running a Script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

About Scripts and JSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Creating Adobe Flash Versions of the Profiler and Bubble Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Example: Saving an Adobe Flash Version of a Bubble Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

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6 Special Features

Automatic Updating and Integrating with SAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Automatically Updating Analyses and Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Example: Using Automatic Recalc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Changing Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Example: Changing Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Integrating JMP and SAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

Example: Creating SAS Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Example: Submitting SAS Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Index

Discovering JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Credits and Acknowledgments

Origin

JMP was developed by SAS Institute Inc., Cary, NC. JMP is not a part of the SAS System, though portions of JMP were adapted from routines in the SAS System, particularly for linear algebra and probability calculations. Version 1 of JMP went into production in October 1989.

Credits

JMP was conceived and started by John Sall. Design and development were done by John Sall, Chung-Wei Ng, Michael Hecht, Richard Potter, Brian Corcoran, Annie Dudley Zangi, Bradley Jones, Craige Hales, Chris Gotwalt, Paul Nelson, Xan Gregg, Jianfeng Ding, Eric Hill, John Schroedl, Laura Lancaster, Scott McQuiggan, Melinda Thielbar, Clay Barker, Peng Liu, Dave Barbour, Jeff Polzin, John Ponte, and Steve Amerige.

In the SAS Institute Technical Support division, Duane Hayes, Wendy Murphrey, Rosemary Lucas, Win LeDinh, Bobby Riggs, Glen Grimme, Sue Walsh, Mike Stockstill, Kathleen Kiernan, and Liz Edwards provide technical support.

Nicole Jones, Kyoko Keener, Hui Di, Joseph Morgan, Wenjun Bao, Fang Chen, Susan Shao, Yusuke Ono, Michael Crotty, Jong-Seok Lee, Tonya Mauldin, Audrey Ventura, Ani Eloyan, Bo Meng, and Sequola McNeill provide ongoing quality assurance. Additional testing and technical support are provided by Noriki Inoue, Kyoko Takenaka, and Masakazu Okada from SAS Japan.

Bob Hickey and Jim Borek are the release engineers.

The JMP books were written by Ann Lehman, Lee Creighton, John Sall, Bradley Jones, Erin Vang, Melanie Drake, Meredith Blackwelder, Diane Perhac, Jonathan Gatlin, Susan Conaghan, and Sheila Loring, with contributions from Annie Dudley Zangi and Brian Corcoran. Creative services and production was done by SAS Publications. Melanie Drake implemented the Help system.

Jon Weisz and Jeff Perkinson provided project management. Also thanks to Lou Valente, Ian Cox, Mark Bailey, and Malcolm Moore for technical advice.

Thanks also to Georges Guirguis, Warren Sarle, Gordon Johnston, Duane Hayes, Russell Wolfinger, Randall Tobias, Robert N. Rodriguez, Ying So, Warren Kuhfeld, George MacKensie, Bob Lucas, Warren Kuhfeld, Mike Leonard, and Padraic Neville for statistical R&D support. Thanks are also due to Doug Melzer, Bryan Wolfe, Vincent DelGobbo, Biff Beers, Russell Gonsalves, Mitchel Soltys, Dave Mackie, and Stephanie Smith, who helped us get started with SAS Foundation Services from JMP.

Acknowledgments

We owe special gratitude to the people that encouraged us to start JMP, to the alpha and beta testers of JMP, and to the reviewers of the documentation. In particular we thank Michael Benson, Howard

Yetter (d), Andy Mauromoustakos, Al Best, Stan Young, Robert Muenchen, Lenore Herzenberg, Ramon Leon, Tom Lange, Homer Hegedus, Skip Weed, Michael Emptage, Pat Spagan, Paul Wenz, Mike Bowen, Lori Gates, Georgia Morgan, David Tanaka, Zoe Jewell, Sky Alibhai, David Coleman, Linda Blazek, Michael Friendly, Joe Hockman, Frank Shen, J.H. Goodman, David Iklé, Barry Hembree, Dan Obermiller, Jeff Sweeney, Lynn Vanatta, and Kris Ghosh.

Also, we thank Dick DeVeaux, Gray McQuarrie, Robert Stine, George Fraction, Avigdor Cahaner, José Ramirez, Gudmunder Axelsson, Al Fulmer, Cary Tuckfield, Ron Thisted, Nancy McDermott, Veronica Czitrom, Tom Johnson, Cy Wegman, Paul Dwyer, DaRon Huffaker, Kevin Norwood, Mike Thompson, Jack Reese, Francois Mainville, and John Wass.

We also thank the following individuals for expert advice in their statistical specialties: R. Hocking and P. Spector for advice on effective hypotheses; Robert Mee for screening design generators; Roselinde Kessels for advice on choice experiments; Greg Piepel, Peter Goos, J. Stuart Hunter, Dennis Lin, Doug Montgomery, and Chris Nachtsheim for advice on design of experiments; Jason Hsu for advice on multiple comparisons methods (not all of which we were able to incorporate in JMP); Ralph O’Brien for advice on homogeneity of variance tests; Ralph O’Brien and S. Paul Wright for advice on statistical power; Keith Muller for advice in multivariate methods, Harry Martz, Wayne Nelson, Ramon Leon, Dave Trindade, Paul Tobias, and William Q. Meeker for advice on reliability plots; Lijian Yang and J.S. Marron for bivariate smoothing design; George Milliken and Yurii Bulavski for development of mixed models; Will Potts and Cathy Maahs-Fladung for data mining; Clay Thompson for advice on contour plotting algorithms; and Tom Little, Damon Stoddard, Blanton Godfrey, Tim Clapp, and Joe Ficalora for advice in the area of Six Sigma; and Josef Schmee and Alan Bowman for advice on simulation and tolerance design.

For sample data, thanks to Patrice Strahle for Pareto examples, the Texas air control board for the pollution data, and David Coleman for the pollen (eureka) data.

Translations

Trish O'Grady coordinates localization. Special thanks to Noriki Inoue, Kyoko Takenaka, Masakazu Okada, Naohiro Masukawa and Yusuke Ono (SAS Japan); and Professor Toshiro Haga (retired, Tokyo University of Science) and Professor Hirohiko Asano (Tokyo Metropolitan University) for reviewing our Japanese translation; Professors Fengshan Bai, Xuan Lu, and Jianguo Li at Tsinghua University in Beijing, and their assistants Rui Guo, Shan Jiang, Zhicheng Wan, and Qiang Zhao; and William Zhou (SAS China) and Zhongguo Zheng, professor at Peking University, for reviewing the Simplified Chinese translation; Jacques Goupy (consultant, ReConFor) and Olivier Nuñez (professor, Universidad Carlos III de Madrid) for reviewing the French translation; Dr. Byung Chun Kim (professor, Korea Advanced Institute of Science and Technology) and Duk-Hyun Ko (SAS Korea) for reviewing the Korean translation; Bertram Schäfer and David Meintrup (consultants, StatCon) for reviewing the German translation; Patrizia Omodei, Maria Scaccabarozzi, and Letizia Bazzani (SAS Italy) for reviewing the Italian translation. Finally, thanks to all the members of our outstanding translation teams.

Past Support

Many people were important in the evolution of JMP. Special thanks to David DeLong, Mary Cole, Kristin Nauta, Aaron Walker, Ike Walker, Eric Gjertsen, Dave Tilley, Ruth Lee, Annette Sanders, Tim Christensen, Eric Wasserman, Charles Soper, Wenjie Bao, and Junji Kishimoto. Thanks to SAS Institute quality assurance by Jeanne Martin, Fouad Younan, and Frank Lassiter. Additional testing for Versions 3 and 4 was done by Li Yang, Brenda Sun, Katrina Hauser, and Andrea Ritter.

Also thanks to Jenny Kendall, John Hansen, Eddie Routten, David Schlotzhauer, and James Mulherin. Thanks to Steve Shack, Greg Weier, and Maura Stokes for testing JMP Version 1.

Thanks for support from Charles Shipp, Harold Gugel (d), Jim Winters, Matthew Lay, Tim Rey, Rubin Gabriel, Brian Ruff, William Lisowski, David Morganstein, Tom Esposito, Susan West, Chris Fehily, Dan Chilko, Jim Shook, Ken Bodner, Rick Blahunka, Dana C. Aultman, and William Fehlner.

Technology License Notices

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WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL NEIL HODGSON BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL KEITH PACKARD BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

KEITH PACKARD DISCLAIMS ALL WARRANTIES WITH REGARD

NEIL HODGSON DISCLAIMS ALL

viii

Gallery of JMP Graphs

Various Graphs and their Platforms

Here are pictures of many of the graphs that you can create with JMP. Each picture is labeled with the platform used to create it. For more information about the platforms and these and other graphs, see the documentation on the

Help > Books menu.

Histogram Analyze > Distribution

t Test Analyze > Fit Y by X

Bivariate Analyze > Fit Y by X

Logistic Analyze > Fit Y by X

Oneway Analyze > Fit Y by X

Mosaic Plot Analyze > Fit Y by X

LS Means Plot Analyze > Fit Model

Neural Diagram Analyze > Modeling > Neural

Matched Pairs Analyze > Matched Pairs

MANOVA Analyze > Fit Model

Actual by Predicted Plot Analyze > Fit Model

Screening Analyze > Modeling > Screening

Partition Analyze > Modeling > Partition

Time Series Analyze > Modeling > Time Series

Share Chart Analyze > Modeling > Categorical

Self Organizing Map Analyze > Multivariate Methods > Cluster

Principal Components

Dendrogram Analyze > Multivariate Methods > Cluster

Analyze > Multivariate Methods > Principal Components

Canonical Plot Analyze > Multivariate Methods > Discriminant

Characteristic Curves Analyze > Multivariate Methods > Item Analysis

Loadings Plot Analyze > Multivariate Methods > PLS

Dual Plot Analyze > Multivariate Methods > Item Analysis

Scatterplot Analyze > Reliability and Survival > Fit Life by X

Compare Distributions Analyze > Reliability and Survival > Life Distribution

MCF Plot Analyze > Reliability and Survival > Recurrence Analysis

Nonparametric Overlay Analyze > Reliability and Survival > Fit Life by X

Line Graphs Graph > Graph Builder

Pie Chart

Box Plots Graph > Graph Builder

Stacked Bar Chart Graph > Chart

Graph > Chart

Needle and Line Chart Graph > Overlay Plot

Three Dimensional Scatterplot Graph > Scatterplot 3D

Dot and Line Chart Graph > Overlay Plot

Contour Plot Graph > Contour Plot

Three Dimensional Scatterplot Graph > Scatterplot 3D

Bubble Plot Graph > Bubble Plot

Parallel Plot Graph > Parallel Plot

Scatterplot Matrix Graph > Scatterplot Matrix

Cell Plot Graph > Cell Plot

Ter n a ry P l o t Graph > Ternary Plot

Tree Map Graph > Tree Map

Ishikawa Chart Fishbone Chart Graph > Diagram

Individual Measurement Chart Moving Range Chart Graph > Control Chart > IR

XBar Chart Graph > Control Chart > XBar

Variability Chart Graph > Variability/Gauge Chart

Goal Plot Graph > Capability

Prediction Profiler

Graph > Profiler Pareto Plot Graph > Pareto Plot

Contour Profiler Graph > Contour Profiler

Surface Plot Graph > Surface Plot

Mixture Profiler

Graph > Mixture Profiler

About This Guide

Discovering JMP provides a general introduction to the JMP software. This guide assumes that you have no knowledge of JMP. Whether you are an analyst, researcher, student, professor, or statistician, this guide gives you a general overview of JMP’s user interface and features.

This guide introduces you to the following information:

•Starting JMP

•The structure of a JMP window

• Preparing and manipulating data

• Using interactive graphs to learn from your data

• Performing simple analyses to augment graphs

• Customizing JMP and special features

This guide contains six chapters. Each chapter contains examples that reinforce the concepts presented in the chapter. All of the statistical concepts are at an introductory level. The sample data used in this book are included with the software. Here is a description of each chapter:

•Chapter 1, Introducing JMP, provides an overview of the JMP application. You learn how content is organized and how to navigate the software.

• Chapter 2, Working with Your Data, describes how to import data from a variety of sources, and prepare it for analysis. There is also an overview of data manipulation tools.

•Chapter 3, Visualizing Your Data, describes graphs and charts you can use to visualize and understand your data. The examples range from simple analyses involving a single variable, to multiple-variable graphs that enable you to see relationships between many variables.

•Chapter 4, Analyzing Your Data, describes many commonly used analysis techniques. These techniques range from simple techniques that do not require the use of statistical methods, to advanced techniques, where knowledge of statistics is useful.

•Chapter 5, Saving and Sharing Your Work, describes using journals and projects, and saving scripts.

•Chapter 6, Special Features, describes how to automatically update graphs and analyses as data changes, how to use preferences to customize your reports, and how JMP interacts with SAS.

After reviewing this guide, you will be comfortable navigating and working with your data in JMP.

While JMP is available for both Windows and Macintosh operating systems, the material in this guide is based on a Windows operating system.

Chapter 1

Introducing JMP

Basic Concepts

JMP (pronounced jump) is a powerful and interactive data visualization and statistical analysis tool. Use JMP to learn more about your data by performing analyses and interacting with the data using data tables, graphs, charts, and reports.

JMP is useful to the researcher who wants to perform a wide range of statistical analyses and modeling. JMP is equally useful to the business analyst who wants to quickly uncover trends and patterns in data. With JMP, you do not have to be an expert in statistics to get information from your data.

For example, you can use JMP to do the following:

• Create interactive graphs and charts to explore your data and discover relationships.

• Discover patterns of variation across many variables at once.

• Explore and summarize large amounts of data.

• Develop powerful statistical models to predict the future.

Figure 1.1 Examples of JMP Reports

Contents

Concepts You Should Know . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

How Do I Get Started? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Starting JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Using Sample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

Understanding Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

Understanding the JMP Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Step 1: Launching a Platform and Viewing Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Step 2: Removing the Box Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Step 3: Requesting Additional Output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

Step 4: Interacting with Platform Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

How is JMP Different from Excel? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Columns Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Tables and Worksheets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

The Data Grid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Analysis and Graph Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

How Do I Find Help? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Chapter 1 Introducing JMP 13

Concepts You Should Know

Before you begin using JMP, you should be familiar with these concepts:

• Enter, view, edit, and manipulate data using JMP data tables.

• Select a platform from the use to analyze data and work with graphs.

• Platforms use these windows:

– Launch windows where you set up and run your analysis.

– Report windows showing the output of your analysis.

• Report windows normally contain the following items:

– A graph of some type (such as a scatterplot or a chart).

–Specific reports that you can show or hide using the disclosure button .

–Platform options that are located within red triangle menus .

Analyze and Graph menus. Platforms contain interactive windows that you

How Do I Get Started?

The general workflow in JMP is simple:

1. Get your data into JMP.

2. Select a platform and complete its launch window.

3. Explore your results and discover where your data takes you.

This workflow is described in more detail in “Understanding the JMP Workflow,” p. 18.

Typically, you start your work in JMP by using graphs to visualize individual variables and relationships among your variables. Graphs make it easy to see this information, and to see the deeper questions to ask. Then you use analysis platforms to dig deeper into your problems and find solutions.

•The “Working with Your Data” chapter shows you how to get data into JMP.

•The “Visualizing Your Data” chapter shows you how to use some of the useful graphs JMP provides to look more closely at your data.

•The “Analyzing Your Data” chapter shows you how to use some of the analysis platforms.

Each chapter teaches through examples. The following sections in this chapter describe data tables and general concepts for working in JMP.

Starting JMP

Start JMP in two ways:

• Double-click on the JMP icon, normally found on your desktop. This starts JMP, but does not open any existing JMP files.

14 Introducing JMP Chapter 1

How Do I Get Started?

• Double-click an existing JMP file. This starts JMP and opens the file.

The initial view of JMP includes the Tip of the Day window and the JMP Starter window. The JMP Starter window classifies actions and platforms using categories.

Figure 1.2 The JMP Starter

On the left is a list of categories. Click a category to see the features and the commands related to that category. For a description of all of the features in the JMP Starter, see Using JMP.

Another useful window is the Home window.

Chapter 1 Introducing JMP 15

How Do I Get Started?

Figure 1.3 The Home Window

To open the Home window, select View > Home Window. This window includes links to the following:

• the data tables and report windows that are currently open

• files that you have opened recently

For more details about the JMP Starter window and the Home window, see Using JMP.

Almost all JMP windows contain a menu bar and a toolbar. You can find most JMP features in three ways:

• using the menu bar

• using the toolbar buttons

• using the buttons on the JMP Starter window

Note: By default, windows in JMP are not maximized. This enables you to see the interaction between the windows.

About the Menu Bar and Toolbars

The menus and toolbars are hidden in many windows. To see them, hover your mouse cursor over the blue bar under the window’s title bar. The menus in the JMP Starter window, the Home window, and all data tables are always visible.

16 Introducing JMP Chapter 1

Understanding Data Tables

Using Sample Data

The examples in this book and the other JMP books use sample data tables. The default location on Windows for the sample data is here:

C:\Program Files\SAS\JMP\9\Support Files English\Sample Data

The Sample Data Index groups the data tables by category. Click a disclosure button to see a list of data tables for that category, and then click a link to open a data table.

Opening a JMP sample data table

1. From the

2. Open the

3. Click the name of the data table to use it in the examples in this book.

Sample Import Data

Use files from other applications to learn how to import data into JMP.

The default location on Windows for the sample import data is here:

C:\Program Files\SAS\JMP\9\Support Files English\Sample Import Data

Help menu, select Sample Data.

Data Tables for Discovering JMP list by clicking on the disclosure button next to it.

Understanding Data Tables

A data table is a collection of data organized in rows and columns. It is similar to a Microsoft® Excel® spreadsheet, but with some important differences that are discussed in “How is JMP Different from Excel?,”

p. 23. A data table might also contain other information like notes, variables, and scripts. These

supplementary items are discussed in later chapters.

Open the VA Lung Cancer data table to see the data table described here.

Chapter 1 Introducing JMP 17

The data grid has rows and columns for data

Ta bl e panel

Columns panel

Rows panel

Column names

Thumbnail links to report windows

Understanding Data Tables

Figure 1.4 A Data Table

A data table contains the following parts:

Data grid The data grid contains the data arranged in rows and columns. Generally, each row in the

data grid is an observation, and the columns (also called variables) give information about the observations. In Figure 1.4, each row corresponds to a test subject, and there are twelve columns of information. Although all twelve columns cannot be shown in the data grid, the Columns panel lists them all. The information given about each test subject includes the time, cell type, treatment, and more. Each column has a header, or name. That name is not part of the table’s total count of rows.

Table panel The table panel can contain table variables or table scripts. In Figure 1.4, there is one

saved script called

Model that can automatically recreate an analysis. This table also has a variable

named Notes that contains information about the data. Table variables and table scripts are discussed in a later chapter.

18 Introducing JMP Chapter 1

Understanding the JMP Workflow

Columns panel

The columns panel shows the total number of columns, whether any columns are selected, and a list of all the columns by name. The numbers in parentheses (12/0) show that there are twelve columns, and that no columns are selected. An icon to the left of each column name shows that column’s modeling type. Modeling types are described in “Understanding Modeling

Ty p es ,” p. 92 in the “Analyzing Your Data” chapter. Icons to the right show any attributes assigned

to the column. See “Viewing or Changing Column Information,” p. 39 in the “Working with Your Data” chapter for more information about these icons.

Rows panel The rows panel shows the number of rows in the data table, and how many rows are

selected, excluded, hidden, or labeled. In Figure 1.4, there are 137 rows in the data table.

Thumbnail links to report windows This area shows thumbnails of all reports based on the data

table. Hover over one to see a larger preview of the report window. Double-click a thumbnail to bring the report window to the front.

Interacting with the data grid, which includes adding rows and columns, entering data, and editing data, is discussed in the “Working with Your Data” chapter. If you open multiple data tables, each one appears in a separate window.

Understanding the JMP Workflow

Once your data is in a data table, you can create graphs or plots, and perform analyses. All features are located in platforms, which are found primarily on the because they do not just produce simple static results. Platform results appear in report windows, are highly interactive, and are linked to the data table and to each other.

Analyze or Graph menus. They are called platforms

The platforms under the

Analyze and Graph menus provide a variety of analytical features and data

exploration tools.

The general steps to produce a graph or analysis are as follows:

1. Open a data table.

2. Select a platform from the Graph or Analysis menu.

3. Complete the platform launch window to set up your analysis.

4. Click OK to create the report window that contains your graphs and statistical analyses.

5. Customize your report by using report options.

6. Save, export, and share your results with others.

Later chapters discuss these concepts in greater detail.

The following example shows you how to perform a simple analysis and customize it in four steps. This example uses the

Companies.jmp file sample data table to show a basic analysis of the variable Profits ($M).

Chapter 1 Introducing JMP 19

Understanding the JMP Workflow

Step 1: Launching a Platform and Viewing Results

1. Open the Companies.jmp data table.

2. Select

3. Select

Figure 1.5 Assign Profits ($M)

Analyze > Distribution to open the Distribution launch window.

Profits ($M) in the Select Columns box and click the Y, Co l u m ns button.

The variable

Profits ($M) appears in the Y, C o lum n s role. See Figure 1.5 for the completed window.

Another way to assign variables is to click and drag columns from the Select Columns box to any of the roles boxes.

4. Click OK.

The Distribution report window appears.

20 Introducing JMP Chapter 1

Disclosure buttons

Red triangle menus

Blue bar that indicates the hidden menu bar and toolbars

Link to data table

Understanding the JMP Workflow

Figure 1.6 Distribution Report Window

Step 2: Removing the Box Plot

The report window contains basic plots or graphs and preliminary analysis reports. The results appear in an outline format, and you can show or hide any report by clicking on the disclosure button.

Red triangle menus contain options and commands to request additional graphs and analyses at any time.

Hover over the blue bar at the top of the window to see the menu bar and the toolbars.

Click the data table button to bring the data table that was used to create this report to the front.

Continue using the Distribution report that you created earlier.

1. Click the red triangle next to

2. Deselect

Outlier Box Plot to turn the option off.

Profits ($M) to see a menu of report options.

The outlier box plot is removed from the report window.

Chapter 1 Introducing JMP 21

Click here to remove the outlier box plot.

Understanding the JMP Workflow

Figure 1.7 Removing the Outlier Box Plot

Step 3: Requesting Additional Output

Continue to use the same report window.

1. From the red triangle menu next to

Profits ($M), select Test Mean.

The Test Mean window appears.

2. Enter 500 in the

3. Click

OK.

Specify Hypothesized Mean box.

The test for the mean is added to the report window.

Figure 1.8 Test for the Mean

22 Introducing JMP Chapter 1

Click here to collapse the Quantiles report

Understanding the JMP Workflow

Step 4: Interacting with Platform Results

All platforms produce results that are interactive. For example:

• Reports can be shown or hidden.

• Additional graphs and statistical details can be added or removed to suit your purposes.

• Platform results are connected to the data table and to each other.

For example, to close the

Figure 1.9 Close the Quantiles Report

Quantiles report, click the disclosure button next to Quantiles.

Platform results are connected to the data table. The histogram in Figure 1.10 shows that a group of companies makes a much higher profit that the others. To quickly identify that group, click on the histogram bar for them. The corresponding rows in the data table are selected.

Chapter 1 Introducing JMP 23

Click the bar to select the corresponding rows

How is JMP Different from Excel?

Figure 1.10 Connection Between Platform Results and Data Table

In this case, the group includes only one company, and that one row is selected.

How is JMP Different from Excel?

There are a number of important differences between JMP and Excel or other spreadsheet applications.

Formulas

Excel Formulas are applied to individual cells.

JMP Formulas are applied only to entire columns. “Calculating Values With Formulas,” p. 40 in the

“Working with Your Data” chapter describes how to use formulas.

Columns Names

Excel Column names are part of the grid. Numbered rows and labeled columns extend past the data.

Numeric and character data reside in the same column.

JMP Column names are not part of the grid. There are no rows and columns beyond the existing data.

The grid is only as big as the data. A column is either numeric or character. If a column contains both character and numeric data, the entire column’s data type is character, and the numbers are treated as character data.

“Understanding Modeling Types,” p. 92 in the “Analyzing Your Data” chapter describes how data type

influences platform results.

24 Introducing JMP Chapter 1

How Do I Find Help?

Tables and Worksheets

Excel A single spreadsheet contains several tables, or worksheets.

JMP JMP does not have the concept of worksheets. Each data table is a separate .jmp file and appears

in a separate window.

The Data Grid

Excel Data can be located anywhere in the data grid.

JMP Data always begins in row 1 and column 1.

Analysis and Graph Reports

Excel All data, analyses, and graphs are placed inside the data grid.

JMP Results appear in a separate window.

How Do I Find Help?

As you start using JMP, a variety of resources are available to supplement your learning. The JMP books, Help, web information, sample data, and more are all available on the Help menu.

Ta bl e 1 .1 Help Menu Options

Option Description

Contents, Search, and Index

Tip of the Day

Tutorials

These three menu items open the JMP Help system. The Help system provides navigable and searchable documentation.

A collection of helpful tips and hints that enhance your experience with JMP.

Interactive tutorials that demonstrate how to use some of JMP’s statistical and graphical features.

If you are not familiar with JMP, start with the Beginners Tutorial. Every new JMP user should take this five-minute tutorial on the user-interface basics of JMP.

Books

Contains links to the full documentation book set, a description of all the JMP menus, and a quick reference describing keyboard shortcuts.

Sample Data

Provides links to all the sample data used in the documentation. The sample data tables help when you are learning JMP.

Statistics Index

JSL Functions

Provides definitions of statistical terms.

Describes JSL functions and provides examples and topic help.

Chapter 1 Introducing JMP 25

How Do I Find Help?

Ta bl e 1 .1 Help Menu Options (Continued)

Option Description

Object Scripting

DisplayBox Scripting

JMP.com

JMP User Community

About JMP

Describes JSL objects and messages and provides examples and topic help.

Describes JSL display box objects.

Takes you to the JMP Web site (www.jmp.com). The Web site contains information about JMP and provides links to the JMP Blog, JMP Discussion Forum, JMP User Community, the latest news and events, and file sharing. You can register for free weekly webinars, and find podcasts and other JMP literature. The Web site also contains videos that demonstrate the newest JMP features. You can also learn how to join one of the regional JMP User’s Groups.

Takes you to the online user forum where you can download JMP files, read the JMP Blog, and discuss JMP topics with other users.

Shows the release, copyright, operating system, and the owner of the copy of JMP that is running.

26 Introducing JMP Chapter 1

How Do I Find Help?

Chapter 2

Working with Your Data

Preparing Your Data for Graphing and Analyzing

Before graphing or analyzing your data, the data has to be in a data table and in the proper format. This chapter shows some basic data management tasks, including the following:

• creating new data tables

• opening existing data tables

• importing data from other applications into JMP

• managing your data

Figure 2.1 Example of a Data Table

Contents

Getting Your Data Into JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Copying and Pasting Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Importing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Entering Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Working with Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

Editing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

Selecting, Deselecting, and Finding Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Viewing or Changing Column Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

Calculating Values With Formulas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Filtering Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Managing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Requesting Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45

Creating Subsets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Joining Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Sorting Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Chapter 2 Working with Your Data 29

Getting Your Data Into JMP

• To copy and paste data from another application, see “Copying and Pasting Data,” p. 29.

• To import data from another application, see “Importing Data,” p. 29.

• To enter data directly into a data table, see “Entering Data,” p. 31

• To open a data table, double-click on the file, or use the

You can also import data into JMP from a database. For more information, see Using JMP.

This chapter uses sample data tables and sample import data that is installed with JMP. To find these files, see “Using Sample Data,” p. 16 in the “Introducing JMP” chapter.

Copying and Pasting Data

You can move data into JMP by copying and pasting from another application, such as Excel or a text file.

Example: Copying and pasting data

File > Open command.

1. Open the

2. Select all of the rows and columns, including the column names. There are 12 columns and 138 rows.

3. Copy the selected data.

4. In JMP, select

5. Select

Edit > Paste with Column Names to paste the data and column headings.

If the data that you are pasting into JMP does not have column names, then you can use

Importing Data

You can move data into JMP by importing data from another application, such as Excel, SAS, or text files. The basic steps to import data are as follows:

1. Select

2. Navigate to your file’s location.

3. If your file is not listed in the Open Data File window, select the correct file type from the

4. Click

Example: Importing a Microsoft Excel File

1. Select

2. Navigate to the

3. Select VA Lung Cancer.xls.

4. Click

File > Open.

menu.

Open.

File > Open.

Open.

VA Lung Cancer.xls file in Excel. This file is located in the Sample Import Data folder.

File > New > Data Table to create an empty table.

Edit > Paste.

Files of type

Sample Import Data folder.

30 Working with Your Data Chapter 2

Getting Your Data Into JMP

The Open Data File window has additional options:

• You can control whether JMP assumes that column headings are on row one in the Excel file.

• If your Excel file has multiple worksheets, choose which worksheets to import. Each worksheet is imported to a separate data table.

• Select which version of Excel you need to import from the

All JMP Files does not show your Excel file, select the correct version from this menu.

Files of type menu. If the default setting of

When you import Excel files, JMP predicts whether columns headings exist, and if the column names are on row one. The copy and paste method is recommended for the following situations:

• if the column names are located in a row other than row one

• if the file does not include column names and the data does not start in row one

• if the file contains column names and the data does not start in row two

See “Copying and Pasting Data,” p. 29.

Example: Importing a Text File

One way to import a text file is to let JMP assume the data’s format and place the data in a data table. This method uses settings that you can specify in Preferences. See Using JMP for information about setting text import preferences.

Another way to import a text file is to use a Text Preview window to see what your data table will look like after importing, and make adjustments. The following example shows you how to use Text Import Preview window.

1. Select

2. Navigate to the

3. Select

4. At the bottom of the Open window, select

5. Click

File > Open.

Sample Import Data folder.

Animals_line3.txt.

Data with preview.

Open.

Chapter 2 Working with Your Data 31

Getting Your Data Into JMP

Figure 2.2 Initial Preview Window

This text file has a title on the first line, column names on the third line, and the data starts on line four. If you opened this directly in JMP, the Animals Data line would be the first column name, and all the column names and data afterward would be out of sync. The Preview window lets you adjust the settings before you open the file, and see how your adjustments affect the final data table.

6. Enter 3 in the

7. Enter 4 in the

8. Click

In the second window, you can exclude columns from the import and change the data modeling of the columns. For this example, use the default settings.

9. Click

The new data table has columns named columns are character data. The

Entering Data

You can enter data directly in a data table. The following example shows you how to enter data that was collected over several months into a data table.

File contains column names on line field.

Data starts on line field.

Next.

Import.

species, subject, miles, and season. The species and season

subject and miles columns are continuous numeric data.

32 Working with Your Data Chapter 2

Click once to select the column.

Click again, and then type “Month”.

Getting Your Data Into JMP

Scenario

Table 2.1 shows the data from a study that investigated a new blood pressure medication. Each individual’s blood pressure was measured over a six month period. Two doses (300mg and 450mg) of the medication were used, along with a control and placebo group. The data shows the average blood pressure for each group.

Ta bl e 2 .1 Blood Pressure Data

Month Control Placebo 300mg 450mg

March 165 163 166 168

April 162 159 165 163

May 164 158 161 153

June 162 161 158 151

July 166 158 160 148

August 163 158 157 150

Entering Data in a New Data Table

1. Select

File > New > Data Table to create an empty data table.

A new data table has one column and no rows.

2. Click the column name to select it, and then click again to edit the name.

Note: If you double-click too quickly, the Column Info window appears. You can change the column name there as well.

3. Change the column name to

Figure 2.3 Entering a Column Name

Month. See Figure 2.3.

Chapter 2 Working with Your Data 33

Columns panel

Modeling type icon

Double-click here, and then type the new name.

Getting Your Data Into JMP

4. Select Rows > Add Rows.

The Add Rows window appears.

5. Since you want to add six rows, type 6.

6. Click

7. Enter the

Figure 2.4 Month Column Completed

OK. Six empty rows are added to the data table.

Month information by double-clicking in a cell and typing.

In the columns panel, look at the modeling type icon to the left of the column name. It has changed to reflect that

Month is now nominal (previously it was continuous). Compare the modeling type shown for

Column 1 in Figure 2.3 and for Month in Figure 2.4. This difference is important and is discussed in

“Viewing or Changing Column Information,” p. 39.

8. Double-click in the space on the right side of the Month column to add the

9. Change the name to

10. Enter the

Control data as shown in Table 2.1. Your data table now consists of six rows and two columns.

11. Continue adding columns and entering data as shown in Table 2.1 to create the final data table with six rows and five columns.

Changing the data table name

1. Double-click on the data table name (Untitled) in the Table Panel.

2. Type the new name (Blood Pressure).

Figure 2.5 Changing the Data Table Name

Control column.

Control.

34 Working with Your Data Chapter 2

Working with Data Tables

This section contains the following information:

• “Editing Data,” p. 34

• “Selecting, Deselecting, and Finding Values,” p. 35

• “Viewing or Changing Column Information,” p. 39

• “Filtering Data,” p. 42

• “Calculating Values With Formulas,” p. 40

Editing Data

You can enter or change data, either a few cells at a time or for an entire column. This section contains the following information:

• “Changing Values,” p. 34

• “Recoding Values,” p. 34

• “Creating Patterned Data,” p. 35

The examples in this section use the table.

Changing Values

To change a value, select a cell and type the change. You can also double-click a cell to edit it.

Note: Double-clicking in a cell is not the same as selecting a cell. A single click selects a cell. You can select more than one cell at the same time, and you can perform certain actions on selected cells. Double-clicking only lets you edit a cell. For more information about selecting rows, columns, and cells, see “Selecting,

Deselecting, and Finding Values,” p. 35.

Recoding Values

Use the recoding tool to change all of the values in a column at once. For example, suppose you are interested in comparing the sales of computer and pharmaceutical companies. Your current company labels are Computer and Pharmaceutical. You want to change them to Technical and Drug. Going through all 32 rows of data and changing all the values would be tedious, inefficient, and error-prone, especially if you had many more rows of data. Recode is a better option.

1. Select the

2. Select

3. In the Recode window, enter the desired values in the Technical in the Computer row, and Drug in the Pharmaceutical row.

4. Select the

Companies.jmp sample data table. Before you continue, open this data

Ty pe column by clicking once on the column heading.

Cols > Recode.

New Values boxes. For this example, enter

In Place option from the menu.

Chapter 2 Working with Your Data 35

Working with Data Tables

Figure 2.6 Recode Window

5. Click OK.

All cells are updated automatically to the new values.

Creating Patterned Data

Use the Fill options to populate a column with patterned data. The Fill options are especially useful if your data table is large, and typing in the values for each row would be cumbersome.

Example: Filling a column with the pattern

1. Add a new column.

2. Enter 1 in the first cell, 2 in the second cell, and 3 in the third cell.

3. Select the three cells, and right-click anywhere in the selected cells to see a menu.

4. Select

Fill > Repeat sequence to end of table.

The rest of the column is filled with the sequence (1, 2, 3, 1, 2, 3, ...).

To continue a pattern instead of repeating it (1, 2, 3, 4, 5, 6, ...), select

table

. This command can also be used to generate patterns like (1, 1, 1, 2, 2, 2, 3, 3, 3, ...).

The Fill options can recognize simple arithmetic and geometric sequences. For character data, the Fill options only repeat the values.

Selecting, Deselecting, and Finding Values

You can select rows, columns, or cells within a data table. For example, to create a subset of an existing data table, you must first select the parts of the table that you want to subset. Also, selecting rows can make data points stand out on a graph. Select rows and columns manually by clicking, or select rows that meet certain search criteria. This section contains the following information:

• “Selecting and Deselecting Rows,” p. 36

• “Selecting and Deselecting Columns,” p. 36

• “Selecting and Deselecting Cells,” p. 37

• “Searching for Values,” p. 38

Continue sequence to end of

36 Working with Your Data Chapter 2

To deselect all rows at once, click here.

Working with Data Tables

Selecting and Deselecting Rows

Ta bl e 2 .2 Selecting and Deselecting Rows

Task Action

Select rows one at a time Click on the row number.

Select multiple adjacent rows Click and drag on the row numbers.

Select the beginning row, and then hold down the SHIFT key and click the last row number.

Select multiple non-adjacent rows Select the first row, and then hold down the CTRL key and

click the other row numbers.

Deselect rows one at a time Hold down the CTRL key and click the row numbers.

Deselect all rows Click in the lower-triangular space in the top left corner of the

table. See Figure 2.7.

Figure 2.7 Deselecting Rows

Selecting and Deselecting Columns

Ta bl e 2 .3 Selecting and Deselecting Columns

Task Action

Select columns one at a time Click the column heading.

Select multiple adjacent columns Click and drag across the column headings.

Select the beginning column, and then hold down the SHIFT key and click the last header.

Chapter 2 Working with Your Data 37

To deselect all columns at once, click here.

Working with Data Tables

Ta bl e 2 .3 Selecting and Deselecting Columns

Task Action

Select multiple non-adjacent columns Select the first column, and then hold down the CTRL key

and click the other column headings.

Deselect columns one at a time Hold down the CTRL key and click the column heading.

Deselect all columns Click in the upper-triangular space in the top left corner of

the table. See Figure 2.8.

Figure 2.8 Deselecting Columns

Selecting and Deselecting Cells

Ta bl e 2 .4 Selecting and Deselecting Cells

Task Action

Select cells one at a time Click each cell individually.

Select multiple adjacent cells Click and drag across the cells.

Select the beginning cell, and then hold down the SHIFT key and click the last cell.

Select multiple non-adjacent cells Select the first cell, and then hold down the CTRL key and click

the other cells.

Deselect all cells Click in the upper and lower triangular spaces in the top left

corner of the table.

38 Working with Your Data Chapter 2

Select your target column here

Enter your search criteria here

Working with Data Tables

Searching for Values

In a data table that has thousands or tens of thousands of rows, it can be difficult to locate a particular cell by scrolling through the table. If you are looking for specific information, use the Search feature to find it. If data is found that matches the search criteria, the cell is selected and the data grid scrolls to show it in the window. For example, the

Companies.jmp data table contains information about a company that has total

sales of $11,899. Use the Search feature to find that cell.

Example: Searching for a value

1. Select

2. In the

3. Click

If multiple cells meet the search criteria, click

You can also search for multiple rows at once, with each row matching some criteria.

Example: Select all of the rows that correspond to medium-sized companies

1. Select

2. In the column list box on the left, select

3. In the text box on the right, enter medium.

4. Click

Figure 2.9 Select Rows Window

Edit > Search > Find to launch the Search window.

Find what box, enter 11899.

Find. JMP finds the first cell that has 11,899 in it, and selects it.

Find again to find the next cell that matches the search term.

Rows > Row Selection > Select Where to open the Select rows window.

Size Co.

OK.

Chapter 2 Working with Your Data 39

Working with Data Tables

JMP selects all of the rows that have Size Co equal to medium. There are seven.

Viewing or Changing Column Information

Information about a column is not limited to the data in the column. Data type, modeling type, format, and formulas can also be set.

To view or change column characteristics, double-click on the column heading. Or, right-click on the column heading and select

Figure 2.10 Column Info Window

Column Info. The Column Info window appears.

Ta bl e 2 .5 Column Information

Option Description

Column Name

Enter or change the column name. No two columns can have the same

column name.

Data Type Select one of the following data types:

•

Numeric specifies the column values as numbers.

•

Character specifies the column values as non-numeric, such as letters or

symbols.

•

Row State specifies the column values as row states. This is an advanced

topic. See Using JMP.

40 Working with Your Data Chapter 2

Working with Data Tables

Ta bl e 2 .5 Column Information (Continued)

Option Description

Modeling Type Modeling types define how values are used in analyses. Select one of the

following modeling types:

•

Continuous values are numeric only.

•

Ordinal values are either numeric or character, and are ordered

categories.

•

Nominal values are either numeric or character, but not ordered.

Format Select a format for numeric values. This option is not available for character

data. Here are a few of the most common formats:

•

Best lets JMP choose the best display format.

•

Fixed Dec specifies the number of decimal places that appear.

•

Date specifies the syntax for date values.

•

Time specifies the syntax for time values.

•

Currency specifies the type of currency and decimal points that are used

for currency values.

Column Properties Set special column properties such as formulas, notes, and value orders. See

Using JMP.

Lock Lock a column, so that the values in the column cannot be changed.

Calculating Values With Formulas

Use the Formula Editor to create columns that contain calculated values.

Scenario

The sample data table data was collected for March, June, and August of 1999.

Creating the Formula

Suppose that you want to create a new column containing the average on-time percentage for each airline.

1. Add a new column.

2. Right-click on the column heading of the new column and select appears.

On-Time Arrivals.jmp reflects the percent of on-time arrivals for several airlines. The

Formula. The Formula Editor window

Chapter 2 Working with Your Data 41

Table Columns Keypad

Functions

If the red box is not showing, click here to show it.

Working with Data Tables

Figure 2.11 Formula Editor

Create the formula for the average on-time percentage of each airline:

3. From the Table Columns list, select

March 1999.

4. Click the button on the keypad.

5. Select

6. Select

Figure 2.12 Sum of the Months

June 1999, followed by another sign.

August 1999.

Notice that only August 1999 is selected (has the red box around it).

7. Click on the box surrounding the entire formula.

42 Working with Your Data Chapter 2

Working with Data Tables

Figure 2.13 Entire Formula Selected

8. Click the button.

9. Type a 3 in the denominator box, and then click outside of the formula in any of the white space.

Figure 2.14 Completed Formula

10. Click OK

The new column contains the averages.

The Formula Editor has many built-in arithmetic and statistical functions. For example, another way to calculate the average on-time arrival percentage is to use the

Mean( ) function in the Statistical functions list.

For details about all of the Formula Editor functions, see Using JMP.

Filtering Data

Use the Data Filter to interactively select complex subsets of data, hide these subsets in plots, or exclude them from analyses. For example, look at profit per employee for computer and pharmaceutical companies.

1. Open the

2. Select

3. Select

4. Click

5. From the red triangle menu for profit/emp, select

Companies.jmp sample data table.

Analyze > Distribution.

profit/emp and click Y, Col u m ns .

OK.

Display Options > Horizontal Layout.

Chapter 2 Working with Your Data 43

Working with Data Tables

Figure 2.15 Distribution of profit/emp

6. Turn on Automatic Recalc by selecting Script > Automatic Recalc from the red triangle menu for

Distributions.

When this option is on, every change that you make (for example, hiding or excluding points) causes your report window to automatically update itself.

7. Select

8. Select

9. Clear

Rows > Data Filter.

Ty pe and click Add.

Select and select Include.

10. To filter out the Pharmaceutical companies from the Distribution results, and include only the Computer companies, click the

Computer box in the Data Filter window.

The distribution results update to only include Computer companies.

44 Working with Your Data Chapter 2

Click the Computer box to include only computer companies in the Distribution results.

The graph and the statistics report automatically reflect only the rows that are selected.

Managing Data

Figure 2.16 Filter for Computer Companies

Conversely, to change the Distribution results to include only the Pharmaceutical companies, click the

Pharmaceutical button on the Data Filter window.

Managing Data

The commands on the Tab le s menu summarize and manipulate data tables into the format that you need for graphing and analyzing. This section describes five of the commands:

Summary Creates a table that contains summary statistics that describe your data.

Ta bu l at e Provides a drag and drop workspace to create summary statistics.

Subset Creates a table that contains a subset of your data.

Join Joins the data from two data tables into one new data table.

Sort Sorts your data by one or more columns.

For complete details about these and the other Tables menu commands, see Using JMP.

Chapter 2 Working with Your Data 45

Managing Data

Requesting Summary Statistics

Summary statistics, such as sums and means, can instantly provide useful information about your data. For example, if you look at the annual profit of each company out of thirty-two companies, it’s difficult to compare the profits of small, medium, and large companies. A summary shows that information immediately.

Create summary tables by using either the Summary or Tabulate commands on the Tables menu. The Summary command creates a new data table. As with any data table, you can perform analyses and create graphs from the summary table. The Tabulate command creates a report window with a table of summary data. You can also create a table from the Tabulate report.

Summary

A summary table contains statistics for each level of a grouping variable. For example, look at the financial data for computer and pharmaceutical companies. Suppose that you want to calculate the mean of sales and the mean of profits, for each combination of company type and size.

1. Open the

2. Select

3. Select

4. Select

Figure 2.17 Completed Summary Window

Companies.jmp sample data table.

Tables > Summary.

Ty pe and Size Co and click Group.

Sales ($M) and Profits ($M) and click Statistics > Mean.

46 Working with Your Data Chapter 2

Managing Data

5. Click OK.

JMP calculates the mean of

Size Co.

Figure 2.18 Summary Table

Sales ($M) and the mean of Profit ($M) for each combination of Typ e and

The summary table contains the following:

• There are columns for each grouping variable (in this example,

•The

N Rows column shows the number of rows from the original table that correspond to each

Ty pe and Size Co).

combination of grouping variables. For example, the original data table contains 14 rows corresponding to small computer companies.

• There is a column for each summary statistic requested. In this example, there is a column for the mean of

Sales ($M) and a column for the mean of Profits ($M).

Tabulate

The summary table is linked to the source table. Selecting a row in the summary table also selects the corresponding rows in the source table.

Use the Tabulate command to drag columns into a workspace, creating summary statistics for each combination of grouping variables. This example shows you how to use Tabulate to create the same summary information that you just created using Summary.

1. Open the

2. Select

Companies.jmp sample data table.

Tabl es > Tab ulate.

Chapter 2 Working with Your Data 47

Managing Data

Figure 2.19 Tabulate Workspace

3. Select both Ty pe and Size Co.

4. Drag and drop them into the

Figure 2.20 Dragging Columns to the Row Zone

Drop zone for rows to see a menu.

48 Working with Your Data Chapter 2

Managing Data

5. Select Add Grouping Columns.

The initial tabulation shows the number of rows per group.

Figure 2.21 Initial Tabulation

6. Select both Sales ($M) and Profits ($M), and drag and drop them over the N in the table to see a menu.

Figure 2.22 Adding Sales and Profit

7. Select Add Analysis Columns.

The tabulation now shows the sum of is to change the sums to means.

Figure 2.23 Tabulation of Sums

Sales ($M) and the sum of Profits ($M) per group. The final step

Chapter 2 Working with Your Data 49

Managing Data

8. Right-click on Sum (either of them) and select Statistics > Mean.

The sums are replaced by the means for each group.

Figure 2.24 Final Tabulation

The means are the same as those obtained using the Summary command. Compare Figure 2.24 to Figure 2.18.

Creating Subsets

If you want to look closely at only part of your data table, you can create a subset. For example, suppose that you have already compared the sales and profits of big, medium, and small computer and pharmaceutical companies. Now you want to look at the sales and profits of only the medium-sized companies.

Creating a subset is a two-step process. First select the target data, and then extract the data into a new table.

Subsetting with the Subset Command

1. Open the

Companies.jmp sample data table.

Selecting the rows and columns that you want to subset

2. Select

3. Select

Rows > Row Selection > Select Where.

Size Co in the column list box on the left.

4. Enter medium in the text enter box.

5. Click

6. Select the

OK.

Ty pe , Sales ($M), and Profits ($M) columns.

Creating the subset table

7. Select

Tables > Subset to launch the Subset window.

50 Working with Your Data Chapter 2

Managing Data

Figure 2.25 Subset Window

Your selections are already set up in this window. You could also set your subset selections in this window.

8. Click

OK.

The resulting subset data table has seven rows and three columns. For complete details about the Subset command, see Using JMP.

Subsetting with the Distribution Platform

Another way to create subsets uses the connection between platform results and data tables.

Example: Creating a subset using the Distribution command

1. Open the

2. Select

3. Select

4. Click

Companies.jmp sample data table.

Analyze > Distribution.

Ty pe and click Y, C o l u m ns.

OK.

5. Double-click on the histogram bar that represents Computer to create a subset table of the Computer companies.

Caution: This method creates a linked subset table. This means if you make any changes to the data in the subset table, the corresponding value changes in the source table.

Chapter 2 Working with Your Data 51

Managing Data

Joining Data Tables

Use the Join option to combine information from multiple data tables into a single data table. For example, suppose that you have a data table containing results from an experiment on popcorn yields. In another data table, you have the results of a second experiment on popcorn yields. To compare the two experiments or to analyze the trials using both sets of results, you need to have the data in the same table. Also, the experimental data was not entered into the data tables in the same order. One of the columns has a different name, and the second experiment is incomplete. This means that you cannot copy and paste from one table into another.

Example: Joining two data tables

1. Open the

2. Click on

3. Select

4. In the

Trial 1. jmp and Little.jmp data tables.

Trial 1. jmp to make it the active data table.

Tables > J o i n .

Join ‘Trial1’ With box, select Little.

5. From the Matching Specification menu, select By Matching Columns.

6. In the

7. In the same way, match

Source Columns boxes, select popcorn in both boxes, and then click Match.

batch in both boxes, and then match oil amt for Trial1 and oil for Little.

Your matching columns do not have to have the same name.

8. Select

Include non-matches for both tables.

Since one experiment is partial, you want to include all columns, including any with missing data.

9. To avoid duplicate columns, select the

10. From

11. From

Trial1, select all four columns and click Select.

Little, select only yield and click Select.

Select columns for joined table option.

52 Working with Your Data Chapter 2

Managing Data

Figure 2.26 Completed Join Window

12. Click OK.

Figure 2.27 Joined Table

Chapter 2 Working with Your Data 53

Ascending icon

Descending button

Managing Data

Sorting Tables

Use the Sort command to sort a data table by one or more columns in the data table. For example, look at financial data for computer and pharmaceutical companies. Suppose that you want to sort the data table by

Ty pe , then by Profits ($M). Additionally, you want Profits ($M) to be in descending order within each Ty pe .

1. Open the

2. Select

3. Select

4. Select

Companies.jmp sample data table.

Tabl es > Sort.

Ty pe and click By to assign Ty pe as a sorting variable.

Profits ($M) and click By.

At this point, both variables are set to be sorted in ascending order. See the ascending icon next to the variables in Figure 2.28.

Figure 2.28 Sort Ascending Icon

5. To change Profits ($M) to sort in descending order, select Profits ($M) and click the descending button.

Figure 2.29 Change Profits to Descending

The icon next to Profits ($M) changes to descending.

54 Working with Your Data Chapter 2

Managing Data

6. Select the Replace Table check box.

When selected, the

Replace Table option tells JMP to sort the original data table instead of creating a

new table with the sorted values. This option is not available if there are any open report windows created from the original data table. Sorting a data table with open report windows might change how some of the data is displayed in the report window, especially in graphs.

7. Click

OK.

The data table is now sorted by type alphabetically, and by descending profit totals within type.

Chapter 3

Visualizing Your Data

Graphing Data

Visualizing your data is an important first step. The graphs described in this chapter help you to discover important details about your data. For example, histograms show you the shape and range of your data, and help you find unusual data points.

This chapter presents several of the most common graphs and plots that enable you to visualize and explore data in JMP. This chapter is an introduction to some of JMP’s graphical tools and platforms. Use JMP to visualize the distribution of single variables, or the relationships among multiple variables.

Figure 3.1 Visualizing Data with JMP

Contents

Looking at Single Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57

Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57

Bar Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59

Comparing Multiple Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

Scatterplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Scatterplot Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Side-by-Side Box Plots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Overlay Plots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71

Variability Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Bubble Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .82

Chapter 3 Visualizing Your Data 57

Looking at Single Variables

Single-variable graphs, or univariate graphs, let you look closely at one variable at a time. When you begin to look at your data, it’s important to learn about each variable before looking at how the variables interact with each other. Univariate graphs let you visualize each variable individually.

This section covers two graphs that show the distribution of a single variable:

• “Histograms,” p. 57, for continuous variables

• “Bar Charts,” p. 59, for categorical variables

Use the Distribution platform to create both of these graphs. Distribution produces a graphical description and descriptive statistics for each variable.

Histograms

The histogram is one of the most useful graphical tools for understanding the distribution of a continuous variable. Use a histogram to find the following in your data:

• the average value and variation

•extreme values

Scenario

Figure 3.2 Example of a Histogram

This example uses the

A financial analyst wants to explore the following questions:

• Generally, how much profit does each company earn?

• What is the average profit?

• Are there any companies that earn either extremely high or extremely low profits compared to the other companies?

To answer these questions, use a histogram of

Companies.jmp data table, which contains data on profits for a group of companies.

Profits ($M).

58 Visualizing Your Data Chapter 3

Histogram

Box plot

Looking at Single Variables

Creating the Histogram

1. Open the

2. Select

3. Select

Figure 3.3 Distribution Window for Profits ($M)

Companies.jmp sample data table.

Analyze > Distribution.

Profits ($M) and click Y, Co l u m n s .

4. Click OK.

Figure 3.4 Histogram of Profits ($M)

Interpreting the Histogram

The histogram provides these answers:

• Most companies’ profits are between $-1000 and $1500.

All the bars except for one are located in this range. Additionally, more companies’ profits range from $0 to $500 than any other range. The bar representing that range is much longer than the others.

• The average profit is a little less than $500.

The middle of the diamond in the box plot indicates the mean value. In this case, the mean is slightly lower than the $500 mark.

Chapter 3 Visualizing Your Data 59

Looking at Single Variables

• One company has significantly higher profits than the others, and might be an outlier. An outlier is a data point that is separated from the general pattern of the other data points.

This outlier is represented by a single, very short bar at the top of the histogram. The bar is small and represents a small group (in this case, a single company), and it is widely separated from the rest of the histogram bars.

In addition to the histogram, this report includes the following:

• The box plot, which is another graphical summary of the data. For detailed information about the box plot, see Basic Analysis and Graphing.

•

Quantiles and Moments reports. These reports are discussed in “Analyzing Distributions,” p. 95 in the

“Analyzing Your Data” chapter.

Interacting with the Histogram

Data tables and reports are all connected in JMP. Click on a histogram bar to select the corresponding rows in the data table.

Bar Charts

Use a bar chart to visualize the distribution of a categorical variable. A bar chart looks similar to a histogram, since they both have bars that correspond to the levels of a variable. A bar chart shows a bar for every level of the variable, whereas the histogram shows a range of values for the variable.

Scenario

Figure 3.5 Example of a Bar Chart

This example uses the

Companies.jmp data table, which contains data on the size and type of a group of

companies.

A financial analyst wants to explore the following questions:

• What is the most common type of company?

• What is the most common size for a company?

To answer these questions, use bar charts of

Ty pe and Size Co.

60 Visualizing Your Data Chapter 3

Bar Charts

Summary Information

Looking at Single Variables

Creating the Bar Chart

1. Open the

2. Select

3. Select

4. Click

Figure 3.6 Bar Charts of Type and Size Co

Companies.jmp sample data table.

Analyze > Distribution.

Ty pe and Size Co and click Y, C o l um n s .

OK.

Interpreting the Bar Charts

The bar charts provides these answers:

• There are more computer companies than pharmaceutical companies.

The bar that represents computer companies is larger than the bar that represents pharmaceutical companies.

• The most common company size is small.

The bar that represents small companies is larger than the bars that represent medium and big companies.

The additional summary output gives detailed frequencies. This report is discussed in “Distributions of

Categorical Variables,” p. 98 in the “Analyzing Your Data” chapter.

Chapter 3 Visualizing Your Data 61

Click on this bar to select the corresponding data in the other chart.

Comparing Multiple Variables

Interacting with the Bar Charts

As is the case with histograms, click on individual bars to highlight rows of the data table. If more than one graph is created, clicking on a bar in one bar chart highlights the corresponding bar or bars in the other bar chart.

For example, suppose that you want to see the distribution of company size for the pharmaceutical companies. Click on the Pharmaceutical bar in the highlighted on the

Size Co bar chart. Figure 3.7 shows that although most companies in this data table are

Ty pe bar chart, and the pharmaceutical companies are

small, most of the pharmaceutical companies are medium or big.

Also, the corresponding rows in the data table are selected.

Figure 3.7 Clicking Bars

Comparing Multiple Variables

Use multiple-variable graphs to visualize the relationships and patterns between two or more variables. This section covers the following graphs:

Ta bl e 3 .1 Multiple-Variable Graphs

“Scatterplots,” p. 62 Use scatterplots to compare two continuous variables.

“Scatterplot Matrix,” p. 66 Use scatterplot matrices to compare several pairs of continuous

“Side-by-Side Box Plots,” p. 69 Use side-by-side box plots to compare one continuous and one

variables.

categorical variable.

62 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Ta bl e 3 .1 Multiple-Variable Graphs

“Overlay Plots,” p. 71 Use overlay plots to compare one or more variables on the Y-axis to

another variable on the X-axis. Overlay plots are especially useful if the X variable is a time variable, because you can compare how two or more variables change across time.

“Variability Chart,” p. 75 Use variability charts to compare one or more Y variables to one or

more X variables. Variability charts show differences in means and variability across several variables.

“Graph Builder,” p. 77 Use Graph Builder to create and change graphs interactively.

“Bubble Plots,” p. 82 Bubble plots are specialized scatterplots that use color and bubble

sizes to represent up to five variables at once. If one of your variables is a time variable, you can animate the plot to see your other variables change through time.

Scatterplots

The scatterplot is the simplest of all the multiple-variable graphs. Use scatterplots to determine the relationship between two continuous variables and to discover whether two continuous variables are correlated. Correlation indicates how closely two variables are related. When you have two variables that are highly correlated, one might influence the other. Or, both might be influenced by other variables in a similar way.

Scenario

Figure 3.8 Example of a Scatterplot

This example uses the

Companies.jmp data table, which contains sales figures and the number of employees

of a group of companies.

Chapter 3 Visualizing Your Data 63

Comparing Multiple Variables

A financial analyst wants to explore the following questions:

• What is the relationship between sales and the number of employees?

• Does the amount of sales increase with the number of employees?

• Can you predict average sales from the number of employees?

To answer these questions, use a scatterplot of

Creating the Scatterplot

1. Open the

2. Select

3. Select

4. Select

Figure 3.9 Fit Y by X Window

Companies.jmp sample data table.

Analyze > Fit Y by X.

Sales ($M) and Y, Response.

# Employ and X, Factor.

Sales ($M) versus # Employ.

5. Click OK.

64 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.10 Scatterplot of Sales ($M) versus # Employ

Interpreting the Scatterplot

One company has a large number of employees and high sales, represented by the single point at the top right of the plot. The distance between this data point and all the rest makes it difficult to visualize the relationship between the rest of the companies. Remove the point from the plot and recreate the plot by following these steps:

1. Click on the point to select it.

2. Select

3. Select

Rows > Exclude/Unexclude. The data point is no longer included in calculations.

Rows > Hide/Unhide. The data point is hidden on all graphs.

Note: The difference between hiding and excluding is important. Hiding a point removes it from any

graphs but statistical calculations continue to use the point. Excluding a point removes it from any statistical calculations but does not remove it from graphs. When you both hide and exclude a point, you remove it from all calculations and from all graphs.

4. To recreate the plot without the outlier, select

Script > Redo Analysis from the red triangle menu for

Bivariate. You can close the original report window.

Chapter 3 Visualizing Your Data 65

Comparing Multiple Variables

Figure 3.11 Scatterplot with the Outlier Removed

The updated scatterplot provides these answers:

• There is a relationship between the sales and the number of employees.

The data points have a discernible pattern. They are not scattered randomly throughout the graph. You could draw a diagonal line that would be near most of the data points.

• Sales do increase with the number of employees, and the relationship is linear.

If you drew that diagonal line, it would slope from bottom left to top right. This slope shows that as the number of employees increases (left to right on the bottom axis), sales also increases (bottom to top on the left axis). A straight line would be near most of the data points, indicating a linear relationship. If you would have to curve your line to be near the data points, there would still be a relationship (because of the pattern of the points). However, that relationship would not be linear.

• You can predict average sales from the number of employees.

The scatterplot shows that sales generally increase as the number of employees does. You could predict the sales for a company if you knew only the number of employees of that company. Your prediction would be on that imaginary line. It would not be exact, but it would approximate the real sales.

Interacting with the Scatterplot

As with other JMP graphics, the scatterplot is interactive. Hover over the point in the bottom right corner with the mouse to reveal the row number (in this example, 28).

66 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.12 Hover Over a Point

Click on a point to highlight the corresponding row in the data table. Select multiple points by doing one of the following:

• Click and drag with the mouse around the points. This selects points in a rectangular area.

• Select the lasso tool, and then click and drag around multiple points. The lasso tool selects an irregularly shaped area.

Scatterplot Matrix

A scatterplot matrix is a collection of scatterplots organized into a grid (or matrix). Each scatterplot shows the relationship between a pair of variables.

Figure 3.13 Example of a Scatterplot Matrix

Chapter 3 Visualizing Your Data 67

Comparing Multiple Variables

Scenario

This example uses the

Solubility.jmp data table, which contains data for four chemical compounds that were

measured for solubility in different solvents.

A lab technician wants to explore the following questions:

• Is there a relationship between any pair of chemicals? (There are six possible pairs.)

• Which pair has the strongest relationship?

To answer these questions, use a scatterplot matrix of the four solvents.

Creating the Scatterplot Matrix

1. Open the

2. Select

3. Select

Figure 3.14 Scatterplot Matrix Window

Solubility.jmp sample data table.

Graph > Scatterplot Matrix.

Ether, Chloroform, Benzene, and Hexane, and click Y, C olu m n s .

4. Click OK.

68 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.15 Scatterplot Matrix

Interpreting the Scatterplot Matrix

The scatterplot matrix provides these answers:

• All six pairs of variables are positively correlated.

As one variable increases, the other variable increases too.

• The strongest relationship appears to be between

The data points in the scatterplot for imaginary line.

Interacting with the Scatterplot Matrix

If you select a point in one scatterplot, it is selected in all the other scatterplots.

For example, if you select a point in the in the other five plots.

Benzene and Chloroform.

Benzene and Chloroform are the most tightly clustered along an

Benzene versus Chloroform scatterplot, the same point is selected

Chapter 3 Visualizing Your Data 69

Select this point.

The same point is selected in the other scatterplots.

Comparing Multiple Variables

Figure 3.16 Selected Points

Side-by-Side Box Plots

Side-by-side box plots show the following:

• the relationship between one continuous variable and one categorical variable

• differences in the continuous variable across levels of the categorical variable

Figure 3.17 Example of Side-by-Side Box Plots

70 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Scenario

This example uses the

Analgesics.jmp data table, which contains data on pain measurements taken on

patients using three different drugs.

A researcher wants to explore the following questions:

• Are there differences in the average amount of pain control among the drugs?

•Does the variability in the pain control given by each drug differ? A drug with high variability would not be as reliable as a drug with low variability.

To answer these questions, use a side-by-side box plot for the pain levels and the drug categories.

Creating the Side-by-Side Box Plots

1. Open the

2. Select

3. Select

4. Select

Figure 3.18 Fit Y by X Window

Analgesics.jmp data table.

Analyze > Fit Y by X.

pain and click Y, Response.

drug and click X, Factor.

5. Click OK.

6. From the red triangle menu, select Display Options > Box Plots.

Chapter 3 Visualizing Your Data 71

Box

Whiskers

Comparing Multiple Variables

Figure 3.19 Side-by-Side Box Plots

Interpreting the Side-by-Side Box Plots

Box plots are designed according to the following principles:

• The line through the box represents the median.

• The middle half of the data is within the box.

• The majority of the data falls between the ends of the whiskers.

• A data point outside the whiskers might be an outlier.

The box plots in Figure 3.19 show these answers:

• There is evidence to believe that patients on drug A feel less pain, since the box plot for drug A is lower on the pain scale than the others.

• Drug B appears to have higher variability than Drugs A and C, since the box plot is taller.

There is one point for drug C that is a lot lower than the other points for drug C. Hover over it with your mouse to see that it is row 26 of the data table. That point looks like it is more similar to the data in drug group A or B. The information in row 26 deserves investigation. There might have been a typographical error when the data was recorded.

Overlay Plots

Like scatterplots, overlay plots show the relationship between two or more variables. However, if one of the variables is a time variable, an overlay plot shows trends across time better than scatterplots do.

72 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.20 Example of an Overlay Plot

Note: To plot data over time, you can also use Graph Builder, bubble plots, control charts, and variability charts. For complete details, see Basic Analysis and Graphing or Quality and Reliability Methods.

Scenario

This example uses the

Stock Prices.jmp data table, which contains data on the price of a stock over a three

month period.

A potential investor wants to explore the following questions:

• Has the stock’s closing price changed over the last three months?

To answer this question, use an overlay plot of the stock’s closing price over time.

• How do the stock’s high and low prices relate to each other?

To answer this question, use another overlay plot of the stock’s high and low prices over time.

Create the first overlay plot to answer the first question, and then create a second overlay plot to answer the second question.

Creating the Overlay Plot of the Stock’s Price over Time

1. Open the

2. Select

3. Select

Stock Prices.jmp data table.

Graph > Overlay Plot.

Close and click Y.

4. Select Date and click X.

Chapter 3 Visualizing Your Data 73

Comparing Multiple Variables

Figure 3.21 Overlay Plot Window

5. Click OK.

Figure 3.22 Overlay Plot of the Closing Price Over Time

Interpreting and Interacting with the Overlay Plot

The overlay plot shows that the closing stock price has been decreasing over the last several months. To see the trend more clearly, connect the points and add grid lines.

1. From the red triangle menu, select

Connect Thru Missing.

2. Double-click on the Y axis.

3. Select the

4. Click

Major Gridline check box.

OK.

74 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.23 Connected Points and Grid Lines

The potential investor can see that although the stock price has gone up and down over the last three months, the overall trend has been downward.

Creating the Overlay Plot of the Stock’s High and Low Prices

Use an overlay plot to plot more than one Y variable. For example, suppose that you want to see both the high and the low prices on the same plot.

1. Follow the steps in “Creating the Overlay Plot of the Stock’s Price over Time,” p. 72, this time assigning both

High and Low to the Y role.

2. Connect the points and add grid lines as shown in “Interpreting and Interacting with the Overlay Plot,”

p. 73.

Figure 3.24 Two Y Va r i abl e s

The legend at the bottom of the plot shows the colors and markers used for the High and Low variables in the graph. The overlay plot shows that the

High price and Low price track each other very closely.

Chapter 3 Visualizing Your Data 75

Comparing Multiple Variables

Answering the Questions

Both of the overlay plots answer the two questions asked at the beginning of this example.

• The first plot shows that the price of this stock has not remained the same, but has been decreasing.

• The second plot shows that the high and low price of this stock are not very different from each other. The stock price does not vary wildly on any given day.

Variability Chart

In the graphs described so far, you specified only a single X variable. Use a variability chart to specify multiple X variables and see differences in means and variability across all of your variables at once.

Figure 3.25 Example of a Variability Chart

Scenario

This example uses the

Popcorn.jmp data table with data from a popcorn maker. The yield (the volume of

popcorn for a given measure of kernels) was measured for each combination of popcorn style, batch size, and amount of oil used.

The popcorn maker wants to explore the following question:

• Which combination of factors results in the highest popcorn yield?

To answer this question, use a variability chart of the yield versus the style, batch size, and oil amount.

Creating the Variability Chart

1. Open the

Popcorn.jmp data table.

2. Select Graph > Variability/Gauge Chart.

3. Select

yield and click Y, Response.

76 Visualizing Your Data Chapter 3

Comparing Multiple Variables

4. Select popcorn and click X, Grouping.

5. Select

6. Select

batch and click X, Grouping.

oil amt and click X, Grouping.

Note: The order in which you assign the variables to the X, Grouping role is important, because the order in this window determines their nesting order in the variability chart.

Figure 3.26 Variability Chart Window

7. Click OK.

The top chart is the variability chart, showing the yield broken down by each combination of the three variables. The bottom chart shows the standard deviation for each combination of the three variables. Since the bottom chart does not show the yield, hide it.

8. Deselect

Std Dev Chart on the red triangle menu.

Chapter 3 Visualizing Your Data 77

X axis with three variables

Comparing Multiple Variables

Figure 3.27 Results Window

Interpreting the Variability Chart

The variability chart for yield indicates that small, gourmet batches produce the highest yield.

To be more specific, the popcorn maker might ask this additional question: Is the yield high because those batches are small, or because those batches are gourmet?

The variability chart shows the following:

• The yield from small, plain batches is low.

• The yield from large, gourmet batches is low.

Given this information, the popcorn maker can conclude that only the combination of small and gourmet at the same time results in batches with high yield. It would have been impossible to reach this conclusion with a chart that only allowed a single variable.

Graph Builder

Use Graph Builder to interactively create and modify graphs. So far, all of the graphs have been created by launching a platform and specifying variables. Once the graph is created, you cannot change the variables. To create a different type of graph, you must launch a different platform. In Graph Builder, you can change the variables and change the graphs at any time.

Use Graph Builder to accomplish the following tasks:

• Change variables by dragging and dropping them in and out of the graph.

• Create a different type of graph with a few mouse clicks.

• Partition the graph horizontally or vertically.

78 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.28 Example of a Graph that was Created with Graph Builder

Scenario

Note: Only some of the Graph Builder features are covered here. For complete details, see Basic Analysis and Graphing.

This example uses the

Profit by Product.jmp data table, which contains profit data for multiple product

lines.

A business analyst wants to explore the following question:

• How is the profitability different between product lines?

To answer this question, use a line plot that displays revenue, product cost, and profit data across different product lines.

Chapter 3 Visualizing Your Data 79

Comparing Multiple Variables

Creating the Graph

1. Open the

2. Select

Figure 3.29 Graph Builder Workspace

Profit by Product.jmp data table.

Graph > Graph Builder.

3. Click on Quarter and drag and drop it into the X zone to assign Quarter as the X variable.

4. Click on

Revenue, Product Cost, and Profit, and drag and drop them into the Y zone to assign all three

variables as Y variables.

Note: The Y Zone is now the Y Axis.

5. Drag and drop the variables into the

Y zone to assign all three variables as Y variables.

You can also select variables and click the zones to assign them.

80 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.30 After Adding Y and X Variables

Based on the variables that you are using, Graph Builder shows side-by-side box plots.

6. To change the box plots to a line plot, right-click on the plot and select

Box Plot > Change To > Line.

Figure 3.31 Line Plot

7. To create a separate chart for each product, click on Product Line, and drag and drop it into the Wrap zone.

A separate line plot is created for each product.

Chapter 3 Visualizing Your Data 81

Comparing Multiple Variables

Figure 3.32 Final Line Plots

Interpreting the Graph

Figure 3.32 shows revenue, cost, and profit broken down by product line. The business analyst was interested in seeing the difference in profitability between product lines. The line plots in Figure 3.32 can provide some answers, as follows:

• Credit products, deposit products, and revolving credit products produce more revenue than fee-based products, third-party products, and other products.

• However, the profits of all the product lines are similar.

The data table also includes data on sales channels. The business analyst wants to see how revenue, product cost, and profit differ between different sales channels.

1. To remove

Product Line from the graph, click the title of the graph (Product Line) and drag and drop it

into any empty area within Graph Builder.

2. To add

Channel as the wrap variable, click on Channel and drag and drop it into the Wrap zone.

82 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Figure 3.33 Line Plots Showing Sales Channels

Figure 3.33 provides this answer: revenue and profit for ATMs are the highest and are growing the most quickly.

Bubble Plots

A bubble plot is a scatterplot that represents its points as bubbles (or circles). You can change the size and color of the bubbles, and even animate them over time. With the ability to represent up to five dimensions (x position, y position, size, color, and time), a bubble plot can produce dramatic visualizations and make data exploration easy.

Chapter 3 Visualizing Your Data 83

Comparing Multiple Variables

Figure 3.34 Example of a Bubble Plot

Scenario

This example uses the or territories between the years 1950 to 2004. Total population numbers are broken out by age group, and not every country has data for every year.

A sociologist wants to explore the following question:

• Is the age of the population of the world changing?

To answer this question, look at the relationship between the oldest (over 59) and the youngest (younger than 20) portions of the population. Use a bubble plot to determine how this relationship changes over time.

Creating the Bubble Plot

1. Open the

2. Select

PopAgeGroup.jmp data table.

Graph > Bubble Plot.

3. Select Portion60+ and click Y.

This corresponds to the Y variable on the bubble plot.

4. Select

Portion 0-19 and click X.

This corresponds to the X variable on the bubble plot.

5. Select

Country and click ID.

Each unique level of the ID variable is represented by a bubble on the plot.

PopAgeGroup.jmp data table, which contains population statistics for 116 countries

84 Visualizing Your Data Chapter 3

Comparing Multiple Variables

6. Select Ye ar and click Time.

This controls the time indexing when the bubble plot is animated.

7. Select

Pop and click Sizes.

This controls the size of the bubbles.

8. Select

Region and click Coloring.

Each unique level of the Coloring variable is reflected by a bubble of that color. So in this example, all the bubbles for countries located in the same region are the same color.

Figure 3.35 Bubble Plot Window

9. Click OK.

Chapter 3 Visualizing Your Data 85

Comparing Multiple Variables

Figure 3.36 Initial Bubble Plot

Interpreting the Bubble Plot

Because the time variable (in this case, year) starts in 1950, the initial bubble plot shows the data for 1950. Animate the bubble plot to cycle through all the years by clicking data for that year. The data for each year determines the following:

•The X and Y coordinates

• The bubble’s sizes

• The bubble’s coloring

• Bubble aggregation

Note: For detailed information about how the bubble plot aggregates information across multiple rows, see Basic Analysis and Graphing.

The bubble plot for 1950 shows that if a country’s proportion of people younger than 20 is high, then the proportion of people over 59 is low.

Click

Go to animate the bubble plot through the range of years. As time progresses, the Portion 0-19

decreases and the

Stop stops the animation.

Step manually controls the animation forward one unit of time.

Prev manually controls the animation back one unit of time.

Year is used to change the time index manually.

Portion60+ increases.

Go. Each successive bubble plot shows the

86 Visualizing Your Data Chapter 3

Comparing Multiple Variables

Speed

Circle Size controls the absolute sizes of the bubbles, while maintaining the relative sizes.

controls the speed of the animation.

The sociologist wanted to know how the age of the world’s population is changing. The bubble plot indicates that the population of the world is getting older.

Interacting with the Bubble Plot

Click to select a bubble to see the trend for that bubble over time. For example, in the 1950 plot, the large bubble in the middle is Japan.

To see the pattern of population changes in Japan through the years

1. Click in the middle of the Japan bubble to select it.

2. From the red triangle menu, select

3. Click

Go.

As the animation progresses through time, the Japan bubble leaves a trail of bubbles that illustrates its history.

Figure 3.37 Japan’s History of Population Shifts

Trail Bubbles.

Focusing on the Japan bubble, you can see the following over time:

• The proportion of the population 19 years old or less decreased.

• The proportion of the population 60 years old or more increased.

Chapter 4

Analyzing Your Data

Distributions, Relationships, and Models

Analyzing your data helps you make informed decisions. Data analysis often involves these actions:

• Examining distributions

• Discovering relationships

• Hypothesis testing

• Building models

Figure 4.1 Analysis Examples

Contents

About This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

The Importance of Graphing Your Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Understanding Modeling Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Example: Modeling Type Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Changing the Modeling Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Analyzing Distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95

Distributions of Continuous Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Distributions of Categorical Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Analyzing Relationships. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Using Regression with One Predictor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Comparing Averages for One Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .106

Comparing Proportions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .108

Comparing Averages for Multiple Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Using Regression with Multiple Predictors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Chapter 4 Analyzing Your Data 89

About This Chapter

Before you analyze your data, review the following information:

• “The Importance of Graphing Your Data,” p. 89

• “Understanding Modeling Types,” p. 92

The rest of this chapter shows you how to use some basic analytical methods in JMP:

• “Analyzing Distributions,” p. 95

• “Analyzing Relationships,” p. 101

For a description of advanced modeling and analysis techniques, see Modeling and Multivariate Methods and Quality and Reliability Methods.

The Importance of Graphing Your Data

Graphing, or visualizing, your data is important to any data analysis, and should always occur before the use of statistical tests or model building. To illustrate why data visualization should be an early step in your data analysis process, consider the following example:

1. Open the data consists of four pairs of X and Y variables.

2. From the red triangle menu for the

The script creates a simple linear regression on each pair of variables using option is turned off, so that none of the data can be seen on the scatterplots. Figure 4.2 shows the model fit and other summary information for each regression.

Anscombe.jmp data table (F. J. Anscombe (1973), American Statistician, 27, 17-21). This

The Quartet script in the table panel, select Run Script.

Fit Y by X. The Show Points

90 Analyzing Your Data Chapter 4

Model for pair 1

Model for pair 2

Model for pair 3

Model for pair 4

The Importance of Graphing Your Data

Figure 4.2 Four Models

Notice that all four models and the RSquare values are nearly identical. The fitted model in each case is essentially Y = 3 + 0.5X, and the RSquare value in each case is essentially 0.66. If your data analysis took into account only the above summary information, you would likely conclude that the relationship between X and Y is the same in each case. However, at this point, you have not visualized your data. Your conclusion might be wrong.

To visualize the data, add the points to all four scatterplots

1. Hold down the CTRL key.

2. From the red triangle menu for any one of the Bivariate Fits, select

Show Points.

Sas DISCOVERING JMP RELEASE 9 User Manual

Specifications and Main Features

Frequently Asked Questions

User Manual

Introducing JMP

Concepts You Should Know

How Do I Get Started?

Starting JMP

Understanding Data Tables

Using Sample Data

Understanding the JMP Workflow

Step 1: Launching a Platform and Viewing Results

Step 2: Removing the Box Plot

Step 3: Requesting Additional Output

Step 4: Interacting with Platform Results

How is JMP Different from Excel?

Formulas

Columns Names

How Do I Find Help?

Tables and Worksheets

The Data Grid

Analysis and Graph Reports

Working with Your Data

Getting Your Data Into JMP

Copying and Pasting Data

Importing Data

Entering Data

Scenario

Entering Data in a New Data Table

Working with Data Tables

Editing Data

Changing Values

Recoding Values

Creating Patterned Data

Selecting, Deselecting, and Finding Values

Selecting and Deselecting Rows

Selecting and Deselecting Columns

Selecting and Deselecting Cells

Searching for Values

Viewing or Changing Column Information

Calculating Values With Formulas

Scenario

Creating the Formula

Filtering Data

Managing Data

Requesting Summary Statistics

Summary

Tabulate

Creating Subsets

Subsetting with the Subset Command

Subsetting with the Distribution Platform

Joining Data Tables

Sorting Tables

Visualizing Your Data

Looking at Single Variables

Histograms

Scenario

Creating the Histogram

Interpreting the Histogram

Interacting with the Histogram

Bar Charts

Scenario

Creating the Bar Chart

Interpreting the Bar Charts

Comparing Multiple Variables

Interacting with the Bar Charts

Scatterplots

Scenario

Creating the Scatterplot

Interpreting the Scatterplot

Interacting with the Scatterplot

Scatterplot Matrix

Scenario

Creating the Scatterplot Matrix

Interpreting the Scatterplot Matrix

Interacting with the Scatterplot Matrix

Side-by-Side Box Plots

Scenario

Creating the Side-by-Side Box Plots

Interpreting the Side-by-Side Box Plots