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JMP Start Statistics: A Guide to Statistics and Data Analysis Using JMP, Fourth Edition, is a complete and orderly introduction to analyzing data using JMP statistical discovery software from SAS. A mix of software manual and statistics text, this book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use JMP's intuitive interface for data analysis. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises. Written by John Sall, Lee Creighton, and Ann Lehman, this book is a great tool for statistics students or practitioners needing a software-related statistics review.

## Table of Contents

1. Copyright
2. Preface
3. 1. Preliminaries
1. 1.1. What You Need to Know
2. 1.2. Learning About JMP
3. 1.3. Chapter Organization
4. 1.4. Typographical Conventions
4. 2. JMP Right In
1. 2.1. Hello!
2. 2.2. First Session
1. 2.2.1. Open a JMP Data Table
2. 2.2.2. Launch an Analysis Platform
3. 2.2.3. Interact with the Surface of the Report
4. 2.2.4. Special Tools
3. 2.3. Modeling Type
1. 2.3.1. Analyze and Graph
2. 2.3.2. The Analyze Menu
3. 2.3.3. The Graph Menu
4. 2.3.4. Navigating Platforms and Building Context
5. 2.3.5. Contexts for a Histogram
6. 2.3.6. Contexts for the t-Test
7. 2.3.7. Contexts for a Scatterplot
8. 2.3.8. Contexts for Nonparametric Statistics
4. 2.4. The Personality of JMP
5. 3. Data Tables, Reports, and Scripts
1. 3.1. Overview
2. 3.2. The Ins and Outs of a JMP Data Table
1. 3.2.1. Selecting and Deselecting Rows and Columns
2. 3.2.2. Mousing Around a Spreadsheet: Cursor Forms
3. 3.3. Creating a New JMP Table
1. 3.3.1. Define Rows and Columns
2. 3.3.2. Enter Data
3. 3.3.3. The New Column Command
4. 3.3.4. Plot the Data
5. 3.3.5. Importing Data
6. 3.3.6. Importing Text Files
7. 3.3.7. Importing Microsoft Excel Files
8. 3.3.8. Using ODBC
9. 3.3.9. Opening Other File Types
10. 3.3.10. Copy, Paste, and Drag Data
4. 3.4. Moving Data Out of JMP
5. 3.5. Working with Graphs and Reports
1. 3.5.1. Copy and Paste
2. 3.5.2. Drag Report Elements
3. 3.5.3. Context Menu Commands
6. 3.6. Juggling Data Tables
7. 3.7. The Summary Command
8. 3.8. Working with Scripts
6. 4. Formula Editor Adventures
1. 4.1. Overview
2. 4.2. The Formula Editor Window
3. 4.3. A Quick Example
4. 4.4. Formula Editor: Pieces and Parts
5. 4.5. The Keypad Functions
1. 4.5.1. Section21
6. 4.6. The Formula Display Area
7. 4.7. Function Browser Definitions
1. 4.7.1. Row Function Examples
2. 4.7.2. Conditional Expressions and Comparison Operators
3. 4.7.3. Summarize Down Columns or Across Rows
4. 4.7.4. Random Number Functions
8. 4.8. Tips on Building Formulas
9. 4.9. Exercises
7. 5. What Are Statistics?
1. 5.1. Overview
2. 5.2. Ponderings
1. 5.2.1. The Business of Statistics
2. 5.2.2. The Yin and Yang of Statistics
3. 5.2.3. The Faces of Statistics
4. 5.2.4. Don't Panic
3. 5.3. Preparations
1. 5.3.1. Three Levels of Uncertainty
2. 5.3.2. Probability and Randomness
3. 5.3.3. Assumptions
4. 5.3.4. Data Mining?
4. 5.4. Statistical Terms
8. 6. Simulations
1. 6.1. Overview
2. 6.2. Rolling Dice
3. 6.3. Probability of Making a Triangle
4. 6.4. Confidence Intervals
9. 7. Univariate Distributions: One Variable, One Sample
1. 7.1. Overview
2. 7.2. Looking at Distributions
3. 7.3. Describing Distributions of Values
4. 7.4. Statistical Inference on the Mean
5. 7.5. Practical Significance vs. Statistical Significance
6. 7.6. Examining for Normality
7. 7.7. Special Topic: Practical Difference
8. 7.8. Special Topic: Simulating the Central Limit Theorem
9. 7.9. Seeing Kernel Density Estimates
10. 7.10. Exercises
10. 8. The Difference between Two Means
1. 8.1. Overview
2. 8.2. Two Independent Groups
3. 8.3. Normality and Normal Quantile Plots
4. 8.4. Testing Means for Matched Pairs
5. 8.5. The Normality Assumption
6. 8.6. Two Extremes of Neglecting the Pairing Situation: A Dramatization
7. 8.7. A Nonparametric Approach
8. 8.8. Exercises
11. 9. Comparing Many Means: One-Way Analysis of Variance
1. 9.1. overview
2. 9.2. What Is a One-Way Layout?
3. 9.3. Comparing and Testing Means
4. 9.4. Means Diamonds: A Graphical Description of Group Means
5. 9.5. Statistical Tests to Compare Means
6. 9.6. Means Comparisons for Balanced Data
7. 9.7. Means Comparisons for Unbalanced Data
8. 9.8. Adjusting for Multiple Comparisons
9. 9.9. Are the Variances Equal Across the Groups?
10. 9.10. Nonparametric Methods
11. 9.11. Exercises
12. 10. Fitting Curves through Points: Regression
1. 10.1. Overview
2. 10.2. Regression
1. 10.2.1. Least Squares
2. 10.2.2. Seeing Least Squares
3. 10.2.3. Fitting a Line and Testing the Slope
4. 10.2.4. Testing the Slope by Comparing Models
5. 10.2.5. The Distribution of the Parameter Estimates
6. 10.2.6. Confidence Intervals on the Estimates
7. 10.2.7. Examine Residuals
8. 10.2.8. Exclusion of Rows
9. 10.2.9. Time to Clean Up
3. 10.3. Polynomial Models
4. 10.4. Transformed Fits
5. 10.5. Are Graphics Important?
6. 10.6. Why It's Called Regression
7. 10.7. What Happens When X and Y Are Switched?
8. 10.8. Curiosities
9. 10.9. Exercises
13. 11. Categorical Distributions
1. 11.1. Overview
2. 11.2. Categorical Situations
3. 11.3. Categorical Responses and Count Data: Two Outlooks
4. 11.4. A Simulated Categorical Response
5. 11.5. The X2 Pearson Chi-Square Test Statistic
6. 11.6. The G2 Likelihood-Ratio Chi-Square Test Statistic
7. 11.7. Univariate Categorical Chi-Square Tests
8. 11.8. Exercises
14. 12. Categorical Models
1. 12.1. Overview
2. 12.2. Fitting Categorical Responses to Categorical Factors: Contingency Tables
1. 12.2.1. Testing with G2 and X2
2. 12.2.2. Looking at Survey Data
3. 12.2.3. Car Brand by Marital Status
4. 12.2.4. Car Brand by Size of Vehicle
3. 12.3. Two-Way Tables: Entering Count Data
4. 12.4. If You Have a Perfect Fit
5. 12.5. Special Topic: Correspondence Analysis— Looking at Data with Many Levels
6. 12.6. Continuous Factors with Categorical Responses: Logistic Regression
7. 12.7. Surprise: Simpson's Paradox: Aggregate Data versus Grouped Data
8. 12.8. Generalized Linear Models
9. 12.9. Exercises
15. 13. Multiple Regression
1. 13.1. Overview
2. 13.2. Parts of a Regression Model
3. 13.3. A Multiple Regression Example
4. 13.4. Collinearity
5. 13.5. The Longley Data: An Example of Collinearity
6. 13.6. The Case of the Hidden Leverage Point
7. 13.7. Mining Data with Stepwise Regression
8. 13.8. Exercises
16. 14. Fitting Linear Models
1. 14.1. Overview
2. 14.2. The General Linear Model
1. 14.2.1. Kinds of Effects in Linear Models
2. 14.2.2. Coding Scheme to Fit a One-Way anova as a Linear Model
3. 14.2.3. Regressor Construction
4. 14.2.4. Interpretation of Parameters
5. 14.2.5. Predictions Are the Means
6. 14.2.6. Parameters and Means
7. 14.2.7. Analysis of Covariance: Putting Continuous and Classification Terms into the Same Model
8. 14.2.8. The Prediction Equation
9. 14.2.9. The Whole-Model Test and Leverage Plot
10. 14.2.10. Effect Tests and Leverage Plots
11. 14.2.11. Least Squares Means
12. 14.2.12. Lack of Fit
13. 14.2.13. Separate Slopes: When the Covariate Interacts with the Classification Effect
3. 14.3. Two-Way Analysis of Variance and Interactions
4. 14.4. Optional Topic: Random Effects and Nested Effects
5. 14.5. Exercises
17. 15. Bivariate and Multivariate Relationships
1. 15.1. Overview
2. 15.2. Bivariate Distributions
3. 15.3. Density Estimation
4. 15.4. Correlations and the Bivariate Normal
5. 15.5. Three and More Dimensions
6. 15.6. Summary
7. 15.7. Exercises
18. 16. Design of Experiments
1. 16.1. Overview
2. 16.2. Introduction
3. 16.3. JMP DOE
4. 16.4. A Simple Design
1. 16.4.1. The Experiment
2. 16.4.2. The Response
3. 16.4.3. The Factors
4. 16.4.4. The Budget
5. 16.4.5. Enter and Name the Factors
6. 16.4.6. Define the Model
7. 16.4.7. Is the Design Balanced?
8. 16.4.8. Perform Experiment and Enter Data
9. 16.4.9. Analyze the Model
10. 16.4.10. Details of the Design
11. 16.4.11. Using the Custom Designer
12. 16.4.12. Using the Screening Platform
5. 16.5. Screening for Interactions: The Reactor Data
6. 16.6. Response Surface Designs
1. 16.6.1. The Experiment
2. 16.6.2. Response Surface Designs in JMP
3. 16.6.3. Plotting Surface Effects
4. 16.6.4. Designating RSM Designs Manually
5. 16.6.5. The Prediction Variance Profiler
7. 16.7. Design Issues
8. 16.8. Routine Screening Examples
1. 16.8.1. Main Effects Only
9. 16.9. Design Strategies Glossary
19. 17. Exploratory Modeling
1. 17.1. Overview
2. 17.2. The Partition Platform
3. 17.3. Neural Networks
4. 17.4. Exercises
20. 18. Discriminant and Cluster Analysis
1. 18.1. Overview
2. 18.2. Discriminant Analysis
3. 18.3. Cluster Analysis
4. 18.4. Exercises
21. 19. Statistical Quality Control
1. 19.1. Overview
2. 19.2. Control Charts and Shewhart Charts
3. 19.3. The Control Chart Launch Dialog
1. 19.3.1. Process Information
2. 19.3.2. Chart Type Information
3. 19.3.3. Limits Specification Panel
4. 19.3.4. Using Known Statistics
5. 19.3.5. Types of Control Charts for Variables
6. 19.3.6. Types of Control Charts for Attributes
7. 19.3.7. Moving Average Charts
8. 19.3.8. Levey-Jennings Plots
9. 19.3.9. Tailoring the Horizontal Axis
10. 19.3.10. Tests for Special Causes
11. 19.3.11. Westgard Rules
4. 19.4. Multivariate Control Charts
22. 20. Time Series
1. 20.1. Overview
2. 20.2. Introduction
3. 20.3. Lagged Values
4. 20.4. White Noise
5. 20.5. Autoregressive Processes
6. 20.6. Estimating the Parameters of an Autoregressive Process
7. 20.7. Moving Average Processes
8. 20.8. Example of Diagnosing a Time Series
9. 20.9. ARMA Models and the Model Comparison Table
10. 20.10. Stationarity and Differencing
11. 20.11. Seasonal Models
12. 20.12. Spectral Density
13. 20.13. Forecasting
14. 20.14. Exercises
23. 21. Machines of Fit
1. 21.1. Overview
2. 21.2. Springs for Continuous Responses
3. 21.3. Machine of Fit for Categorical Responses
24. References and Data Sources
25. Answers to Selected Exercises
26. Technology License Notices