## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

No credit card required

## Book Description

This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises. Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including: The redesigned Formula Editor. New and improved ways to create formulas in JMP directly from the data table or dialogs. Interface updates, including improved menu layout. Updates and enhancements in many analysis platforms. New ways to get data into JMP and to save and share JMP results. Many new features that make it easier to use JMP.

1. Preface
2. 1 Preliminaries
1. What You Need to Know
3. Chapter Organization
4. Typographical Conventions
3. 2 Getting Started with JMP
1. Hello!
2. First Session
3. Customize JMP
4. Modeling Type
5. The Personality of JMP
4. 3 Data Tables, Reports, and Scripts
1. Overview
2. The Ins and Outs of a JMP Data Table
3. Creating a New JMP Table
4. Importing Data
5. Moving Data Out of JMP
6. Saving Graphs and Reports
7. Juggling Data Tables
8. Creating Summary Statistics
9. Working with Scripts
5. 4 Formula Editor
1. Overview
2. The Formula Editor Window
3. Using Popular Formula Functions
4. Working with Dates
5. Tips on Building Formulas
6. Exercises
6. 5 What Are Statistics?
1. Overview
2. Ponderings
3. Preparations
4. Statistical Terms
7. 6 Simulations
1. Overview
2. Rolling Dice
3. Probability of Making a Triangle
4. Confidence Intervals
5. Data Table-Based Simulations
6. Other JMP Simulators
7. Exercises
8. 7 Univariate Distributions: One Variable, One Sample
1. Overview
2. Looking at Distributions
3. Describing Distributions of Values
4. Statistical Inference on the Mean
5. Practical Significance versus Statistical Significance
6. Examining for Normality
7. Special Topic: Practical Difference
8. Special Topic: Simulating the Central Limit Theorem
9. Seeing Kernel Density Estimates
10. Exercises
9. 8 The Difference Between Two Means
1. Overview
2. Two Independent Groups
3. Normality and Normal Quantile Plots
4. Testing Means for Matched Pairs
5. Two Extremes of Neglecting the Pairing Situation: A Dramatization
6. A Nonparametric Approach
7. Exercises
10. 9 Comparing Many Means: One-Way Analysis of Variance
1. Overview
2. What Is a One-Way Layout?
3. Comparing and Testing Means
5. Are the Variances Equal across the Groups?
6. Testing Means with Unequal Variances
7. Nonparametric Methods
8. Exercises
11. 10 Fitting Curves through Points: Regression
1. Overview
2. Regression
3. Polynomial Models
4. Transformed Fits
5. Are Graphics Important?
6. Why It’s Called Regression
7. What Happens When X and Y Are Switched?
8. Curiosities
9. Exercises
12. 11 Categorical Distributions
1. Overview
2. Categorical Situations
3. Categorical Responses and Count Data: Two Outlooks
4. A Simulated Categorical Response
5. The X2 Pearson Chi-Square Test Statistic
6. The G2 Likelihood-Ratio Chi-Square Test Statistic
7. Univariate Categorical Chi-Square Tests
8. Exercises
13. 12 Categorical Models
1. Overview
2. Fitting Categorical Responses to Categorical Factors: Contingency Tables
3. Two-Way Tables: Entering Count Data
4. If You Have a Perfect Fit
5. Special Topic: Correspondence Analysis— Looking at Data with Many Levels
6. Continuous Factors with Categorical Responses: Logistic Regression
7. Surprise: Simpson’s Paradox: Aggregate Data versus Grouped Data
8. Generalized Linear Models
9. Exercises
14. 13 Multiple Regression
1. Overview
2. Parts of a Regression Model
3. Regression Definitions
4. A Multiple Regression Example
5. Collinearity
6. Mining Data with Stepwise Regression
7. Exercises
15. 14 Fitting Linear Models
1. Overview
2. The General Linear Model
3. Two-Way Analysis of Variance and Interactions
4. Optional Topic: Random Effects and Nested Effects
5. Exercises
16. 15 Design of Experiments
1. Overview
2. Introduction
3. A Simple Design
4. An Interaction Model: The Reactor Data
5. Some Routine Screening Examples
6. Response Surface Designs
7. Split-Plot Designs
8. Design Strategies
9. DOE Glossary of Key Terms
10. Exercises
17. 16 Bivariate and Multivariate Relationships
1. Overview
2. Bivariate Distributions
3. Density Estimation
4. Correlations and the Bivariate Normal
5. Outliers in Three and More Dimensions
6. Identify Variation with Principal Components Analysis
7. Discriminant Analysis
8. Cluster Analysis
9. Some Final Thoughts
10. Exercises
18. 17 Exploratory Modeling
1. Overview
2. Recursive Partitioning (Decision Trees)
3. Neural Nets
4. Exercises
19. 18 Control Charts and Capability
1. Overview
2. What Does a Control Chart Look Like
3. Types of Control Charts
4. Control Chart Basics
5. Control Charts for Variables Data
6. Variables Charts Using Control Chart Builder
7. Control Charts for Attributes Data
8. Specialty Charts
9. Capability Analysis
10. A Few Words about Measurement Systems
11. Exercises
20. 19 Mechanics of Statistics
1. Overview
2. Springs for Continuous Responses
3. Mechanics of Fit for Categorical Responses
21. A Answers to Selected Exercises
22. B References and Data Sources