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JMP Start Statistics, 5th Edition

Book Description

Updated for JMP 10, the 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. Features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises.

Table of Contents

  1. Preface
    1. The Software
    2. JMP Start Statistics, Fifth Edition
    3. SAS
    4. This Book
  2. Preliminaries
    1. What You Need to Know
      1. …about your computer
      2. …about statistics
    2. Learning About JMP
      1. …on your own with JMP Help
      2. …hands-on examples
      3. …using Tutorials
      4. …reading about JMP
    3. Chapter Organization
    4. Typographical Conventions
  3. JMP Right In
    1. Hello!
    2. First Session
      1. Tip of the Day
      2. The JMP Starter (Macintosh)
      3. The JMP Home Window (Windows)
      4. Open a JMP Data Table
      5. Launch an Analysis Platform
      6. Interact with the Report Surface
      7. Special Tools
    3. Customize JMP
    4. Modeling Type
      1. Analyze and Graph
      2. Navigating Platforms and Building Context
      3. Contexts for a Histogram
      4. Contexts for the t-Test
      5. Contexts for a Scatterplot
      6. Contexts for Nonparametric Statistics
    5. The Personality of JMP
  4. Data Tables, Reports, and Scripts
    1. Overview
    2. The Ins and Outs of a JMP Data Table
      1. Selecting and Deselecting Rows and Columns
      2. Mousing Around a Spreadsheet: Cursor Forms
    3. Creating a New JMP Table
      1. Define Rows and Columns
      2. Enter Data
      3. The New Column Command
      4. Plot the Data
    4. Importing Data
      1. Importing Text Files
      2. Importing Other File Types
      3. Copy, Paste, and Drag Data
    5. Moving Data Out of JMP
    6. Working with Graphs and Reports
      1. Copy and Paste
      2. Drag Report Elements
      3. Context Menu Commands
    7. Juggling Data Tables
      1. Data Management
      2. Give New Shape to a Table: Stack Columns
    8. Creating Summary Statistics
      1. Create Summary Statistics with the Summary Command
      2. Create Summary Statistics with Tabulate
    9. Working with Scripts
  5. Formula Editor Adventures
    1. Overview
    2. The Formula Editor Window
      1. The Formula Editor and the JMP Scripting Language
      2. A Quick Example
    3. Formula Editor: Pieces and Parts
      1. Terminology
      2. The Formula Editor Control Panel
      3. The Keypad Functions
      4. The Formula Display Area
    4. Function Definitions
      1. Row Function Examples
      2. Conditional Expressions and Comparison Operators
      3. Summarize Down Columns or Across Rows
      4. Random Number Functions
      5. Local Variables and Table Variables
    5. Some Nice Examples Involving Dates
    6. Tips on Building Formulas
      1. Examining Expression Values
      2. Cutting, Dragging, and Pasting Formulas
      3. Selecting Expressions
      4. Tips on Editing a Formula
    7. Exercises
  6. What Are Statistics?
    1. Overview
    2. Ponderings
      1. The Business of Statistics
      2. The Yin and Yang of Statistics
      3. The Faces of Statistics
      4. Don’t Panic
    3. Preparations
      1. Three Levels of Uncertainty
      2. Probability and Randomness
      3. Assumptions
      4. Data Mining?
    4. Statistical Terms
  7. Simulations
    1. Overview
    2. Rolling Dice
      1. Rolling Several Dice
      2. Flipping Coins, Sampling Candy, or Drawing Marbles
    3. Probability of Making a Triangle
    4. Confidence Intervals
    5. Other JMP Simulations
    6. Exercises
  8. Univariate Distributions: One Variable, One Sample
    1. Overview
    2. Looking at Distributions
      1. Probability Distributions
      2. True Distribution Function or Real-World Sample Distribution
      3. The Normal Distribution
    3. Describing Distributions of Values
      1. Generating Random Data
      2. Histograms
      3. Stem-and-Leaf Plots
      4. Outlier and Quantile Box Plots
      5. Mean and Standard Deviation
      6. Median and Other Quantiles
      7. Mean versus Median
      8. Other Summary Statistics: Skewness and Kurtosis
      9. Extremes, Tail Detail
    4. Statistical Inference on the Mean
      1. Standard Error of the Mean
      2. Confidence Intervals for the Mean
      3. Testing Hypotheses: Terminology
      4. The Normal z-Test for the Mean
      5. Case Study: The Earth’s Ecliptic
      6. Student’s t-Test
      7. Comparing the Normal and Student’s t Distributions
      8. Testing the Mean
      9. The p-Value Animation
      10. Power of the t-Test
    5. Practical Significance vs. Statistical Significance
    6. Examining for Normality
      1. Normal Quantile Plots
      2. Statistical Tests for Normality
    7. Special Topic: Practical Difference
    8. Special Topic: Simulating the Central Limit Theorem
    9. Seeing Kernel Density Estimates
    10. Exercises
  9. The Difference Between Two Means
    1. Overview
    2. Two Independent Groups
      1. When the Difference Isn’t Significant
      2. Check the Data
      3. Launch the Fit Y by X Platform
      4. Examine the Plot
      5. Display and Compare the Means
      6. Inside the Student’s t-Test
      7. Equal or Unequal Variances?
      8. One-Sided Version of the Test
      9. Analysis of Variance and the All-Purpose F-Test
      10. How Sensitive Is the Test? How Many More Observations Are Needed?
      11. When the Difference Is Significant
    3. Normality and Normal Quantile Plots
    4. Testing Means for Matched Pairs
      1. Thermometer Tests
      2. Look at the Data
      3. Look at the Distribution of the Difference
      4. Student’s t-Test
      5. The Matched Pairs Platform for a Paired t-Test
      6. Optional Topic: An Equivalent Test for Stacked Data
    5. Two Extremes of Neglecting the Pairing Situation: A Dramatization
    6. A Nonparametric Approach
      1. Introduction to Nonparametric Methods
      2. Paired Means: The Wilcoxon Signed-Rank Test
      3. Independent Means: The Wilcoxon Rank Sum Test
    7. Exercises
  10. Comparing Many Means: One-Way Analysis of Variance
    1. Overview
    2. What Is a One-Way Layout?
    3. Comparing and Testing Means
      1. Means Diamonds: A Graphical Description of Group Means
      2. Statistical Tests to Compare Means
      3. Means Comparisons for Balanced Data
      4. Means Comparisons for Unbalanced Data
    4. Adjusting for Multiple Comparisons
    5. Are the Variances Equal Across the Groups?
    6. Testing Means with Unequal Variances
    7. Nonparametric Methods
      1. Review of Rank-Based Nonparametric Methods
      2. The Three Rank Tests in JMP
    8. Exercises
  11. Fitting Curves through Points: Regression
    1. Overview
    2. Regression
      1. Least Squares
      2. Seeing Least Squares
      3. Fitting a Line and Testing the Slope
      4. Testing the Slope by Comparing Models
      5. The Distribution of the Parameter Estimates
      6. Confidence Intervals on the Estimates
      7. Examine Residuals
      8. Exclusion of Rows
      9. Time to Clean Up
    3. Polynomial Models
      1. Look at the Residuals
      2. Higher-Order Polynomials
      3. Distribution of Residuals
    4. Transformed Fits
      1. Spline Fit
    5. Are Graphics Important?
    6. Why It’s Called Regression
    7. What Happens When X and Y Are Switched?
    8. Curiosities
      1. Sometimes It’s the Picture That Fools You
      2. High-Order Polynomial Pitfall
      3. The Pappus Mystery on the Obliquity of the Ecliptic
    9. Exercises
  12. Categorical Distributions
    1. Overview
    2. Categorical Situations
    3. Categorical Responses and Count Data: Two Outlooks
    4. A Simulated Categorical Response
      1. Simulating Some Categorical Response Data
      2. Variability in the Estimates
      3. Larger Sample Sizes
      4. Monte Carlo Simulations for the Estimators
      5. Distribution of the Estimates
    5. The X2 Pearson Chi-Square Test Statistic
    6. The G2 Likelihood-Ratio Chi-Square Test Statistic
      1. Likelihood Ratio Tests
      2. The G2 Likelihood Ratio Chi-Square Test
    7. Univariate Categorical Chi-Square Tests
      1. Comparing Univariate Distributions
      2. Charting to Compare Results
    8. Exercises
  13. Categorical Models
    1. Overview
    2. Fitting Categorical Responses to Categorical Factors: Contingency Tables
      1. Testing with G2 and X2
      2. Looking at Survey Data
      3. Car Brand by Marital Status
      4. Car Brand by Size of Vehicle
    3. Two-Way Tables: Entering Count Data
      1. Expected Values Under Independence
      2. Entering Two-Way Data into JMP
      3. Testing for Independence
    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
      1. Fitting a Logistic Model
      2. Degrees of Fit
      3. A Discriminant Alternative
      4. Inverse Prediction
      5. Polytomous Responses: More Than Two Levels
      6. Ordinal Responses: Cumulative Ordinal Logistic Regression
    7. Surprise: Simpson's Paradox: Aggregate Data versus Grouped Data
    8. Generalized Linear Models
    9. Exercises
  14. Multiple Regression
    1. Overview
    2. Parts of a Regression Model
    3. Regression Definitions
    4. A Multiple Regression Example
      1. Residuals and Predicted Values
      2. The Analysis of Variance Table
      3. The Whole Model F-Test
      4. Whole-Model Leverage Plot
      5. Details on Effect Tests
      6. Effect Leverage Plots
    5. Collinearity
      1. Exact Collinearity, Singularity, Linear Dependency
      2. The Longley Data: An Example of Collinearity
      3. The Case of the Hidden Leverage Point
    6. Mining Data with Stepwise Regression
    7. Exercises
  15. Fitting Linear Models
    1. Overview
    2. The General Linear Model
      1. Kinds of Effects in Linear Models
      2. Coding Scheme to Fit a One-Way anova as a Linear Model
      3. Regressor Construction
      4. Interpretation of Parameters
      5. Predictions Are the Means
      6. Parameters and Means
      7. Analysis of Covariance: Continuous and Categorical Terms in the Same Model
      8. The Prediction Equation
      9. The Whole-Model Test and Leverage Plot
      10. Effect Tests and Leverage Plots
      11. Least Squares Means
      12. Lack of Fit
      13. Separate Slopes: When the Covariate Interacts with a Categorical Effect
    3. Two-Way Analysis of Variance and Interactions
    4. Optional Topic: Random Effects and Nested Effects
      1. Nesting
      2. Repeated Measures
      3. Method 1: Random Effects-Mixed Model
      4. Method 2: Reduction to the Experimental Unit
      5. Method 3: Correlated Measurements-Multivariate Model
      6. Varieties of Analysis
      7. Closing Thoughts
    5. Exercises
  16. Design of Experiments
    1. Overview
    2. Introduction
      1. Key Concepts
      2. JMP DOE
    3. A Simple Design
      1. The Experiment
      2. Enter the Response and Factors
      3. Define the Model
      4. Is the Design Balanced?
      5. Perform Experiment and Enter Data
      6. Analyze the Model
      7. Flour Paste Conclusions
      8. Details of the Design - Confounding Structure
    4. Using the Custom Designer
      1. How the Custom Designer Works
      2. Choices in the Custom Designer
    5. An Interaction Model: The Reactor Data
      1. Analyzing the Reactor Data
      2. Where Do We Go From Here?
    6. Some Routine Screening Examples
      1. Main Effects Only (a Review)
      2. All Two-Factor Interactions Involving A Single Factor
      3. Alias Optimal Designs
    7. Response Surface Designs
      1. The Odor Experiment
      2. Response Surface Designs in JMP
      3. Analyzing the Odor Response Surface Design
      4. Plotting Surface Effects
      5. Specifying Response Surface Effects Manually
      6. The Custom Designer vs. the Response Surface Design Platform
    8. Split Plot Designs
      1. The Box Corrosion Split-Plot Experiment
      2. Designing the Experiment
      3. Analysis of Split Plot Designs
    9. Design Strategies
    10. Design of Experiments Glossary
    11. Exercises
  17. Bivariate and Multivariate Relationships
    1. Overview
    2. Bivariate Distributions
    3. Density Estimation
      1. Bivariate Density Estimation
      2. Mixtures, Modes, and Clusters
      3. The Elliptical Contours of the Normal Distribution
    4. Correlations and the Bivariate Normal
      1. Simulating Bivariate Correlations
      2. Correlations Across Many Variables
      3. Bivariate Outliers
    5. Outliers in Three and More Dimensions
    6. Identify Variation with Principal Components Analysis
      1. Principal Components for Six Variables
      2. How Many Principal Components?
    7. Discriminant Analysis
      1. Canonical Plot
      2. Discriminant Scores
      3. Stepwise Discriminant Variable Selection
    8. Cluster Analysis
      1. Hierarchical clustering work: How Does it Work?
      2. A Real-World Example
    9. Some Final Thoughts
    10. Exercises
  18. Exploratory Modeling
    1. Overview
    2. Recursive Partitioning (Decision Trees)
      1. Growing Trees
      2. Exploratory Modeling with Partition
      3. Saving Columns and Formulas
    3. Neural Nets
      1. A Simple Example
      2. Modeling with Neural Networks
      3. Saving Columns
      4. Profiles in Neural
    4. Exercises
  19. Control Charts and Capability
    1. Overview
    2. What Does a Control Chart Look Like
    3. Types of Control Charts
      1. Variables Charts
      2. Attributes Charts
      3. Specialty Charts
    4. Control Chart Basics
    5. Control Charts for Variables Data
    6. Variables Charts using Control Chart Builder
      1. The Control Chart Builder Work Space
      2. Control Chart Builder Examples
    7. Control Charts for Attributes Data
    8. Specialty Charts
      1. Presummarize Charts
      2. Levey-Jennings Charts
      3. Uniformly Weighted Moving Average (UWMA) Charts
      4. Exponentially Weighted Moving Average (EWMA) Chart
    9. Capability Analysis
      1. What is Process Capability?
      2. Capability for One Process Measurement
      3. Capability for Many Process Measurements
      4. Capability for Time-Ordered Data
    10. A Few Words About Measurement Systems
    11. Exercises
  20. Mechanics of Statistics
    1. Overview
    2. Springs for Continuous Responses
      1. Fitting a Mean
      2. Testing a Hypothesis
      3. One-Way Layout
      4. Effect of Sample Size Significance
      5. Effect of Error Variance on Significance
      6. Experimental Design’s Effect on Significance
      7. Simple Regression
      8. Leverage
      9. Multiple Regression
      10. Summary: Significance and Power
    3. Mechanics of Fit for Categorical Responses
      1. How Do Pressure Cylinders Behave?
      2. Estimating Probabilities
      3. One-Way Layout for Categorical Data
      4. Logistic Regression
  21. Analyze and Graph Menu Commands
    1. Analyze Menu
  22. Answers to Selected Exercises
    1. Chapter 4, "Formula Editor Adventures"
    2. Chapter 7, "Univariate Distributions: One Variable, One Sample"
    3. Chapter 8, "The Difference Between Two Means"
    4. Chapter 9, "Comparing Many Means: One-Way Analysis of Variance"
    5. Chapter 10, "Fitting Curves through Points: Regression"
    6. Chapter 11, "Categorical Distributions"
    7. Chapter 12, "Categorical Models"
    8. Chapter 13, "Multiple Regression"
    9. Chapter 14, "Fitting Linear Models"
    10. Chapter 15, "Design of Experiments"
    11. Chapter 16, "Bivariate and Multivariate Relationships"
    12. Chapter 17, "Exploratory Modeling"
    13. Chapter 18, "Control Charts and Capability"
  23. References and Data Sources
  24. Technology License Notices
  25. Index