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Practical Data Analysis with JMP, Third Edition, 3rd Edition

Author Robert Carver
Release Date: 2019/10/01
ISBN: 9781642956122
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Book Description

Master the concepts and techniques of statistical analysis using JMP

Practical Data Analysis with JMP, Third Edition, highlights the powerful interactive and visual approach of JMP to introduce readers to statistical thinking and data analysis. It helps you choose the best technique for the problem at hand by using real-world cases. It also illustrates best-practice workflow throughout the entire investigative cycle, from asking valuable questions through data acquisition, preparation, analysis, interpretation, and communication of findings.

The book can stand on its own as a learning resource for professionals, or it can be used to supplement a college-level textbook for an introductory statistics course. It includes varied examples and problems using real sets of data. Each chapter typically starts with an important or interesting research question that an investigator has pursued. Reflecting the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, and economics, as well as international and historical examples.

Application Scenarios at the end of each chapter challenge you to use your knowledge and skills with data sets that go beyond mere repetition of chapter examples.

New in the third edition, chapters have been updated to demonstrate the enhanced capabilities of JMP, including projects, Graph Builder, Query Builder, and Formula Depot.

Table of Contents

  1. Contents
  2. About This Book
  3. About The Author
  4. Chapter 1: Getting Started: Data Analysis with JMP
    1. Overview
    2. Goals of Data Analysis: Description and Inference
    3. Types of Data
    4. Starting JMP
    5. A Simple Data Table
    6. Graph Builder: An Interactive Tool to Explore Data
    7. Using an Analysis Platform
    8. Row States
    9. Exporting and Sharing JMP Reports
    10. Saving and Reproducing Your Work
    11. Leaving JMP
  5. Chapter 2: Data Sources and Structures
    1. Overview
    2. Populations, Processes, and Samples
    3. Representativeness and Sampling
    4. Cross-Sectional and Time Series Sampling
    5. Study Design: Experimentation, Observation, and Surveying
    6. Creating a Data Table
    7. Raw Case Data and Summary Data
    8. Application
  6. Chapter 3: Describing a Single Variable
    1. Overview
    2. The Concept of a Distribution
    3. Variable Types and Their Distributions
    4. Distribution of a Categorical Variable
    5. Using Graph Builder to Explore Categorical Data Visually
    6. Distribution of a Quantitative Variable
    7. Using the Distribution Platform for Continuous Data
    8. Exploring Further with the Graph Builder
    9. Summary Statistics for a Single Variable
    10. Application
  7. Chapter 4: Describing Two Variables at a Time
    1. Overview
    2. Two-by-Two: Bivariate Data
    3. Describing Covariation: Two Categorical Variables
    4. Describing Covariation: One Continuous, One Categorical Variable
    5. Describing Covariation: Two Continuous Variables
    6. Application
  8. Chapter 5: Review of Descriptive Statistics
    1. Overview
    2. The World Development Indicators1
    3. Questions for Analysis
    4. Applying an Analytic Framework
    5. Preparation for Analysis
    6. Univariate Descriptions
    7. Explore Relationships with Graph Builder
    8. Further Analysis with the Multivariate Platform
    9. Further Analysis with Fit Y by X
    10. Summing Up: Interpretation and Conclusions
    11. Visualizing Multiple Relationships
  9. Chapter 6: Elementary Probability and Discrete Distributions
    1. Overview
    2. The Role of Probability in Data Analysis
    3. Elements of Probability Theory
    4. Contingency Tables and Probability
    5. Discrete Random Variables: From Events to Numbers
    6. Three Common Discrete Distributions
    7. Simulating Random Variation with JMP
    8. Discrete Distributions as Models of Real Processes
    9. Application
  10. Chapter 7: The Normal Model
    1. Overview
    2. Continuous Data and Probability
    3. Density Functions
    4. The Normal Model
    5. Normal Calculations
    6. Checking Data for the Suitability of a Normal Model
    7. Generating Pseudo-Random Normal Data
    8. Application
  11. Chapter 8: Sampling and Sampling Distributions
    1. Overview
    2. Why Sample?
    3. Methods of Sampling
    4. Using JMP to Select a Simple Random Sample
    5. Variability Across Samples: Sampling Distributions
    6. Application
  12. Chapter 9: Review of Probability and Probabilistic Sampling
    1. Overview
    2. Probability Distributions and Density Functions
    3. The Normal and t Distributions
    4. The Usefulness of Theoretical Models
    5. When Samples Surprise Us: Ordinary and Extraordinary Sampling Variability
    6. Conclusion
  13. Chapter 10: Inference for a Single Categorical Variable
    1. Overview
    2. Two Inferential Tasks
    3. Statistical Inference Is Always Conditional
    4. Using JMP to Conduct a Significance Test
    5. Confidence Intervals
    6. Using JMP to Estimate a Population Proportion
    7. A Few Words about Error
    8. Application
  14. Chapter 11: Inference for a Single Continuous Variable
    1. Overview
    2. Conditions for Inference
    3. Using JMP to Conduct a Significance Test
    4. What If Conditions Are Not Satisfied?
    5. Using JMP to Estimate a Population Mean
    6. Matched Pairs: One Variable, Two Measurements
    7. Application
  15. Chapter 12: Chi-Square Tests
    1. Overview
    2. Chi-Square Goodness-of-Fit Test
    3. Inference for Two Categorical Variables
    4. Contingency Tables Revisited
    5. Chi-Square Test of Independence
    6. Application
  16. Chapter 13: Two-Sample Inference for a Continuous Variable
    1. Overview
    2. Conditions for Inference
    3. Using JMP to Compare Two Means
    4. Using JMP to Compare Two Variances
    5. Application
  17. Chapter 14: Analysis of Variance
    1. Overview
    2. What Are We Assuming?
    3. One-Way ANOVA
    4. What If Conditions Are Not Satisfied?
    5. Including a Second Factor with Two-Way ANOVA
    6. Application
  18. Chapter 15: Simple Linear Regression Inference
    1. Overview
    2. Fitting a Line to Bivariate Continuous Data
    3. The Simple Regression Model
    4. What Are We Assuming?
    5. Interpreting Regression Results
    6. Application
  19. Chapter 16: Residuals Analysis and Estimation
    1. Overview
    2. Conditions for Least Squares Estimation
    3. Residuals Analysis
    4. Estimation
    5. Application
  20. Chapter 17: Review of Univariate and Bivariate Inference
    1. Overview
    2. Research Context
    3. One Variable at a Time
    4. Life Expectancy by Income Group
    5. Life Expectancy by GDP per Capita
    6. Conclusion
  21. Chapter 18: Multiple Regression
    1. Overview
    2. The Multiple Regression Model
    3. Visualizing Multiple Regression
    4. Fitting a Model
    5. A More Complex Model
    6. Residuals Analysis in the Fit Model Platform
    7. Using a Regression Tree Approach: The Partition Platform
    8. Collinearity
    9. Evaluating Alternative Models
    10. Application
  22. Chapter 19: Categorical, Curvilinear, and Non-Linear Regression Models
    1. Overview
    2. Dichotomous Independent Variables
    3. Dichotomous Dependent Variable
    4. Curvilinear and Non-Linear Relationships
    5. More Non-Linear Functions
    6. Application
  23. Chapter 20: Basic Forecasting Techniques
    1. Overview
    2. Detecting Patterns Over Time
    3. Smoothing Methods
    4. Trend Analysis
    5. Autoregressive Models
    6. Application
  24. Chapter 21: Elements of Experimental Design
    1. Overview
    2. Why Experiment?
    3. Goals of Experimental Design
    4. Factors, Blocks, and Randomization
    5. Multi-Factor Experiments and Factorial Designs
    6. Blocking
    7. A Design for Main Effects Only
    8. Definitive Screening Designs
    9. Non-Linear Response Surface Designs
    10. Application
  25. Chapter 22: Quality Improvement
    1. Overview
    2. Processes and Variation
    3. Control Charts
    4. Variability Charts
    5. Capability Analysis
    6. Pareto Charts
    7. Application
  26. Bibliography
  27. Index
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