Practical Data Analysis with JMP, Second Edition, 2nd Edition

Book Description

Understand the concepts and techniques of analysis while learning to reason statistically. Being an effective analyst requires that you know how to properly define a problem and apply suitable statistical techniques, as well as clearly and honestly communicate the results with information-rich visualizations and precise language. Being a well-informed consumer of analyses requires the same set of skills so that you can recognize credible, actionable research when you see it. Robert Carver's Practical Data Analysis with JMP, Second Edition uses the powerful interactive and visual approach of JMP to introduce readers to the logic and methods of statistical thinking and data analysis. It enables you to discriminate among and to use fundamental techniques of analysis, enabling you to engage in statistical thinking by analyzing real-world problems. “Application Scenarios” at the end of each chapter challenge you to put your knowledge and skills to use with data sets that go beyond mere repetition of chapter examples, and three new review chapters help readers integrate ideas and techniques. In addition, the scope and sequence of the chapters have been updated with more coverage of data management and analysis of data. The book can stand on its own as a learning resource for professionals or be used to supplement a standard college-level introduction-to-statistics textbook. It includes varied examples and problems that rely on real sets of data, typically starting with an important or interesting research question that an investigator has pursued. Reflective of the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, economics, among Practical Data Analysis with JMP, Second Edition introduces you to the major platforms and essential features of JMP and will leave you with a sufficient background and the confidence to continue your exploration independently. This book is part of the SAS Press program.

Table of Contents

  1. About This Book
  2. About The Author
  3. 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 JMP Results to a Word-Processor Document
    10. Saving Your Work
    11. Leaving JMP
  4. Chapter 2 Data Sources and Structures
    1. Overview
    2. Populations, Processes, and Samples
    3. Representativeness and Sampling
      1. Simple Random Sampling
      2. Other Types of Random Sampling
      3. Non-Random Sampling
      4. Big Data
    4. Cross-Sectional and Time Series Sampling
    5. Study Design: Experimentation, Observation, and Surveying
      1. Experimental Data—An Example
      2. Observational Data—An Example
      3. Survey Data—An Example
    6. Creating a Data Table
    7. Raw Case Data and Summary Data
    8. Application
  5. 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
      1. Using the Data Filter to Temporarily Narrow the Focus
      2. Using the Chart Command to Graph Categorical Data
      3. Using the Graph Builder to Explore Categorical Data
    5. Distribution of a Quantitative Variable
      1. Using the Distribution Platform for Continuous Data
      2. Taking Advantage of Linked Graphs and Tables to Explore Data
      3. Customizing Bars and Axes in a Histogram
      4. Exploring Further with the Graph Builder
      5. Summary Statistics for a Single Variable
      6. Outlier Box Plots
    6. Application
  6. 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. More Informative Scatter Plots
    7. Application
  7. Chapter 5 Review of Descriptive Statistics
    1. Overview
    2. The World Development Indicators
      1. Millennium Development Goals
    3. Questions for Analysis
    4. Applying an Analytic Framework
      1. Data Source and Structure
      2. Observational Units
      3. Variable Definitions and Data Types
    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
  8. Chapter 6 Elementary Probability and Discrete Distributions
    1. Overview
    2. The Role of Probability in Data Analysis
    3. Elements of Probability Theory
      1. Probability of an Event
      2. Rules for Two Events
      3. Assigning Probability Values
    4. Contingency Tables and Probability
    5. Discrete Random Variables: From Events to Numbers
    6. Three Common Discrete Distributions
      1. Integer Distribution
      2. Binomial
      3. Poisson
    7. Simulating Random Variation with JMP
    8. Discrete Distributions as Models of Real Processes
    9. Application
  9. Chapter 7 The Normal Model
    1. Overview
    2. Continuous Data and Probability
    3. Density Functions
    4. The Normal Model
    5. Normal Calculations
      1. Solving Cumulative Probability Problems
      2. Solving Inverse Cumulative Problems
      3. Checking Data for the Suitability of a Normal Model
      4. Normal Quantile Plots
    6. Generating Pseudo-Random Normal Data
    7. Application
  10. 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
      1. Sampling Distribution of the Sample Proportion
      2. From Simulation to Generalization
      3. Sampling Distribution of the Sample Mean
      4. The Central Limit Theorem
      5. Stratification, Clustering, and Complex Sampling (optional)
    6. Application
  11. 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: Ordinary and Extraordinary Sampling Variability
      1. Case 1: Sample Observations of a Categorical Variable
      2. Case 2: Sample Observations of a Continuous Variable
    6. Conclusion
  12. 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
      1. Working with Casewise Data
      2. Working with Summary Data
    7. A Few Words About Error
    8. Application
  13. Chapter 11 Inference for a Single Continuous Variable
    1. Overview
    2. Conditions for Inference
    3. Using JMP to Conduct a Significance Test
      1. More About P-Values
      2. The Power of a Test
    4. What if Conditions Aren’t Satisfied?
    5. Using JMP to Estimate a Population Mean
    6. Matched Pairs: One Variable, Two Measurements
    7. Application
  14. Chapter 12 Chi-Square Tests
    1. Overview
    2. Chi-Square Goodness-of-Fit Test
      1. What Are We Assuming?
    3. Inference for Two Categorical Variables
    4. Contingency Tables Revisited
    5. Chi-Square Test of Independence
      1. What Are We Assuming?
      2. Application
  15. Chapter 13 Two-Sample Inference for a Continuous Variable
    1. Overview
    2. Conditions for Inference
    3. Using JMP to Compare Two Means
      1. Assuming Normal Distributions or CLT
      2. Using Sampling Weights (optional section)
      3. Equal vs. Unequal Variances
      4. Dealing with Non-Normal Distributions
    4. Using JMP to Compare Two Variances
    5. Application
  16. Chapter 14 Analysis of Variance
    1. Overview
    2. What Are We Assuming?
    3. One-Way ANOVA
      1. Does the Sample Satisfy the Assumptions?
      2. Factorial Analysis for Main Effects
    4. What if Conditions Are Not Satisfied?
    5. Including a Second Factor with Two-Way ANOVA
      1. Evaluating Assumptions
      2. Interaction and Main Effects
    6. Application
  17. Chapter 15 Simple Linear Regression Inference
    1. Overview
    2. Fitting a Line to Bivariate Continuous Data
    3. The Simple Regression Model
      1. Thinking About Linearity
      2. Random Error
    4. What Are We Assuming?
    5. Interpreting Regression Results
      1. Summary of Fit
      2. Lack of Fit
      3. Analysis of Variance
      4. Parameter Estimates and t-tests
      5. Testing for a Slope Other Than Zero
    6. Application
  18. Chapter 16 Residuals Analysis and Estimation
    1. Overview
    2. Conditions for Least Squares Estimation
    3. Residuals Analysis
      1. Linearity
      2. Curvature
      3. Influential Observations
      4. Normality
      5. Constant Variance
      6. Independence
    4. Estimation
      1. Confidence Intervals for Parameters
      2. Confidence Intervals for Y|X
      3. Prediction Intervals for Y|X
    5. Application
  19. 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
      1. Checking Assumptions
      2. Conducting an ANOVA
    5. Life Expectancy by GDP Per Capita
    6. Summing Up
  20. 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. Collinearity
      1. An Example Free of Collinearity Problems
      2. An Example of Collinearity
      3. Dealing with Collinearity
    8. Evaluating Alternative Models
    9. Application
  21. Chapter 19 Categorical, Curvilinear, and Non-Linear Regression Models
    1. Overview
    2. Dichotomous Independent Variables
    3. Dichotomous Dependent Variable
      1. Whole Model Test
      2. Parameter Estimates
      3. Effect Likelihood Ratio Tests
    4. Curvilinear and Non-Linear Relationships
      1. Quadratic Models
      2. Logarithmic Models
    5. Application
  22. Chapter 20 Basic Forecasting Techniques
    1. Overview
    2. Detecting Patterns Over Time
    3. Smoothing Methods
      1. Simple Moving Average
      2. Simple Exponential Smoothing
      3. Linear Exponential Smoothing (Holt’s Method)
      4. Winters’ Method
    4. Trend Analysis
    5. Autoregressive Models
    6. Application
  23. 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. Fractional Designs
    8. Response Surface Designs
    9. Application
  24. Chapter 22 Quality Improvement
    1. Overview
    2. Processes and Variation
    3. Control Charts
      1. Charts for Individual Observations
      2. Charts for Means
      3. Charts for Proportions
    4. Capability Analysis
    5. Pareto Charts
    6. Application
  25. Appendix A Data Sources
    1. Overview
    2. Data Tables and Sources
  26. Appendix B Data Management
    1. Overview
    2. Entering Data from the Keyboard
    3. Moving Data from Excel Files into a JMP Data Table
      1. Importing an Excel File from JMP
      2. The JMP Add-in for Excel
    4. Importing Data Directly from a Website
    5. Combining Data from Two or More Sources
  27. Bibliography
  28. Index

Product Information

  • Title: Practical Data Analysis with JMP, Second Edition, 2nd Edition
  • Author(s): Robert Carver
  • Release date: July 2014
  • Publisher(s): SAS Institute
  • ISBN: 9781629592657