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Practical Data Analysis with JMP®

Book Description

Uses the powerful interactive and visual approach of JMP to introduce readers to the logic and methods of statistical thinking and data analysis. It includes a myriad of examples and problems that rely on real sets of data, often starting with an important or interesting research question that an investigator has pursued.

Table of Contents

  1. Copyright
  2. Preface
    1. Learning to Reason Statistically with Real Data
    2. Scope and Structure of This Book
    3. The Data Files
    4. Using This Book
    5. Thanks and Acknowledgments
  3. Getting Started: Introduction to JMP
    1. Goals of Data Analysis: Description and Inference
    2. Types of Data
    3. Starting JMP
    4. A Simple Data Table
    5. Hot Spots
    6. Analysis Platforms—A First Descriptive Analysis
    7. Row States
    8. Exporting JMP Results to a Word-Processor Document
    9. Saving Your Work
    10. Leaving JMP
  4. Understanding Data
    1. Populations, Processes, and Samples
    2. Representativeness and Sampling
    3. Cross-Sectional and Time Series Sampling
    4. Study Design: Experimentation, Observation, Surveying
    5. Loading Data into a Data Table
    6. Application
  5. Describing a Single Variable
    1. The Concept of a Distribution
    2. Variable Types and Their Distributions
    3. Distribution of a Categorical Variable
    4. Distribution of a Quantitative Variable
    5. Summary Statistics for a Single Variable
    6. Outlier Box Plots
    7. Application
  6. Describing Two Variables at a Time
    1. Two-by-Two: Bivariate Data
    2. Describing Covariation: Two Categorical Variables
    3. Describing Covariation: Two Continuous Variables
    4. Comparing Two Groups: One Continuous, One Categorical Variable
    5. Visualizing Covariation with the Graph Builder
    6. Application
  7. Elementary Probability and Discrete Distributions
    1. Probability Theory and Data Analysis
    2. Elements of Probability Theory
    3. Contingency Tables and Probability
    4. Discrete Random Variables: From Events to Numbers
    5. Three Common Discrete Distributions
    6. Simulating Random Variation with JMP
    7. Application
  8. The Normal Model
    1. Continuous Data and Probability
    2. Density Functions
    3. The Normal Model
    4. Normal Calculations
    5. Checking Data for Suitability of Normal Model
    6. Simulating Normal Data
    7. Application
  9. Sampling and Sampling Distributions
    1. Why Sample?
    2. Methods of Sampling
    3. Using JMP to Select a Simple Random Sample
    4. All Possible Samples: Sampling Distributions
    5. Extent of Sampling Variation
    6. Application
  10. Inference for a Single Categorical Variable
    1. Two Inferential Tasks
    2. Statistical Inference Is Always Conditional
    3. Confidence Intervals
    4. Using JMP to Estimate a Population Proportion
    5. Using JMP to Conduct a Significance Test
    6. A Few Words About Error
    7. Application
  11. Inference for a Single Continuous Variable
    1. Conditions for Inference
    2. Using JMP to Estimate a Variable's Mean
    3. Using JMP to Conduct a Significance Test
    4. What If Conditions Aren't Satisfied?
    5. Matched Pairs: One Variable, Two Measurements
    6. Application
  12. Two-Sample Inference for a Continuous Variable
    1. Conditions for Inference
    2. Using JMP to Compare Two Means
    3. Using JMP to Compare Two Variances
    4. Application
  13. Chi-Square Tests
    1. Further Inference for Categorical Variables
    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
  14. Analysis of Variance
    1. What Are We Assuming?
    2. One-Way ANOVA
    3. Approaches When Conditions Are Not Satisfied
    4. Two-Way ANOVA
    5. Application
  15. Simple Linear Regression
    1. Fitting a Line to Bivariate Continuous Data
    2. The Simple Regression Model
    3. What Are We Assuming?
    4. Interpreting Regression Results
    5. Application
  16. Regression Conditions and Estimation
    1. Conditions for Least Squares Estimation
    2. Residual Analysis
    3. Estimation
    4. Application
  17. Multiple Regression
    1. The Multiple Regression Model
    2. Visualizing Multiple Regression
    3. Fitting a Model
    4. A More Complex Model
    5. Residual Analysis in the Fit Model Platform
    6. Collinearity
    7. Evaluating Alternative Models
    8. Application
  18. Categorical and Non-Linear Regression Models
    1. Introduction
    2. Dichotomous Independent Variables
    3. Dichotomous Dependent Variable
    4. Non-Linear Relationships
    5. Application
  19. Basic Forecasting Techniques
    1. Detecting Patterns Over Time
    2. Smoothing Methods
    3. Trend Analysis
    4. Autoregressive Models
    5. Application
  20. Elements of Experimental Design
    1. Experimental and Observational Studies
    2. Goals of Experimental Design
    3. Factors, Blocks, and Randomization
    4. Multi-factor Experiments and Factorial Designs
    5. Blocking
    6. Fractional Designs
    7. Response Surface Designs
    8. Application
  21. Quality Improvement
    1. Processes and Variation
    2. Control Charts
    3. Capability Analysis
    4. Pareto Charts
    5. Application
  22. Data Sources
    1. Introduction
    2. Data Tables and Sources
  23. Bibliography
  24. Index