Practical Data Analysis with JMP, Third Edition

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