From Practical Data Analysis with JMP, Second Edition. Full book available for purchase here. About This Book... xiii About The Author...

Transcription

1 From Practical Data Analysis with JMP, Second Edition. Full book available for purchase here. Contents About This Book... xiii About The Author... xxiii Chapter 1 Getting Started: Data Analysis with JMP... 1 Overview... 1 Goals of Data Analysis: Description and Inference... 2 Types of Data... 3 Starting JMP... 4 A Simple Data Table... 5 Graph Builder: An Interactive Tool to Explore Data... 9 Using an Analysis Platform Row States Exporting JMP Results to a Word-Processor Document Saving Your Work Leaving JMP Chapter 2 Data Sources and Structures Overview Populations, Processes, and Samples Representativeness and Sampling Simple Random Sampling Other Types of Random Sampling Non-Random Sampling Big Data Cross-Sectional and Time Series Sampling Study Design: Experimentation, Observation, and Surveying... 27

2 iv Experimental Data An Example Observational Data An Example Survey Data An Example 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 the Data Filter to Temporarily Narrow the Focus Using the Chart Command to Graph Categorical Data Using the Graph Builder to Explore Categorical Data Distribution of a Quantitative Variable Using the Distribution Platform for Continuous Data Taking Advantage of Linked Graphs and Tables to Explore Data Customizing Bars and Axes in a Histogram Exploring Further with the Graph Builder Summary Statistics for a Single Variable Outlier Box Plots 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 More Informative Scatter Plots Application... 79

3 v Chapter 5 Review of Descriptive Statistics Overview The World Development Indicators Millennium Development Goals Questions for Analysis Applying an Analytic Framework Data Source and Structure Observational Units Variable Definitions and Data Types 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 Probability of an Event Rules for Two Events Assigning Probability Values Contingency Tables and Probability Discrete Random Variables: From Events to Numbers Three Common Discrete Distributions Integer Distribution Binomial Poisson Simulating Random Variation with JMP Discrete Distributions as Models of Real Processes Application

4 vi Chapter 7 The Normal Model Overview Continuous Data and Probability Density Functions The Normal Model Normal Calculations Solving Cumulative Probability Problems Solving Inverse Cumulative Problems Checking Data for the Suitability of a Normal Model Normal Quantile Plots 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 Sampling Distribution of the Sample Proportion From Simulation to Generalization Sampling Distribution of the Sample Mean The Central Limit Theorem Stratification, Clustering, and Complex Sampling (optional) 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: Ordinary and Extraordinary Sampling Variability Case 1: Sample Observations of a Categorical Variable Case 2: Sample Observations of a Continuous Variable Conclusion

5 vii 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 Working with Casewise Data Working with Summary Data 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 More About P-Values The Power of a Test What if Conditions Aren t 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 What Are We Assuming? Inference for Two Categorical Variables Contingency Tables Revisited Chi-Square Test of Independence What Are We Assuming? Application Chapter 13 Two-Sample Inference for a Continuous Variable Overview Conditions for Inference

6 viii Using JMP to Compare Two Means Assuming Normal Distributions or CLT Using Sampling Weights (optional section) Equal vs. Unequal Variances Dealing with Non-Normal Distributions Using JMP to Compare Two Variances Application Chapter 14 Analysis of Variance Overview What Are We Assuming? One-Way ANOVA Does the Sample Satisfy the Assumptions? Factorial Analysis for Main Effects What if Conditions Are Not Satisfied? Including a Second Factor with Two-Way ANOVA Evaluating Assumptions Interaction and Main Effects Application Chapter 15 Simple Linear Regression Inference Overview Fitting a Line to Bivariate Continuous Data The Simple Regression Model Thinking About Linearity Random Error What Are We Assuming? Interpreting Regression Results Summary of Fit Lack of Fit Analysis of Variance Parameter Estimates and t-tests Testing for a Slope Other Than Zero Application

7 ix Chapter 16 Residuals Analysis and Estimation Overview Conditions for Least Squares Estimation Residuals Analysis Linearity Curvature Influential Observations Normality Constant Variance Independence Estimation Confidence Intervals for Parameters Confidence Intervals for Y X Prediction Intervals for Y X Application Chapter 17 Review of Univariate and Bivariate Inference Overview Research Context One Variable at a Time Life Expectancy by Income Group Checking Assumptions Conducting an ANOVA Life Expectancy by GDP Per Capita Summing Up 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

8 x Collinearity An Example Free of Collinearity Problems An Example of Collinearity Dealing with Collinearity Evaluating Alternative Models Application Chapter 19 Categorical, Curvilinear, and Non-Linear Regression Models Overview Dichotomous Independent Variables Dichotomous Dependent Variable Whole Model Test Parameter Estimates Effect Likelihood Ratio Tests Curvilinear and Non-Linear Relationships Quadratic Models Logarithmic Models Application Chapter 20 Basic Forecasting Techniques Overview Detecting Patterns Over Time Smoothing Methods Simple Moving Average Simple Exponential Smoothing Linear Exponential Smoothing (Holt s Method) Winters Method Trend Analysis Autoregressive Models Application Chapter 21 Elements of Experimental Design Overview Why Experiment? Goals of Experimental Design Factors, Blocks, and Randomization

9 xi Multi-Factor Experiments and Factorial Designs Blocking Fractional Designs Response Surface Designs Application Chapter 22 Quality Improvement Overview Processes and Variation Control Charts Charts for Individual Observations Charts for Means Charts for Proportions Capability Analysis Pareto Charts Application Appendix A Data Sources Overview Data Tables and Sources Appendix B Data Management Overview Entering Data from the Keyboard Moving Data from Excel Files into a JMP Data Table Importing an Excel File from JMP The JMP Add-in for Excel Importing Data Directly from a Website Combining Data from Two or More Sources Bibliography Index From Practical Data Analysis with JMP, Second Edition by Robert H. Carver. Copyright 2014, SAS Institute Inc., Cary, North Carolina, USA. ALL RIGHTS RESERVED.