From Biostatistics Using JMP: A Practical Guide. Full book available for purchase here. Chapter 1: Introduction... 1

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1 From Biostatistics Using JMP: A Practical Guide. Full book available for purchase here. Contents Dedication... iii Acknowledgments... xi About This Book... xiii About the Author... xvii Chapter 1: Introduction Background and Overview Getting Started with JMP General Outline How to Use This Book Reference... 5 Chapter 2: Data Wrangling: Data Collection Introduction Collecting Data from Files JMP Native Files SAS Format Files Excel Spreadsheets Text and CSV Format Extracting Data from Internet Locations Opening as Data Opening as a Webpage Data Modeling Types Incorporating Expression and Contextual Data References Chapter 3: Data Wrangling: Data Cleaning Introduction Tables Stacking Columns Basic Table Organization Column Properties The Sorted Array... 32

2 vi 3.4 Restructuring Data Combining Columns Separating Out a Column (Text to Columns) Creating Indicator Columns Grouping Inside Columns References Chapter 4: Initial Data Analysis with Descriptive Statistics Introduction Histograms and Distributions Histograms Box Plots Stem-and-Leaf Plots Pareto Charts Descriptive Statistics Sample Mean and Standard Deviation Additional Statistical Measures References Chapter 5: Data Visualization Tools Introduction Scatter Plots Coloring Points Copying Better-Looking Figures Multiple Scatter Plots Charts Multidimensional Plots Parallel Plots Cell Plots Multivariate and Correlations Tool Correlation Table Correlation Heat Maps Simple Statistics Additional Multivariate Measures Graph Builder and Custom Figures Graph Builder Custom Colors Incorporating Contextual Data References... 99

3 Chapter 6: Rates, Proportions, and Epidemiology Introduction Rates Crude Rates Adjusted Rates Geographic Visualizations National Visualizations County and Lower Level Visualizations References Chapter 7: Statistical Tests and Confidence Intervals Introduction General Hypothesis Test Background Selecting the Appropriate Method Testing for Normality Histogram Analysis Normal Quantile/Probability Plot Goodness-of-Fit Tests Goodness-of-Fit for Other Distributions General Hypothesis Tests Z-Test Hypothesis Test of Mean T-Test Hypothesis Test of Mean Nonparametric Test of Mean (Wilcoxon Signed Rank) Standard Deviation Hypothesis Test Tests of Proportions Confidence Intervals Mean Confidence Intervals Mean Confidence Intervals with Different Thresholds Confidence Intervals for Proportions Chi-Squared Analysis of Frequency and Contingency Tables Two Sample Tests Comparing Two Group Means Paired Comparison, Matched Pairs References Chapter 8: Analysis of Variance (ANOVA) and Design of Experiments (DoE) Introduction vii

4 viii 8.2 One-Way ANOVA One-Way ANOVA with Fit Y by X Means Comparison, LSD Matrix, and Connecting Letters Fit Y by X Changing Significance Levels Multiple Comparisons, Multiple One-Way ANOVAs One-Way ANOVA via Fit Model One-Way ANOVA for Unequal Group Sizes (Unbalanced) Blocking One-Way ANOVA with Blocking via Fit Y by X One-Way ANOVA with Blocking via Fit Model Note on Blocking Multiple Factors Experimental Design Considerations Multiple ANOVA Feature Selection and Parsimonious Models Multivariate ANOVA (MANOVA) and Repeated Measures Repeated Measures MANOVA Background MANOVA in Fit Model References Chapter 9: Regression and Curve Fitting Introduction Simple Linear Regression Fit Y by X for Bivariate Fits (One X and One Y) Special Fitting Tools Multiple Regression Fit Model Stepwise Feature Selection Analysis of Covariance (ANCOVA) Nonlinear Curve Fitting and a Nonlinear Platform Example References Chapter 10: Diagnostic Methods for Regression, Curve Fitting, and ANOVA Introduction Computing Residuals with Fit Y by X and Fit Model Fit Y by X Fit Model Checking for Normality

5 ix 10.4 Checking for Nonconstant Error Variance (Heteroscedasticity) Checking for Outliers Checking for Nonindependence Multiple Factor Diagnostics Nonlinear Fit Residuals Developing Appropriate Models References Chapter 11: Categorical Data Analysis Introduction Clustering Hierarchical Clustering K-means Clustering Classification JMP Data Preliminaries for Classification Example Data Sets Classification by Logistic Regression Logistic Regression in Fit Y by X Logistic Regression in Fit Model Classification by Discriminant Analysis Discriminant Analysis Loadings Stepwise Discriminant Analysis Classification with Tabulated Data Classifier Performance Verification References Chapter 12: Advanced Modeling Methods Introduction Principal Components and Factor Analysis Principal Components in JMP Dimensionality Assessment Factor Analysis in JMP Partial Least Squares Decision Trees Classification Decision Trees in JMP Predictive Decision Trees in JMP Artificial Neural Networks Neural Network Architecture Classification Neural Networks in JMP Predictive Neural Networks in JMP

6 x 12.6 Control Charts References Chapter 13: Survival Analysis Introduction Life Distributions Kaplan-Meier Curves Simple Survival Analysis Multiple Groups Censoring Proportional Hazards References Chapter 14: Collaboration and Additional Functionality Introduction Saving Scripts and SAS Coding Saving Scripts to Data Table SAS Coding Functionality Collaboration Journals Web Reports Add-Ins Finding Add-Ins Developing Add-Ins Example Add-In: Forest Plot / Meta-analysis Add-In Version Control References Index From Biostatistics Using JMP : A Practical Guide by Trevor Bihl. Copyright 2017, SAS Institute Inc., Cary, North Carolina, USA. ALL RIGHTS RESERVED.