book

Biostatistics Using JMP

Name: Biostatistics Using JMP
Author: Trevor Bihl
ISBN: 9781635262414

by Trevor Bihl

October 2017

Intermediate to advanced

356 pages

8h 39m

English

SAS Institute

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Dedication
Acknowledgments
About This Book
About the Author
Chapter 1: Introduction
1.1 Background and Overview1.2 Getting Started with JMP1.3 General Outline1.4 How to Use This Book1.5 Reference
Chapter 2: Data Wrangling: Data Collection
2.1 Introduction2.2 Collecting Data from Files2.2.1 JMP Native Files2.2.2 SAS Format Files2.2.3 Excel Spreadsheets2.2.4 Text and CSV Format2.3 Extracting Data from Internet Locations2.3.1 Opening as Data2.3.2 Opening as a Webpage2.4 Data Modeling Types2.4.1 Incorporating Expression and Contextual Data2.5 References
Chapter 3: Data Wrangling: Data Cleaning
3.1 Introduction3.2 Tables3.2.1 Stacking Columns3.2.2 Basic Table Organization3.2.3 Column Properties3.3 The Sorted Array3.4 Restructuring Data3.4.1 Combining Columns3.4.2 Separating Out a Column (Text to Columns)3.4.3 Creating Indicator Columns3.4.4 Grouping Inside Columns3.5 References
Chapter 4: Initial Data Analysis with Descriptive Statistics
4.1 Introduction4.2 Histograms and Distributions4.2.1 Histograms4.2.2 Box Plots4.2.3 Stem-and-Leaf Plots4.2.4 Pareto Charts4.3 Descriptive Statistics4.3.1 Sample Mean and Standard Deviation4.3.2 Additional Statistical Measures4.4 References
Chapter 5: Data Visualization Tools
5.1 Introduction5.2 Scatter Plots5.2.1 Coloring Points5.2.2 Copying Better-Looking Figures5.2.3 Multiple Scatter Plots5.3 Charts5.4 Multidimensional Plots5.4.1 Parallel Plots5.4.2 Cell Plots5.5 Multivariate and Correlations Tool5.5.1 Correlation Table5.5.2 Correlation Heat Maps5.5.3 Simple Statistics5.5.4 Additional Multivariate Measures5.6 Graph Builder and Custom Figures5.6.1 Graph Builder Custom Colors5.6.2 Incorporating Contextual Data5.7 References
Chapter 6: Rates, Proportions, and Epidemiology
6.1 Introduction6.2 Rates6.2.1 Crude Rates6.2.2 Adjusted Rates6.3 Geographic Visualizations6.3.1 National Visualizations6.3.2 County and Lower Level Visualizations6.4 References

Chapter 7: Statistical Tests and Confidence Intervals
7.1 Introduction7.1.1 General Hypothesis Test Background7.1.2 Selecting the Appropriate Method7.2 Testing for Normality7.2.1 Histogram Analysis7.2.2 Normal Quantile/Probability Plot7.2.3 Goodness-of-Fit Tests7.2.4 Goodness-of-Fit for Other Distributions7.3 General Hypothesis Tests7.3.1 Z-Test Hypothesis Test of Mean7.3.2 T-Test Hypothesis Test of Mean7.3.3 Nonparametric Test of Mean (Wilcoxon Signed Rank)7.3.4 Standard Deviation Hypothesis Test7.3.5 Tests of Proportions7.4 Confidence Intervals7.4.1 Mean Confidence Intervals7.4.2 Mean Confidence Intervals with Different Thresholds7.4.3 Confidence Intervals for Proportions7.5 Chi-Squared Analysis of Frequency and Contingency Tables7.6 Two Sample Tests7.6.1 Comparing Two Group Means7.6.2 Paired Comparison, Matched Pairs7.7 References
Chapter 8: Analysis of Variance (ANOVA) and Design of Experiments (DoE)
8.1 Introduction8.2 One-Way ANOVA8.2.1 One-Way ANOVA with Fit Y by X8.2.2 Means Comparison, LSD Matrix, and Connecting Letters8.2.3 Fit Y by X Changing Significance Levels8.2.4 Multiple Comparisons, Multiple One-Way ANOVAs8.2.5 One-Way ANOVA via Fit Model8.2.6 One-Way ANOVA for Unequal Group Sizes (Unbalanced)8.3 Blocking8.3.1 One-Way ANOVA with Blocking via Fit Y by X8.3.2 One-Way ANOVA with Blocking via Fit Model8.3.3 Note on Blocking8.4 Multiple Factors8.4.1 Experimental Design Considerations8.4.2 Multiple ANOVA8.4.3 Feature Selection and Parsimonious Models8.5 Multivariate ANOVA (MANOVA) and Repeated Measures8.5.1 Repeated Measures MANOVA Background8.5.2 MANOVA in Fit Model8.6 References
Chapter 9: Regression and Curve Fitting
9.1 Introduction9.2 Simple Linear Regression9.2.1 Fit Y by X for Bivariate Fits (One X and One Y)9.2.2 Special Fitting Tools9.3 Multiple Regression9.3.1 Fit Model9.3.2 Stepwise Feature Selection9.3.3 Analysis of Covariance (ANCOVA)9.4 Nonlinear Curve Fitting and a Nonlinear Platform Example9.5 References
Chapter 10: Diagnostic Methods for Regression, Curve Fitting, and ANOVA
10.1 Introduction10.2 Computing Residuals with Fit Y by X and Fit Model10.2.1 Fit Y by X10.2.2 Fit Model10.3 Checking for Normality10.4 Checking for Nonconstant Error Variance (Heteroscedasticity)10.5 Checking for Outliers10.6 Checking for Nonindependence10.7 Multiple Factor Diagnostics10.8 Nonlinear Fit Residuals10.9 Developing Appropriate Models10.10 References
Chapter 11: Categorical Data Analysis
11.1 Introduction11.2 Clustering11.2.1 Hierarchical Clustering11.2.2 K-means Clustering11.3 Classification11.3.1 JMP Data Preliminaries for Classification11.3.2 Example Data Sets11.4 Classification by Logistic Regression11.4.1 Logistic Regression in Fit Y by X11.4.2 Logistic Regression in Fit Model11.5 Classification by Discriminant Analysis11.5.1 Discriminant Analysis Loadings11.5.2 Stepwise Discriminant Analysis11.6 Classification with Tabulated Data11.7 Classifier Performance Verification11.8 References
Chapter 12: Advanced Modeling Methods
12.1 Introduction12.2 Principal Components and Factor Analysis12.2.1 Principal Components in JMP12.2.2 Dimensionality Assessment12.2.3 Factor Analysis in JMP12.3 Partial Least Squares12.4 Decision Trees12.4.1 Classification Decision Trees in JMP12.4.2 Predictive Decision Trees in JMP12.5 Artificial Neural Networks12.5.1 Neural Network Architecture12.5.2 Classification Neural Networks in JMP12.5.3 Predictive Neural Networks in JMP12.6 Control Charts12.7 References
Chapter 13: Survival Analysis
13.1 Introduction13.2 Life Distributions13.3 Kaplan-Meier Curves13.3.1 Simple Survival Analysis13.3.2 Multiple Groups13.3.3 Censoring13.3.4 Proportional Hazards13.4 References
Chapter 14: Collaboration and Additional Functionality
14.1 Introduction14.2 Saving Scripts and SAS Coding14.2.1 Saving Scripts to Data Table14.2.2 SAS Coding Functionality14.3 Collaboration14.3.1 Journals14.3.2 Web Reports14.4 Add-Ins14.4.1 Finding Add-Ins14.4.2 Developing Add-Ins14.4.3 Example Add-In: Forest Plot / Meta-analysis14.4.4 Add-In Version Control14.5 References
Index

Content preview from Biostatistics Using JMP

Chapter 11: Categorical Data Analysis

11.1 Introduction

11.2 Clustering

11.2.1 Hierarchical Clustering

11.2.2 K-means Clustering

11.3 Classification

11.3.1 JMP Data Preliminaries for Classification

11.3.2 Example Data Sets

11.4 Classification by Logistic Regression

11.4.1 Logistic Regression in Fit Y by X

11.4.2 Logistic Regression in Fit Model

11.5 Classification by Discriminant Analysis

11.5.1 Discriminant Analysis Loadings

11.5.2 Stepwise Discriminant Analysis

11.6 Classification with Tabulated Data

11.7 Classifier Performance Verification

11.8 References

11.1 Introduction

Categorical data analysis involves developing models that consider categorical groupings. Although goodness-of-fit tests, discussed in Chapter 7, can be considered a form ...

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Biostatistics Using JMP

by Trevor Bihl