Biostatistics Using JMP

Biostatistics Using JMP

by Trevor Bihl
Released October 2017
Publisher(s): SAS Institute
ISBN: 9781635262414

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Book description

Analyze your biostatistics data with JMP!

Trevor Bihl's Biostatistics Using JMP: A Practical Guide provides a practical introduction on using JMP, the interactive statistical discovery software, to solve biostatistical problems. Providing extensive breadth, from summary statistics to neural networks, this essential volume offers a comprehensive, step-by-step guide to using JMP to handle your data.

The first biostatistical book to focus on software, Biostatistics Using JMP discusses such topics as data visualization, data wrangling, data cleaning, histograms, box plots, Pareto plots, scatter plots, hypothesis tests, confidence intervals, analysis of variance, regression, curve fitting, clustering, classification, discriminant analysis, neural networks, decision trees, logistic regression, survival analysis, control charts, and metaanalysis.

Written for university students, professors, those who perform biological/biomedical experiments, laboratory managers, and research scientists, Biostatistics Using JMP provides a practical approach to using JMP to solve your biostatistical problems.

Table of contents

  1. Dedication
  2. Acknowledgments
  3. About This Book
  4. About the Author
  5. Chapter 1: Introduction
  6. 1.1 Background and Overview
  7. 1.2 Getting Started with JMP
  8. 1.3 General Outline
  9. 1.4 How to Use This Book
  10. 1.5 Reference
  11. Chapter 2: Data Wrangling: Data Collection
  12. 2.1 Introduction
  13. 2.2 Collecting Data from Files
    1. 2.2.1 JMP Native Files
    2. 2.2.2 SAS Format Files
    3. 2.2.3 Excel Spreadsheets
    4. 2.2.4 Text and CSV Format
  14. 2.3 Extracting Data from Internet Locations
    1. 2.3.1 Opening as Data
    2. 2.3.2 Opening as a Webpage
  15. 2.4 Data Modeling Types
    1. 2.4.1 Incorporating Expression and Contextual Data
  16. 2.5 References
  17. Chapter 3: Data Wrangling: Data Cleaning
  18. 3.1 Introduction
  19. 3.2 Tables
    1. 3.2.1 Stacking Columns
    2. 3.2.2 Basic Table Organization
    3. 3.2.3 Column Properties
  20. 3.3 The Sorted Array
  21. 3.4 Restructuring Data
    1. 3.4.1 Combining Columns
    2. 3.4.2 Separating Out a Column (Text to Columns)
    3. 3.4.3 Creating Indicator Columns
    4. 3.4.4 Grouping Inside Columns
  22. 3.5 References
  23. Chapter 4: Initial Data Analysis with Descriptive Statistics
  24. 4.1 Introduction
  25. 4.2 Histograms and Distributions
    1. 4.2.1 Histograms
    2. 4.2.2 Box Plots
    3. 4.2.3 Stem-and-Leaf Plots
    4. 4.2.4 Pareto Charts
  26. 4.3 Descriptive Statistics
    1. 4.3.1 Sample Mean and Standard Deviation
    2. 4.3.2 Additional Statistical Measures
  27. 4.4 References
  28. Chapter 5: Data Visualization Tools
  29. 5.1 Introduction
  30. 5.2 Scatter Plots
    1. 5.2.1 Coloring Points
    2. 5.2.2 Copying Better-Looking Figures
    3. 5.2.3 Multiple Scatter Plots
  31. 5.3 Charts
  32. 5.4 Multidimensional Plots
    1. 5.4.1 Parallel Plots
    2. 5.4.2 Cell Plots
  33. 5.5 Multivariate and Correlations Tool
    1. 5.5.1 Correlation Table
    2. 5.5.2 Correlation Heat Maps
    3. 5.5.3 Simple Statistics
    4. 5.5.4 Additional Multivariate Measures
  34. 5.6 Graph Builder and Custom Figures
    1. 5.6.1 Graph Builder Custom Colors
    2. 5.6.2 Incorporating Contextual Data
  35. 5.7 References
  36. Chapter 6: Rates, Proportions, and Epidemiology
  37. 6.1 Introduction
  38. 6.2 Rates
    1. 6.2.1 Crude Rates
    2. 6.2.2 Adjusted Rates
  39. 6.3 Geographic Visualizations
    1. 6.3.1 National Visualizations
    2. 6.3.2 County and Lower Level Visualizations
  40. 6.4 References
  41. Chapter 7: Statistical Tests and Confidence Intervals
  42. 7.1 Introduction
    1. 7.1.1 General Hypothesis Test Background
    2. 7.1.2 Selecting the Appropriate Method
  43. 7.2 Testing for Normality
    1. 7.2.1 Histogram Analysis
    2. 7.2.2 Normal Quantile/Probability Plot
    3. 7.2.3 Goodness-of-Fit Tests
    4. 7.2.4 Goodness-of-Fit for Other Distributions
  44. 7.3 General Hypothesis Tests
    1. 7.3.1 Z-Test Hypothesis Test of Mean
    2. 7.3.2 T-Test Hypothesis Test of Mean
    3. 7.3.3 Nonparametric Test of Mean (Wilcoxon Signed Rank)
    4. 7.3.4 Standard Deviation Hypothesis Test
    5. 7.3.5 Tests of Proportions
  45. 7.4 Confidence Intervals
    1. 7.4.1 Mean Confidence Intervals
    2. 7.4.2 Mean Confidence Intervals with Different Thresholds
    3. 7.4.3 Confidence Intervals for Proportions
  46. 7.5 Chi-Squared Analysis of Frequency and Contingency Tables
  47. 7.6 Two Sample Tests
    1. 7.6.1 Comparing Two Group Means
    2. 7.6.2 Paired Comparison, Matched Pairs
  48. 7.7 References
  49. Chapter 8: Analysis of Variance (ANOVA) and Design of Experiments (DoE)
  50. 8.1 Introduction
  51. 8.2 One-Way ANOVA
    1. 8.2.1 One-Way ANOVA with Fit Y by X
    2. 8.2.2 Means Comparison, LSD Matrix, and Connecting Letters
    3. 8.2.3 Fit Y by X Changing Significance Levels
    4. 8.2.4 Multiple Comparisons, Multiple One-Way ANOVAs
    5. 8.2.5 One-Way ANOVA via Fit Model
    6. 8.2.6 One-Way ANOVA for Unequal Group Sizes (Unbalanced)
  52. 8.3 Blocking
    1. 8.3.1 One-Way ANOVA with Blocking via Fit Y by X
    2. 8.3.2 One-Way ANOVA with Blocking via Fit Model
    3. 8.3.3 Note on Blocking
  53. 8.4 Multiple Factors
    1. 8.4.1 Experimental Design Considerations
    2. 8.4.2 Multiple ANOVA
    3. 8.4.3 Feature Selection and Parsimonious Models
  54. 8.5 Multivariate ANOVA (MANOVA) and Repeated Measures
    1. 8.5.1 Repeated Measures MANOVA Background
    2. 8.5.2 MANOVA in Fit Model
  55. 8.6 References
  56. Chapter 9: Regression and Curve Fitting
  57. 9.1 Introduction
  58. 9.2 Simple Linear Regression
    1. 9.2.1 Fit Y by X for Bivariate Fits (One X and One Y)
    2. 9.2.2 Special Fitting Tools
  59. 9.3 Multiple Regression
    1. 9.3.1 Fit Model
    2. 9.3.2 Stepwise Feature Selection
    3. 9.3.3 Analysis of Covariance (ANCOVA)
  60. 9.4 Nonlinear Curve Fitting and a Nonlinear Platform Example
  61. 9.5 References
  62. Chapter 10: Diagnostic Methods for Regression, Curve Fitting, and ANOVA
  63. 10.1 Introduction
  64. 10.2 Computing Residuals with Fit Y by X and Fit Model
    1. 10.2.1 Fit Y by X
    2. 10.2.2 Fit Model
  65. 10.3 Checking for Normality
  66. 10.4 Checking for Nonconstant Error Variance (Heteroscedasticity)
  67. 10.5 Checking for Outliers
  68. 10.6 Checking for Nonindependence
  69. 10.7 Multiple Factor Diagnostics
  70. 10.8 Nonlinear Fit Residuals
  71. 10.9 Developing Appropriate Models
  72. 10.10 References
  73. Chapter 11: Categorical Data Analysis
  74. 11.1 Introduction
  75. 11.2 Clustering
    1. 11.2.1 Hierarchical Clustering
    2. 11.2.2 K-means Clustering
  76. 11.3 Classification
    1. 11.3.1 JMP Data Preliminaries for Classification
    2. 11.3.2 Example Data Sets
  77. 11.4 Classification by Logistic Regression
    1. 11.4.1 Logistic Regression in Fit Y by X
    2. 11.4.2 Logistic Regression in Fit Model
  78. 11.5 Classification by Discriminant Analysis
    1. 11.5.1 Discriminant Analysis Loadings
    2. 11.5.2 Stepwise Discriminant Analysis
  79. 11.6 Classification with Tabulated Data
  80. 11.7 Classifier Performance Verification
  81. 11.8 References
  82. Chapter 12: Advanced Modeling Methods
  83. 12.1 Introduction
  84. 12.2 Principal Components and Factor Analysis
    1. 12.2.1 Principal Components in JMP
    2. 12.2.2 Dimensionality Assessment
    3. 12.2.3 Factor Analysis in JMP
  85. 12.3 Partial Least Squares
  86. 12.4 Decision Trees
    1. 12.4.1 Classification Decision Trees in JMP
    2. 12.4.2 Predictive Decision Trees in JMP
  87. 12.5 Artificial Neural Networks
    1. 12.5.1 Neural Network Architecture
    2. 12.5.2 Classification Neural Networks in JMP
    3. 12.5.3 Predictive Neural Networks in JMP
  88. 12.6 Control Charts
  89. 12.7 References
  90. Chapter 13: Survival Analysis
  91. 13.1 Introduction
  92. 13.2 Life Distributions
  93. 13.3 Kaplan-Meier Curves
    1. 13.3.1 Simple Survival Analysis
    2. 13.3.2 Multiple Groups
    3. 13.3.3 Censoring
    4. 13.3.4 Proportional Hazards
  94. 13.4 References
  95. Chapter 14: Collaboration and Additional Functionality
  96. 14.1 Introduction
  97. 14.2 Saving Scripts and SAS Coding
    1. 14.2.1 Saving Scripts to Data Table
    2. 14.2.2 SAS Coding Functionality
  98. 14.3 Collaboration
    1. 14.3.1 Journals
    2. 14.3.2 Web Reports
  99. 14.4 Add-Ins
    1. 14.4.1 Finding Add-Ins
    2. 14.4.2 Developing Add-Ins
    3. 14.4.3 Example Add-In: Forest Plot / Meta-analysis
    4. 14.4.4 Add-In Version Control
  100. 14.5 References
  101. Index

Product information

  • Title: Biostatistics Using JMP
  • Author(s): Trevor Bihl
  • Release date: October 2017
  • Publisher(s): SAS Institute
  • ISBN: 9781635262414