Biostatistics Using JMP

Trevor Bihl

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.

Sujets

• Medical statistics
• Pharmaceutical drug
• Design of experiments

Informations

• Publié par: SAS Institute
• Date de parution: 03 octobre 2017
• Nombre de lectures: 0
• EAN13: 9781635262414
• Langue: English
• Poids de l'ouvrage: 21 Mo

Extrait

Biostatistics Using JMP
A Practical Guide
Trevor Bihl
The correct bibliographic citation for this manual is as follows: Bihl, Trevor. 2017. Biostatistics Using JMP : A Practical Guide . Cary, NC: SAS Institute Inc .
Biostatistics Using JMP : A Practical Guide
Copyright 2017, SAS Institute Inc., Cary, NC, USA ISBN 978-1-62960-383-4 (Hard copy) ISBN 978-1-63526-241-4 (EPUB) ISBN 978-1-63526-242-1 (MOBI) ISBN 978-1-63526-243-8 (PDF)
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U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government s rights in Software and documentation shall be only those set forth in this Agreement.
SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414
September 2017
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration.
Other brand and product names are trademarks of their respective companies.
SAS software may be provided with certain third-party software, including but not limited to open-source software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to http://support.sas.com/thirdpartylicenses .
Dedication
To the memory of Gregory Boivin, DVM, MBA, who provided encouragement and much needed data for this endeavor
Contents
Dedication
Acknowledgments
About This Book
About the Author
Chapter 1: Introduction
1.1 Background and Overview
1.2 Getting Started with JMP
1.3 General Outline
1.4 How to Use This Book
1.5 Reference
Chapter 2: Data Wrangling: Data Collection
2.1 Introduction
2.2 Collecting Data from Files
2.2.1 JMP Native Files
2.2.2 SAS Format Files
2.2.3 Excel Spreadsheets
2.2.4 Text and CSV Format
2.3 Extracting Data from Internet Locations
2.3.1 Opening as Data
2.3.2 Opening as a Webpage
2.4 Data Modeling Types
2.4.1 Incorporating Expression and Contextual Data
2.5 References
Chapter 3: Data Wrangling: Data Cleaning
3.1 Introduction
3.2 Tables
3.2.1 Stacking Columns
3.2.2 Basic Table Organization
3.2.3 Column Properties
3.3 The Sorted Array
3.4 Restructuring Data
3.4.1 Combining Columns
3.4.2 Separating Out a Column (Text to Columns)
3.4.3 Creating Indicator Columns
3.4.4 Grouping Inside Columns
3.5 References
Chapter 4: Initial Data Analysis with Descriptive Statistics
4.1 Introduction
4.2 Histograms and Distributions
4.2.1 Histograms
4.2.2 Box Plots
4.2.3 Stem-and-Leaf Plots
4.2.4 Pareto Charts
4.3 Descriptive Statistics
4.3.1 Sample Mean and Standard Deviation
4.3.2 Additional Statistical Measures
4.4 References
Chapter 5: Data Visualization Tools
5.1 Introduction
5.2 Scatter Plots
5.2.1 Coloring Points
5.2.2 Copying Better-Looking Figures
5.2.3 Multiple Scatter Plots
5.3 Charts
5.4 Multidimensional Plots
5.4.1 Parallel Plots
5.4.2 Cell Plots
5.5 Multivariate and Correlations Tool
5.5.1 Correlation Table
5.5.2 Correlation Heat Maps
5.5.3 Simple Statistics
5.5.4 Additional Multivariate Measures
5.6 Graph Builder and Custom Figures
5.6.1 Graph Builder Custom Colors
5.6.2 Incorporating Contextual Data
5.7 References
Chapter 6: Rates, Proportions, and Epidemiology
6.1 Introduction
6.2 Rates
6.2.1 Crude Rates
6.2.2 Adjusted Rates
6.3 Geographic Visualizations
6.3.1 National Visualizations
6.3.2 County and Lower Level Visualizations
6.4 References
Chapter 7: Statistical Tests and Confidence Intervals
7.1 Introduction
7.1.1 General Hypothesis Test Background
7.1.2 Selecting the Appropriate Method
7.2 Testing for Normality
7.2.1 Histogram Analysis
7.2.2 Normal Quantile/Probability Plot
7.2.3 Goodness-of-Fit Tests
7.2.4 Goodness-of-Fit for Other Distributions
7.3 General Hypothesis Tests
7.3.1 Z-Test Hypothesis Test of Mean
7.3.2 T-Test Hypothesis Test of Mean
7.3.3 Nonparametric Test of Mean (Wilcoxon Signed Rank)
7.3.4 Standard Deviation Hypothesis Test
7.3.5 Tests of Proportions
7.4 Confidence Intervals
7.4.1 Mean Confidence Intervals
7.4.2 Mean Confidence Intervals with Different Thresholds
7.4.3 Confidence Intervals for Proportions
7.5 Chi-Squared Analysis of Frequency and Contingency Tables
7.6 Two Sample Tests
7.6.1 Comparing Two Group Means
7.6.2 Paired Comparison, Matched Pairs
7.7 References
Chapter 8: Analysis of Variance (ANOVA) and Design of Experiments (DoE)
8.1 Introduction
8.2 One-Way ANOVA
8.2.1 One-Way ANOVA with Fit Y by X
8.2.2 Means Comparison, LSD Matrix, and Connecting Letters
8.2.3 Fit Y by X Changing Significance Levels
8.2.4 Multiple Comparisons, Multiple One-Way ANOVAs
8.2.5 One-Way ANOVA via Fit Model
8.2.6 One-Way ANOVA for Unequal Group Sizes (Unbalanced)
8.3 Blocking
8.3.1 One-Way ANOVA with Blocking via Fit Y by X
8.3.2 One-Way ANOVA with Blocking via Fit Model
8.3.3 Note on Blocking
8.4 Multiple Factors
8.4.1 Experimental Design Considerations
8.4.2 Multiple ANOVA
8.4.3 Feature Selection and Parsimonious Models
8.5 Multivariate ANOVA (MANOVA) and Repeated Measures
8.5.1 Repeated Measures MANOVA Background
8.5.2 MANOVA in Fit Model
8.6 References
Chapter 9: Regression and Curve Fitting
9.1 Introduction
9.2 Simple Linear Regression
9.2.1 Fit Y by X for Bivariate Fits (One X and One Y)
9.2.2 Special Fitting Tools
9.3 Multiple Regression
9.3.1 Fit Model
9.3.2 Stepwise Feature Selection
9.3.3 Analysis of Covariance (ANCOVA)
9.4 Nonlinear Curve Fitting and a Nonlinear Platform Example
9.5 References
Chapter 10: Diagnostic Methods for Regression, Curve Fitting, and ANOVA
10.1 Introduction
10.2 Computing Residuals with Fit Y by X and Fit Model
10.2.1 Fit Y by X
10.2.2 Fit Model
10.3 Checking for Normality
10.4 Checking for Nonconstant Error Variance (Heteroscedasticity)
10.5 Checking for Outliers
10.6 Checking for Nonindependence
10.7 Multiple Factor Diagnostics
10.8 Nonlinear Fit Residuals
10.9 Developing Appropriate Models
10.10 References
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
Chapter 12: Advanced Modeling Methods
12.1 Introduction
12.2 Principal Components and Factor Analysis
12.2.1 Principal Components in JMP
12.2.2 Dimensionality Assessment
12.2.3 Factor Analysis in JMP
12.3 Partial Least Squares
12.4 Decision Trees
12.4.1 Classification Decision Trees in JMP
12.4.2 Predictive Decision Trees in JMP
12.5 Artificial Neural Networks
12.5.1 Neural Network Architecture
12.5.2 Classification Neural Networks in JMP
12.5.3 Predictive Neural Networks in JMP
12.6 Control Charts
12.7 References
Chapter 13: Survival Analysis
13.1 Introduction
13.2 Life Distributions
13.3 Kaplan-Meier Curves
13.3.1 Simple Survival Analysis
13.3.2 Multiple Groups
13.3.3 Censoring
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