Introduction to Biostatistics with JMP

Contents
About This Book
	What Does This Book Cover?
	Is This Book for You?
	What Should You Know about the Examples?
	We Want to Hear from You
About The Author
Chapter 1: Some JMP Basics
	Introduction
	JMP Help
	Manual Data Entry
	Opening Excel Files
	Column Information – Value Ordering
	Formulas
	“Platforms”
	The Little Red Triangle is Your Friend!
	Row States – Color and Markers
	Row States – Hiding and Excluding
	Saving Scripts
	Saving Outputs – Journals & RTF Files
	Graph Builder
Chapter 2: Thinking Statistically
	Thinking Like a Statistician
	Summary
Chapter 3: Statistical Topics in Experimental Design
	Introduction
	Sample Size and Power
	Replication and Pseudoreplication
	Randomization and Preventing Bias
	Variation and Variables
Chapter 4: Describing Populations
	Introduction
	Population Description
	The Most Common Distribution – Normal or Gaussian
	Two Other Biologically Relevant Distributions
	The JMP Distribution Platform
	An Example: Big Class.jmp
	Parametric versus Nonparametric and “Normal Enough”
Chapter 5: Inferring and Estimating
	Introduction
	Inferential Estimation
	Confidence Intervals
	There Are Error Bars, and Then There Are Error Bars
	So, You Want to Put Error Bars on Your JMP Graphs…
Chapter 6: Null Hypothesis Significance Testing
	Introduction
	Biological Versus Statistical Ho
	NHST Rationale
	Error Types
	A Case Study in JMP
Chapter 7: Tests on Frequencies: Analyzing Rates and Proportions
	Introduction
	Y.O.D.A. Assessment
	One-way Chi-Square Tests and Mendel’s Peas
	Two-way Chi-Square Tests and Piscine Brain Worms
Chapter 8: Tests on Frequencies: Odds Ratios and Relative Risk
	Introduction
	Experimental Design and Data Collection
	Relative Risk
	Odds Ratios
Chapter 9: Tests of Differences Between Two Groups
	Introduction
	Comparing Two Unrelated Samples and Bone Density
	Comparing Two Related Samples and Secondhand Smoke
Chapter 10: Tests of Differences Between More Than Two Groups
	Introduction
	Comparing Unrelated Data
	Comparing Related Data
Chapter 11: Tests of Association: Regression
	Introduction
	What Is Bivariate Linear Regression?
	What Is Regression?
	What Does Linear Regression Tell Us?
	What Are the Assumptions of Linear Regression?
	Is Your Weight Related to Your Fat?
	How Do You Identify Independent and Dependent Variables?
	It Is Difficult to Make Predictions, Especially About the Future
Chapter 12: Tests of Association: Correlation
	Introduction
	What Is Correlation?
	How Does It Work?
	What Can’t Correlation Do?
	How to Calculate Correlation Coefficients: An Eyepopping Example
Chapter 13: Modeling Trends: Multiple Regression
	Introduction
	What Is Multiple Regression?
	The Fit Model Platform Is Your Friend!
	Let’s Throw All of Them in…
	Stepwise
Chapter 14: Modeling Trends: Other Regression Models
	Introduction
	Modeling Nominal Responses
	It’s Not Linear! Now What?
	Predictions
Chapter 15: Modeling Trends: Generalized Linear Models
	Introduction
	What Are Generalized Linear Models?
	Why Use Generalized Linear Models?
	How to Use Generalized Linear Models
	The General Linear Model
	Binomial Generalized Linear Models
	Poisson Generalized Linear Models
Chapter 16: Design of Experiments (DOE)
	Introduction
	What Is DOE?
	The Goals of DOE
	But Why DOE?
	DOE Flow in JMP
	Modeling the Data
	The Practical Steps for a DOE
	A DOE Example Start to Finish in JMP
Chapter 17: Survival Analysis
	Introduction
	So, What Is It?
	Comparing Survival with Kaplan-Meier Curves
	Modeling Survival
	Quantitating Survival: Hazard Ratios
Chapter 18: Hindrances to Data Analysis
	Introduction
	Hindrance #1: Outliers
	Hindrance #2: “Unclean” Data
	Hindrance #3: Sample Size and Power