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Index
About This Book
Why Am I Reading This Book?
What Does This Book Cover?
Is This Book for You?
What Should You Know about the Examples?
Software Used to Develop the Book’s Content
Example Code and Data and Chapter Exercises
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
Step One: What Is Your Objective?
Step Two: What Type of Data Do You Have?
Step Three (The Forgotten Step): Check Method Assumptions!
Summary
Chapter 3: Statistical Topics in Experimental Design
Introduction
Sample Size and Power
Power
Power Analyses
Power Examples
Replication and Pseudoreplication
Randomization and Preventing Bias
Variation and Variables
Some Definitions
Relationships Between Variables
Just to Make Life Interesting…
Chapter 4: Describing Populations
Introduction
Population Description
Central Tendency (Location)
Sample Variation
Frequency Distributions
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…
Graph Platform
Graph Builder Platform
Chapter 6: Null Hypothesis Significance Testing
Introduction
Biological Versus Statistical Ho
NHST Rationale
p-Values
p-Value Interpretation
A Tale of Tails…
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
Background and Data
Data Entry into JMP
Analysis
Interpretation and Statistical Conclusions
Two-way Chi-Square Tests and Piscine Brain Worms
Background and Data
Data Entry into JMP
Analysis
Interpretation and Statistical Conclusions
Chapter 8: Tests on Frequencies: Odds Ratios and Relative Risk
Introduction
Experimental Design and Data Collection
Prospective Design
Retrospective Design
Relative Risk
Definition and Calculation
Interpretation of Relative Risk
Hormone Replacement Therapy: Yea or Nay?
Odds Ratios
Definition and Calculation
Interpretation of the Odds Ratio
Odds versus Probability
Renal Cell Cancer and Smoking
Chapter 9: Tests of Differences Between Two Groups
Introduction
Comparing Two Unrelated Samples and Bone Density
Applying Statistical Strategy
Preferences of Use
Null Hypotheses
The Categorical Variable and Data Format
Let’s Do It Already!
The Nonparametric Alternative
Comparing Two Related Samples and Secondhand Smoke
Blowing Smoke
Clearing the Fog
But Wait! There’s More!
Chapter 10: Tests of Differences Between More Than Two Groups
Introduction
Comparing Unrelated Data
Why not…?
So how…?
Can you…?
Our Strategy Applied
How Do You Read the Reeds?
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
Choosing the Correlation Coefficient
Calculations (Finally!)
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?
The Underlying Forms
Why Use Generalized Linear Models?
How to Use Generalized Linear Models
The General Linear Model
GLM Assumptions
Reading the Output: Questions Answered
Is Weight a Function of… (a GLM Example)
Binomial Generalized Linear Models
Assumptions of the Binomial GLZM
How Severe Is It?
Poisson Generalized Linear Models
Poisson Distributions
Counting Nodes
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
Step 1: State and Document Your Objective
Step 2: Select the Variables, Factors, and Models to Support the Objective
Step 3: Create a Design to Support the Model
Step 4: Collect the Data Based on the Design
Step 5: Execute the Analysis with the Software
Step 6: Verify the Model with Checkpoints
Step 7: Report and Document Your Entire Experiment
A DOE Example Start to Finish in JMP
Chapter 17: Survival Analysis
Introduction
So, What Is It?
A Primary Problem or Consideration
The Solution: Censoring
Comparing Survival with Kaplan-Meier Curves
Modeling Survival
Quantitating Survival: Hazard Ratios
Chapter 18: Hindrances to Data Analysis
Introduction
Hindrance #1: Outliers
Causes
Detection
Hindrance #2: “Unclean” Data
Definition and Causes
Cleanup Operations
Recoding with Grouping
Hindrance #3: Sample Size and Power
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