Introduction to Biostatistics with JMP

✍ Scribed by Steve Figard

• Publisher: SAS Institute
• Year: 2019
• Tongue: English
• Leaves: 288
• Category: Library

✦ Synopsis

Explore biostatistics using JMP in this refreshing introduction

Presented in an easy-to-understand way, Introduction to Biostatistics with JMP® introduces undergraduate students in the biological sciences to the most commonly used (and misused) statistical methods that they will need to analyze their experimental data using JMP. It covers many of the basic topics in statistics using biological examples for exercises so that the student biologists can see the relevance to future work in the problems addressed.

The book starts by teaching students how to become confident in executing the right analysis by thinking like a statistician then moves into the application of specific tests. Using the powerful capabilities of JMP, the book addresses problems requiring analysis by chi-square tests, t tests, ANOVA analysis, various regression models, DOE, and survival analysis. Topics of particular interest to the biological or health science field include odds ratios, relative risk, and survival analysis.

The author uses an engaging, conversational tone to explain concepts and keep readers interested in learning more. The book aims to create bioscientists who can competently incorporate statistics into their investigative toolkits to solve biological research questions as they arise.

✦ Table of Contents

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