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Introduction to Biostatistics with JMP

Author Steve Figard
Release Date: 2019/10/01
ISBN: 9781635267181
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Book Description

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,

Table of Contents

  1. About This Book
    1. Why Am I Reading This Book?
    2. What Does This Book Cover?
    3. Is This Book for You?
    4. What Should You Know about the Examples?
      1. Software Used to Develop the Book’s Content
      2. Example Code and Data and Chapter Exercises
    5. We Want to Hear from You
  2. About The Author
  3. Chapter 1: Some JMP Basics
    1. Introduction
    2. JMP Help
    3. Manual Data Entry
    4. Opening Excel Files
    5. Column Information – Value Ordering
    6. Formulas
    7. “Platforms”
    8. The Little Red Triangle is Your Friend!
    9. Row States – Color and Markers
    10. Row States – Hiding and Excluding
    11. Saving Scripts
    12. Saving Outputs – Journals & RTF Files
    13. Graph Builder
  4. Chapter 2: Thinking Statistically
    1. Thinking Like a Statistician
      1. Step One: What Is Your Objective?
      2. Step Two: What Type of Data Do You Have?
      3. Step Three (The Forgotten Step): Check Method Assumptions!
    2. Summary
  5. Chapter 3: Statistical Topics in Experimental Design
    1. Introduction
    2. Sample Size and Power
      1. Power
      2. Power Analyses
      3. Power Examples
    3. Replication and Pseudoreplication
    4. Randomization and Preventing Bias
    5. Variation and Variables
      1. Some Definitions
      2. Relationships Between Variables
      3. Just to Make Life Interesting…
  6. Chapter 4: Describing Populations
    1. Introduction
    2. Population Description
      1. Central Tendency (Location)
      2. Sample Variation
      3. Frequency Distributions
    3. The Most Common Distribution – Normal or Gaussian
    4. Two Other Biologically Relevant Distributions
    5. The JMP Distribution Platform
    6. An Example: Big Class.jmp
    7. Parametric versus Nonparametric and “Normal Enough”
  7. Chapter 5: Inferring and Estimating
    1. Introduction
    2. Inferential Estimation
    3. Confidence Intervals
    4. There Are Error Bars, and Then There Are Error Bars
    5. So, You Want to Put Error Bars on Your JMP Graphs…
      1. Graph Platform
      2. Graph Builder Platform
  8. Chapter 6: Null Hypothesis Significance Testing
    1. Introduction
    2. Biological Versus Statistical Ho
    3. NHST Rationale
    4. p-Values
      1. p-Value Interpretation
      2. A Tale of Tails…
    5. Error Types
    6. A Case Study in JMP
  9. Chapter 7: Tests on Frequencies: Analyzing Rates and Proportions
    1. Introduction
    2. Y.O.D.A. Assessment
    3. One-way Chi-Square Tests and Mendel’s Peas
      1. Background and Data
      2. Data Entry into JMP
      3. Analysis
      4. Interpretation and Statistical Conclusions
    4. Two-way Chi-Square Tests and Piscine Brain Worms
      1. Background and Data
      2. Data Entry into JMP
      3. Analysis
      4. Interpretation and Statistical Conclusions
  10. Chapter 8: Tests on Frequencies: Odds Ratios and Relative Risk
    1. Introduction
    2. Experimental Design and Data Collection
      1. Prospective Design
      2. Retrospective Design
    3. Relative Risk
      1. Definition and Calculation
      2. Interpretation of Relative Risk
      3. Hormone Replacement Therapy: Yea or Nay?
    4. Odds Ratios
      1. Definition and Calculation
      2. Interpretation of the Odds Ratio
      3. Odds versus Probability
      4. Renal Cell Cancer and Smoking
  11. Chapter 9: Tests of Differences Between Two Groups
    1. Introduction
    2. Comparing Two Unrelated Samples and Bone Density
      1. Applying Statistical Strategy
      2. Preferences of Use
      3. Null Hypotheses
      4. The Categorical Variable and Data Format
      5. Let’s Do It Already!
      6. The Nonparametric Alternative
    3. Comparing Two Related Samples and Secondhand Smoke
      1. Blowing Smoke
      2. Clearing the Fog
      3. But Wait! There’s More!
  12. Chapter 10: Tests of Differences Between More Than Two Groups
    1. Introduction
    2. Comparing Unrelated Data
      1. Why not…?
      2. So how…?
      3. Can you…?
      4. Our Strategy Applied
      5. How Do You Read the Reeds?
    3. Comparing Related Data
  13. Chapter 11: Tests of Association: Regression
    1. Introduction
    2. What Is Bivariate Linear Regression?
    3. What Is Regression?
    4. What Does Linear Regression Tell Us?
    5. What Are the Assumptions of Linear Regression?
    6. Is Your Weight Related to Your Fat?
    7. How Do You Identify Independent and Dependent Variables?
    8. It Is Difficult to Make Predictions, Especially About the Future
  14. Chapter 12: Tests of Association: Correlation
    1. Introduction
    2. What Is Correlation?
    3. How Does It Work?
    4. What Can’t Correlation Do?
    5. How to Calculate Correlation Coefficients: An Eyepopping Example
      1. Choosing the Correlation Coefficient
      2. Calculations (Finally!)
  15. Chapter 13: Modeling Trends: Multiple Regression
    1. Introduction
    2. What Is Multiple Regression?
    3. The Fit Model Platform Is Your Friend!
    4. Let’s Throw All of Them in…
    5. Stepwise
  16. Chapter 14: Modeling Trends: Other Regression Models
    1. Introduction
    2. Modeling Nominal Responses
    3. It’s Not Linear! Now What?
    4. Predictions
  17. Chapter 15: Modeling Trends: Generalized Linear Models
    1. Introduction
    2. What Are Generalized Linear Models?
      1. The Underlying Forms
    3. Why Use Generalized Linear Models?
    4. How to Use Generalized Linear Models
    5. The General Linear Model
      1. GLM Assumptions
      2. Reading the Output: Questions Answered
      3. Is Weight a Function of… (a GLM Example)
    6. Binomial Generalized Linear Models
      1. Assumptions of the Binomial GLZM
      2. How Severe Is It?
    7. Poisson Generalized Linear Models
      1. Poisson Distributions
      2. Counting Nodes
  18. Chapter 16: Design of Experiments (DOE)
    1. Introduction
    2. What Is DOE?
    3. The Goals of DOE
    4. But Why DOE?
    5. DOE Flow in JMP
    6. Modeling the Data
    7. The Practical Steps for a DOE
      1. Step 1: State and Document Your Objective
      2. Step 2: Select the Variables, Factors, and Models to Support the Objective
      3. Step 3: Create a Design to Support the Model
      4. Step 4: Collect the Data Based on the Design
      5. Step 5: Execute the Analysis with the Software
      6. Step 6: Verify the Model with Checkpoints
      7. Step 7: Report and Document Your Entire Experiment
    8. A DOE Example Start to Finish in JMP
  19. Chapter 17: Survival Analysis
    1. Introduction
    2. So, What Is It?
      1. A Primary Problem or Consideration
      2. The Solution: Censoring
    3. Comparing Survival with Kaplan-Meier Curves
    4. Modeling Survival
    5. Quantitating Survival: Hazard Ratios
  20. Chapter 18: Hindrances to Data Analysis
    1. Introduction
    2. Hindrance #1: Outliers
      1. Causes
      2. Detection
    3. Hindrance #2: “Unclean” Data
      1. Definition and Causes
      2. Cleanup Operations
      3. Recoding with Grouping
    4. Hindrance #3: Sample Size and Power
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