The Graduate Student's Guide to Statistics
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The Graduate Student's Guide to Statistics

A clear description of how to do statistics for your thesis or dissertation

About the Book

As a graduate student writing a thesis or dissertation, you have one primary goal:  Get the required signatures from your committee members.  Then you can graduate and move on with life!

This book is designed to help you do that effectively and in as short a time as humanly possible.

See more at the blog!

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About the Author

Jerome V. Braun, Ph.D.
Jerome V. Braun, Ph.D.

Jerome V. Braun is a Ph.D. Statistician with over 15 years of general statistical consulting experience.  He has consulted with well over 800 clients spanning academia, industry, and government.

He has extensive experience in nonclinical and preclinical statistics, in agricultural field trial statistics, and in Phase I/II clinical trials.  His experience includes over six years as Senior Statistician at the University of California at Davis and three years at Genentech, Inc. (and a few months at Syntex Corporation).

His applied skills include data processing, statistical design, statistical modeling, statistical reporting, and project management.  His programming skills include SAS (BASE, STAT, DATA step, Macro, ODS), R/S-PLUS, Perl, FORTRAN, BASIC, BUGS, C, Matlab, Mathematica, GAUSS, HTML, Javascript, and SQL.  He has experience with Minitab, SPSS, JMP, Systat, and many lesser-known packages.

He is co-author on around a dozen peer-reviewed publications in a variety of fields, has taught several college-level courses, worked as co-principal investigator, and has served as an expert witness in statistical analysis.

Table of Contents

  • Preface
  • Acknowledgements
  • About the Author
  • Introduction
    • This Book Is For Graduate Students
    • The Absolute Goal
    • Some Problem Personality Types
    • Time
    • Topic Discussed in This Book
  • Triage
    • End of Normative Time
    • Middle of Normative Time
    • Start of Normative Time
  • A Note to the Reader
    • Choices of Statistical Methodology
    • Criticism of Statistical Methodology
    • Practical Ethics
  • Organizing Your Work
    • A Caution
    • The Artifacts of a Carefully Planned Research Program
    • Organizing Your Computer Files
    • Literate Programming
    • Notes
    • Back-up
  • Workflow
    • Information Flow
    • The Principle of Analyzing Only One Data Source
  • Communications with your Advisor and Committee
    • Choosing an Advisor and a Committee
    • Managing Communication
  • Consulting with a Statistician
    • A Generality
    • After Your Study is Done
    • Outcome of Statistical Consultation
    • Having the Statistics Done
  • Research Proposal
    • Research Hypothesis
    • Pilot Study
    • Research Protocol
    • Timelines
    • Research Organism
    • Statistical Analysis as the Centerpiece
    • Read More
  • Design
    • Design Considerations
    • Repeated Measures
    • Read More
  • Sample Size
    • The Basics
    • How Not to Figure Sample Size
    • General Strategy for Getting a Sample Size
    • Some Formal Stuff
  • Randomization
    • What is Randomness?
    • Experimental Units
    • Randomization Restrictions
  • Replication
    • Technical Replicates
    • Biological Replicates
    • Repeats versus Replicates
    • Pseudoreplication
    • Hierarchical Models
    • What to do?
  • Statistical Packages
    • Common Statistical Packages
    • The Problem with Statistical Packages
    • Excel
  • Statistical Languages
    • The Upside of Statistical Languages
    • R
    • SAS
  • Statistical Analysis Plan
    • Mapping the Statistical Analysis to the Research Questions
    • Writing the Statistical Analysis Plan
    • Example of a Simple Statistical Analysis Plan
    • A Work Plan
    • Timeline
  • Data Collection
    • Paper Forms
    • Excel as a Data Collection Tool
    • Custody of Samples
  • Data Structure
    • Bad Data Structures
    • Good Data Structures
    • Data Key
  • Data Cleaning and Normalization
    • The Unescapable Chores
    • Analysis Data
    • Basics of Data Cleaning
    • Variable Names
  • Data Quality Assurance
    • Basic Data Quality Checks
    • Handling Outliers
    • Read More
  • Tables, Listings, and Figures
    • Presentation of Non-Results
    • Tables
  • Manuscript Layout
    • Manuscript Organization
    • Results Section
    • Discussion Section
    • Statistical Methods Section
  • Writing the Manuscript
    • Laying out the Manuscript
    • The Rough Draft
    • First Draft
    • Draft Final
    • Formatting
  • Weasel Words
    • Statistical Significance
    • Lack of Statistical Significance
  • Dealing with Reviewer Comments
    • Anonymous Reviewers
    • Committee Member Reviewers
    • Confusing or Non-helpful Reviewers
    • Strategy
    • Responding to Reviewer Comments and Criticism
    • After-thought
  • Linear Models
    • Why Use Linear Models?
    • What are Linear Models?
    • An Important Distinction
    • Normality
    • Checking Assumptions
  • Data Transformation
    • The General Purpose of Data Transformations
    • Untransforming
    • Logarithmic Transformation
    • Square Root Transformation
    • Arcsine Square Root Transformation
    • Inverse Transformation
    • Implementing Transformations
    • Read More
  • Analysis of Variance
    • General Remarks
    • How to Report ANOVA Results
    • Type I, II, III, and IV Tests
    • Sample Main Effects Language
    • Sample Interaction Effects Language
    • Contrasts and Pairwise Comparisons
    • Sample Statistical Methods Language
    • Least Squares Means
    • Read More
  • Regression
    • Handling Multiple Response Variables
    • Model Selection
    • Regression Issues
    • Presenting Regression Results
  • Contingency Tables
    • General Remarks
    • How to Report Contingency Table Results
    • Pairwise Comparisons
    • Graphical Displays
    • Analyzing Larger Contingency Tables
  • References

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