Ken Gerow, Jorge Navarro Alberto

Essentials of Statistics for Research (eBook, PDF)

• Format: PDF

Produktbeschreibung

Essentials of Statistics for Research offers a working introduction to essential statistical methods through an accessible conceptual approach without excessive mathematical details. The book emphasizes the importance of good judgment when choosing analysis approaches and illustrates the statistical analysis process through numerous examples. At its core, this text demonstrates how analysis should serve science and illuminate the stories contained within data.

Key Features

• Provides conceptual foundations of a practitioner's statistical toolkit, focusing on the role of normality, hypothesis tests, and confidence intervals
• Presents regression methods as core analytical tools while also covering t-based methods for comparing means among groups
• Demonstrates how logarithmic transformations capture relativity in relationships (such as exponential increase) rather than simply meeting statistical assumptions
• Includes over 100 graphs and visual representations to enhance understanding of statistical concepts
• Written in an engaging first-person voice that positions the authors as fellow learners alongside the reader
• Emphasizes stories, examples, and practical applications over abstract theory

This book is designed for researchers across a broad range of disciplines, from graduate students beginning their research journey to experienced professionals seeking a refresher on statistical methods. The accessible approach makes it particularly valuable for those who need to understand and apply statistical concepts without getting lost in mathematical complexity. Readers will gain practical knowledge they can immediately apply to their own research questions and data analysis challenges.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 225
• Erscheinungstermin: 5. Dezember 2025
• Englisch
• ISBN-13: 9781040583579
• Artikelnr.: 75704971

Autorenporträt

Ken Gerow, PhD, recently retired from the University of Wyoming, where, as a professor of statistics for over thirty years, he taught statistics to quantitative scientists from many disciplines. Dr. Gerow earned his PhD degree in Statistics at Cornell University. He is the author or a coauthor of over ninety research articles, books, and book chapters, in topics ranging from the molecular and cellular world to the visible world around us (plant, animal, and human systems). Ken considers himself to be a parasitic biologist because he only publishes with other people's data.

Jorge A. Navarro Alberto, PhD, is a professor emeritus at the Autonomous University of Yucatán, México, where he specialized in ecological and environmental statistics research. Dr. Navarro Alberto earned his PhD degree in Statistics at the University of Otago, New Zealand. His academic career spanned more than 36 years teaching statistics for biologists, marine biologists, and natural resource managers in Mexico, and as a visiting professor at the University of Wyoming, with a vast experience in teaching multivariate analysis courses for life scientists. He is the co-author of the last edition of the book Randomization, Bootstrap and Monte Carlo Methods in Biology, and the co-editor of Introduction to Ecological Sampling, published by CRC Press. After retirement, Jorge is still active in the professional and academic arenas, working as a (more relaxed) part-time statistical consultant, and as one of the associate editors of the international journal, Environmental and Ecological Statistics. He also member of the Mexican representation at the International Statistical Literacy Project, Finland.

Inhaltsangabe

1. Introduction. 2. Data Concepts. 3. The Statistical Law of Gravity (a.k.a
the Central Limit Theorem). 4. Using the data: Introducing Hypothesis Tests
and Confidence Intervals. 5. How Confidence Intervals and tests play well
together (or not). 6. Introduction to Simple Linear Regression. 7.
Regression by the Numbers: Making Sense of and Using the Output. 8.
Background Reading and a Few New Ideas. 9. The Use of Logarithms in
Regression Models. 10. Introduction to Multiple Regression. 11. Multiple
Regression Examples. 12. Two Essays on Multiple Regression. 13.
Introduction to Logistic Regression. 14. One and Two Sample Methods for
Means and Proportions. 15. Relative Inference for Means From Two Samples:
Introducing the Bootstrap. 16. A Brief Introduction to ANOVA. 17. Response
Feature Analyses for Repeated Measures Data. 18. Epilogue.