Statistical Data Analysis for Ocean and Atmospheric Sciences

Includes a Data Disk Designed to Be Used as a Minitab File.


  • H. Thiebaux, National Science Foundation

Studies of local and global phenomena generate descriptions which require statistical analysis. In this text, H. Jean Thiebaux presents a succinct yet comprehensive review of the fundamentals of statistics as they pertain to studies in oceanic and atmospheric sciences. The text includes an accompanying disk with compatible Minitab sample data. Together, this volume and the included data provide insights into the basics of statistical inference, data analysis, and distributional models of variability. Oceanographers, meteorologists, marine biologists, and other environmental scientists will find this book of great value as a statistical tool for their continuing studies.
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Oceanographers, meteorologists, and other physical scientists who analyze data from marine and atmospheric systems.


Book information

  • Published: November 1994
  • ISBN: 978-0-12-686925-5


"Its innovative style, real-life examples and exercises at the conclusion of chapters, should be found interesting and valuable by readers."
--Zbigniew Sorbjan, University of Oklahoma, in PAGEOPH

"The text provides a good coverage of the classical distributions of traditional statistics....Overall, the author is to be congratulated for presenting a human oriented, rather than a robot oriented, book about statistics..."

Table of Contents

Statistical Analysis and Inference in Science: The Art of Reaching Conclusions at the Interface of Theory and Observation. Data and Data Management: What We Have to Go On or Accumulated Records of Observations and Their Expeditious Reorganization. Descriptive Statistics: First Impressions or Sketching Features of Observed Systems with Data. The Foundations of Inference: Probability Models as Descriptions of Research Outcomes. Stochastic Variables andthe Identification of Their Distributions: Distilling Uncertainty. The Exponential and Uniform Distributions: Describing Uncertainty in Time and Space. The Normal Distributions: Good Approximations for Many Composite Variables. AnalyzingVariability: Establishing Differences between Means and between Variances. Testing Hypotheses: Dealing with the Generic Critic while Establishing Powerful Support for New Ideas. Linear Regression: Analyzing an Influence Network. Bootstrapping: Scientific Inference when None of the Above Apply. Chapter Exercises. References. Subject Index.