# Statistical Data Analysis for Ocean and Atmospheric Sciences

• ID: 1763682
• Book
• 247 Pages
• Elsevier Science and Technology
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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.

- Specifically designed for students of the ocean and atmospheric sciences- Contains a disk containing files of real ocean and atmospheric data, in universal ASCII format, on which many of the exercises are based- Provides succinct yet comprehensive coverage- Designed to teach students statistical methods with the scientific realism of computer analysis and statistical inference

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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.
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