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Say goodbye to dry presentations, grueling formulas, and abstract theories that would put Einstein to sleep -- now there's an easier way to master the disciplines you really need to know. McGraw-Hill's Demystified Series teaches complex subjects in a unique, easy-to-absorb manner, and is perfect for users without formal training or unlimited time. They're also the most time-efficient, interestingly written "brush-ups" you can find. Organized as self-teaching guides, they come complete with key points, background information, questions at the end of each chapter, and even final exams. You'll be able to learn more in less time, evaluate your areas of strength and weakness and reinforce your knowledge and confidence. Popular science/hobbyist writer Stan Gibilisco covers every important aspect of basic (algebra-based) statistics, including: notation and jargon, describing, tables, graphs, randomness and uncertainty, probability principles, distributions, obtaining and interpreting data, correlation, causation, and more.
Stan Gibilisco is one of McGraw-Hill's most prolific and popular authors. His clear, reader-friendly writing style makes his electronics books accessible to a wide audience, and his background in mathematics and research makes him an ideal editor for professional handbooks. He is the author of the TAB Encyclopedia of Electronics for Technicians and Hobbyists, Teach Yourself Electricity and Electronics, and The Illustrated Dictionary of Electronics. Booklist named his McGraw-Hill Encyclopedia of Personal Computing a "Best Reference" of 1996.
Table of Contents
PART ONE: IT’S ALL IN THE DATA
Chapter 1. Notation and Jargon
Chapter 2. How Data is Described
Chapter 3. Creating and Interpreting Tables
Chapter 4. Creating and Interpreting Graphs
Test: Part One
PART TWO: TAKING YOUR CHANCES
Chapter 5. Randomness and Uncertainty
Chapter 6. Probability Principles
Chapter 7. Discrete Distributions
Chapter 8. Continuous Distributions
Test: Part Two
PART THREE: GETTING IT RIGHT
Chapter 9. Obtaining Data
Chapter 10. Interpreting Data
Chapter 11. Correlation, Causation, and Prediction