The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics
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More About This Title The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics

English

A friendly and accessible approach to applying statistics in the real world

With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way.

Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool.  In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include:

• Descriptive statistics
• Analysis of variance
• Probability and sample distributions
• Confidence intervals
• Hypothesis tests
• Regression
• Statistical correlation
• Data collection
• Statistical analysis with graphs

Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning.

English

KRISTIN H. JARMAN, PhD, is Senior Research Scientist at the Pacific Northwest National Laboratory in Richland, Washington, where she applies basic and not-so-basic statistical analyses to her own set of research challenges.

English

Preface ix

PART 1 The Basics

1. Statistics: The Life of the Party 3

2. Lions, and Tigers, and . . . Bigfoot? Oh, My: How Questionable Data Can Screw Up an Otherwise Perfectly Good Statistical Analysis 7

3. Asteroid Belts and Spandex Cars: Using Descriptive Statistics to Answer Your Most Weighty Questions 23

4. Baseball, Game Shows, and Sidekicks: Who Cares about Probability Anyway? 43

5. What It’s Like to Be Normal: Probability Distributions and One Rule That Could Make You Wildly Popular 63

PART 2 What a Few Estimates and a Little Probability Can Do for You

6. Men Are Insensitive and Women Are Illogical. Really: Asserting Your Stereotypes with Confidence 89

7. Godzilla versus King Kong: How Hypothesis Tests Can Be Used to Settle the Battle of the Movie Monsters 109

8. Lab Rats and Roommates: Analysis of Variance and How to Channel Your Inner Mad Scientist 129

9. When the Zombie Flu Went Viral: Regressing the Myth Out of Urban Myths 143

Appendix A: Critical Values for the Standard Normal Distribution 161

Appendix B: Critical Values for the T-Distribution 163

Appendix C: Critical Values for the Chi-Squared Distribution 165

Glossary 167

References 173

Index 175

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“In summary, I can recommend Kristin Jarman’s textbook to interested students and researchers who want to smoothly slip into the world of statistics and prefer a rather prose-oriented introduction over mathematical terminology.”  (Biometrical Journal, 1 January 2016)

“It is simply one of the best books I have read in the last few years. It is pedagogical, intuitive, and light, all wrapped up in a beautiful sense of humor. This book provides a first step toward the vast world of data analysis."  (Computing Reviews, 8 August 2014)

“Summing Up: Recommended.  Lower-division undergraduates, two-year technical program students, and general readers.”  (Choice, 1 December 2013)

"With a focus on presenting basic statistical concepts in a friendly and accessible style, this book focuses on the more subjective side of statistics- the art of data analysis. It provides fun, common sense examples that explore statistical concepts and methods and is aimed at helping anyone who has to make decisions based on raw data or compiled results." (Zentralblatt MATH 2016)

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