The Art of Statistics: How to Learn from DataIn this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems. Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs. |
Contents
CHAPTER 1 | |
How Sure Can We Be About What Is Going On? Estimates | |
Probability the Language of Uncertainty and Variability | |
Summarizing and Communicating Numbers Lots of Numbers | |
CHAPTER 9 | |
Why Are We Looking at Data Anyway? Populations | |
Putting Probability and Statistics Together | |
What Causes What? | |
CHAPTER 12 | |
CHAPTER 14 | |
In Conclusion | |
NOTES | |
Modelling Relationships Using Regression | |
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actually algorithm allocated analysis arm-crossing assumed assumptions average Bayes factor Bayesian bootstrap bowel cancer Brier score calculated chance Chapter claim classification tree coefficients coin complex conclusions confidence intervals correlation data-points doping effect estimate evidence example flip given Heart Protection Study height Higgs boson homicide incidents hypothesis testing idea known likelihood ratio margin of error mathematical measure median mortality multiple null hypothesis number of homicides number of partners odds outcome overall P-value parameters Percentage Poisson distribution polls population distribution positive prediction prior distribution probability distribution probability theory problem proportion question random variable regression reported resamples researchers Ronald Fisher sample mean scientific Shipman shown in Figure shows significance testing simple standard deviation standard error statins statistical inference statistical model statistical science statistically significant statisticians summary statistics surgery survey survival Table test statistic theorem threshold true Type II error uncertainty interval underlying women