The field of statistics is rapidly transforming into a discipline that Hal Varian at Google has called "sexy". And with good reason - from batting averages and political polls to game shows and medical research - the real-world application of statistics is growing by leaps and bounds. In Naked Statistics, Charles Wheelan strips away the arcane and technical details to get at the underlying intuition that is key to understanding the power of statistical concepts. Tackling a wide-ranging set of problems, he demonstrates how statistics can be used to look at questions that are important and relevant to us today. With the trademark wit, accessibility and fun that made Naked Economics a bestseller, Wheelan brings another essential discipline to life with a one-in-a-million statistics book that you will read for pleasure.
Former correspondent for The Economist, current columnist for Yahoo!, and professor at the Harris School of Public Policy at the University of Chicago, Charles Wheelan lives in Chicago with his family.
昨天终于把书读完了,总的来说内容不错,翻译的也可以,毕竟是一本统计学的科普读物。觉得书的最大闪光点是把以前学的概率等统计知识与具体案例联系在一起,能够更深入的理解统计学的意义、算法和用途,能够加深对概率、相关性、极限定理、回归分析这些之前半懂不懂的知识的认...
评分这本书就是统计学101的水平,完全是写给统计盲看的,对我没用,只算是复习了下central tendency, central limit theorem, 和概率、回归分析等方面的基础。书写的浅显易懂,在基本不用专业语言的情况下能把概念解释清楚,这是难得的地方,所以对于想要入门统计基本原理的,是本...
评分http://ecolion.cn/article-3484-1.html 书名:《赤裸裸的统计学:剥开数据枯燥的外壳(Naked Statistics: Stripping the Dread From the Data)》 作者:Charles Wheelan 出版社:Norton,页数:282页,价格:26.95美元 Amazon.com, Amazon.co.uk 有售 当今世界,数据到处都...
评分其实,除了“纯文科”学生,现在几乎所有大学毕业生都应该学过统计学。理工科的教材是“概率与数理统计”,通常比统计学更抽象、更数学一些。而且我最近发现,好像美国中学生就要学一点统计学。所以,这类统计科普书注定会遭到鄙视,因为如果只看它们讲述的内容难度,我认为连...
评分http://ecolion.cn/article-3484-1.html 书名:《赤裸裸的统计学:剥开数据枯燥的外壳(Naked Statistics: Stripping the Dread From the Data)》 作者:Charles Wheelan 出版社:Norton,页数:282页,价格:26.95美元 Amazon.com, Amazon.co.uk 有售 当今世界,数据到处都...
very basic but fun
评分每个人都应该了解的统计概念(可惜大学的老师不能这样教我们)
评分似乎过于简单,这是我挑书不好。不过小作者自认为幽默的废话太多啦,还时常一刷就是好几行。
评分全书讲的东西只是stats sampling的部分,太基础,太浅了,但举的例子还是蛮好的。ps 作者一看就不是统计学家。。
评分全书讲的东西只是stats sampling的部分,太基础,太浅了,但举的例子还是蛮好的。ps 作者一看就不是统计学家。。
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