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Mathematical Statistics for Biologists and Other Interesting People

机译：生物学家和其他有趣的人的数学统计

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As ecologists and evolutionary biologists, along with many of their colleagues across the sciences, have become increasingly statistically sophisticated, there has been a deluge of books, blogs, and tweets explaining how to make sense of data. Many of these are great (many are not). However, most of the resources aimed at nonstatisticians skirt around the mathematical reasoning that underlies procedures; that is to say they often focus on the how of statistics and not the why. Statistical Thinking from Scratch: A Primer for Scientists [1], a new book by M.D. Edge, a population geneticist, fills a unique niche in this landscape, sitting between the inference-focused material most biodiversity scientists are likely familiar with, and mathematical statistics books that focus on the derivations and properties of estimators. Edge’s conceit is that any scientist with a basic familiarity of algebra and calculus – and some serious motivation – can gain a rich understanding of where inference methods come from and why they work (and also when they might not). Rather than work through a variety of different methodologies, almost the entire book is devoted to explaining a single technique, bivariate linear regression, at a deep level; the big concepts can then be ported to other operations.
机译：随着生态学家和进化生物学家，随着他们在整个科学的许多同事以及越来越多的统计上复杂，已经有一个卓越的书籍，博客和推文解释了如何掌握数据。其中许多很棒（很多不是）。但是，大多数资源旨在围绕下降程序的数学推理的非学生裙子;也就是说他们经常专注于统计数据如何而不是为什么。统计思考从划痕：科学家的底漆[1]，一本由MD边缘的新书，一个人口遗传学家，在这种景观中填补了一个独特的利基，坐在推广的专注材料之间大多数生物多样性科学家可能熟悉，数学统计数据专注于估算器的推导和属性的书籍。边缘的自负是，任何科学家都具有基本熟悉的代数和微积分 - 以及一些严重的动机 - 可以丰富地了解推理方法来自以及他们为何工作（以及他们可能不会的时候）。而不是通过各种不同的方法工作，几乎整本书都致力于解释单一技术，在深度水平处比抗性线性回归;然后可以将大概念移植到其他操作。

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