Prelude

M. Edge
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引用次数: 0

Abstract

There are two traditional ways to learn statistics. One way is to pass over the mathematical underpinnings and focus on developing relatively shallow knowledge about a wide variety of statistical procedures. Another is to spend years learning the mathematics necessary for traditional mathematical approaches to statistics. For many people who need to analyze data, neither of these paths is sufficient. The shallow-but-wide approach fails to provide students with the foundation that allows for confidence and creativity in analyzing modern datasets, and many researchers—though possibly motivated to learn math—do not have the background to start immediately on a traditional mathematical approach. This book exists to help researchers jump between tracks, providing motivated students whose knowledge of mathematics may be incomplete or rusty with a serious introduction to statistics that allows further study from more mathematical sources. This is done by focusing on a single statistical technique that is fundamental to statistical practice—simple linear regression—and supplementing the exposition with ample simulations conducted in the statistical programming language R. The first half of the book focuses on preliminaries, including the use of R and probability theory, whereas the second half covers statistical estimation and inference from semiparametric, parametric, and Bayesian perspectives.
前奏
学习统计学有两种传统方法。一种方法是跳过数学基础,专注于发展关于各种统计程序的相对肤浅的知识。另一种方法是花费数年时间学习传统数学方法所必需的数学。对于许多需要分析数据的人来说,这两种途径都不够。这种肤浅而广泛的方法无法为学生提供分析现代数据集的信心和创造力的基础,而且许多研究人员——尽管可能有学习数学的动机——没有立即开始使用传统数学方法的背景。这本书的存在是为了帮助研究人员在轨道之间跳跃,提供有动机的学生,他们的数学知识可能不完整或生锈,一个严肃的统计介绍,允许从更多的数学来源进一步研究。这是通过专注于统计实践基础的单一统计技术-简单的线性回归-并补充在统计编程语言R中进行的大量模拟的阐述来完成的。本书的前半部分侧重于初步介绍,包括R和概率论的使用,而后半部分则涵盖了半参数,参数和贝叶斯观点的统计估计和推断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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