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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/statistical-inference
课程评论：没有评论
课程名称：统计推断 课程概述： 统计推断是从数据中得出关于总体或科学真理结论的过程。推断的方式多种多样，包括统计建模、数据导向策略以及在分析中明确使用设计和随机化。此外，还有广泛的理论（频率学派、贝叶斯学派、似然理论、基于设计等）以及众多复杂性（缺失数据、观察到的和未观察到的混杂因素、偏差）需要考虑。实践者常常会在技术、哲学和细微之处的迷宫中陷入困境。本课程以实用的方法呈现推断的基础，使学生能够高效完成工作。通过本课程的学习，学生将理解统计推断的广泛方向，并能够利用这些信息在数据分析中做出明智的选择。 课程大纲： - 第一周：概率与期望值 描述：本周将重点关注基础知识，包括概率、随机变量、期望值等。 - 第二周：变异性、分布与渐近理论 描述：我们将讨论变异性、分布、极限和置信区间。 - 第三周：区间、检验与p值 描述：本节将重点研究区间、检验和p值。 - 第四周：效能、重抽样与置换检验 描述：我们将开始探讨效能、重抽样和置换检验。
Name:Week 1: Probability & Expected Values
Description:This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
Name:Week 2: Variability, Distribution, & Asymptotics
Description:We're going to tackle variability, distributions, limits, and confidence intervals.
Name:Week: Intervals, Testing, & Pvalues
Description:We will be taking a look at intervals, testing, and pvalues in this lesson.
Name:Week 4: Power, Bootstrapping, & Permutation Tests
Description:We will begin looking into power, bootstrapping, and permutation tests.
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
