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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/inferential-statistics-intro
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课程名称：推断统计 概述： 本课程涵盖了用于数值和分类数据的常用统计推断方法。学员将学习如何建立和执行假设检验，解释p值，并以客户或公众可理解的方式报告分析结果。通过多个数据示例，学员将学习如何报告估计数量，表达相关的不确定性。课程将指导学员安装并使用R和RStudio（免费统计软件），并在实验练习和最终项目中应用该软件。课程介绍了执行数据分析的实用工具，探索了解释和报告分类与数值数据结果所需的基本概念。 大纲： 1. 关于本专业及课程：本短模块介绍Coursera专业和课程的基础知识，包括统计与R专业和本课程的简介。 2. 中心极限定理与置信区间：第一周将讨论推断的基础知识，引入中心极限定理和置信区间。 3. 推断与显著性：第二周将讨论正式的假设检验及其与估计的关系，介绍如何在工作中应用这些方法，并讨论决策错误和统计与实际显著性。 4. 比较均值的推断：第三周将介绍t分布和均值比较，以及基于模拟的置信区间创建方法。 5. 比例的推断：第四周将聚焦于分类数据的推断，学员将使用提供的数据集完成和报告数据分析问题。 本课程旨在提供实际的统计推断工具，帮助学员理解和应用相关的统计方法。
Name:About the Specialization and the Course
Description:This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Inferential Statistics. Please take several minutes to browse them through. Thanks for joining us in this course!
Name:Central Limit Theorem and Confidence Interval
Description:Welcome to Inferential Statistics! In this course we will discuss Foundations for Inference. Check out the learning objectives, start watching the videos, and finally work on the quiz and the labs of this week. In addition to videos that introduce new concepts, you will also see a few videos that walk you through application examples related to the week's topics. In the first week we will introduce Central Limit Theorem (CLT) and confidence interval.
Name:Inference and Significance
Description:Welcome to Week Two! This week we will discuss formal hypothesis testing and relate testing procedures back to estimation via confidence intervals. These topics will be introduced within the context of working with a population mean, however we will also give you a brief peek at what's to come in the next two weeks by discussing how the methods we're learning can be extended to other estimators. We will also discuss crucial considerations like decision errors and statistical vs. practical significance. The labs for this week will illustrate concepts of sampling distributions and confidence levels.
Name:Inference for Comparing Means
Description:Welcome to Week Three of the course! This week we will introduce the t-distribution and comparing means as well as a simulation based method for creating a confidence interval: bootstrapping. If you have questions or discussions, please use this week's forum to ask/discuss with peers.
Name:Inference for Proportions
Description:Welcome to Week Four of our course! In this unit, we’ll discuss inference for categorical data. We use methods introduced this week to answer questions like “What proportion of the American public approves of the job of the Supreme Court is doing?” Also in this week you will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
