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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-analysis-tools
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课程名称：数据分析工具 课程概述：在本课程中，您将学习如何针对数据发展和检验假设。您将掌握各种统计检验方法，以及如何将适当的方法应用于特定数据和问题的策略。使用您选择的两种强大统计软件（SAS或Python），您将探索方差分析（ANOVA）、卡方检验和皮尔逊相关分析。本课程将引导您了解基本的统计原理，提供您回答自己提出的问题的工具。在整个课程中，您将与他人分享您的进展，以获得有价值的反馈，并为其他学习者提供关于他们工作的见解。 课程大纲： 第一部分：假设检验与方差分析 本节将从数据管理和可视化课程的内容继续。在选择了数据集和研究问题、管理了感兴趣的变量并以图形方式可视化其关系之后，您将准备好以统计方式检验这些关系。视频中将描述假设检验的过程，您将在本课程中用于检验不同种类变量之间的关系（定量和分类）。接着，我们将展示如何在方差分析的背景下检验假设（当您有一个定量变量和一个分类变量）。您需要编写程序以管理所需的附加变量，并运行和解释方差分析的结果。如果您的研究问题不包括一个定量变量，您可以选择数据集中其他的定量变量进行练习；如果不包括分类变量，您可以将一个定量变量进行分类。 第二部分：卡方独立性检验 本节将向您展示如何在两个分类变量的背景下进行卡方独立性检验。您需要编写程序以管理所需的附加变量，并运行和解释卡方独立性检验的结果。如果您的研究问题仅包括定量变量，您可以将这些变量进行分类以便练习。 第三部分：皮尔逊相关 本节将展示如何在两个定量变量的背景下进行皮尔逊相关检验。您需要编写程序以管理所需的附加变量，并运行和解释相关系数。如果您的研究问题仅包括分类变量，您可以选择数据集中的其他变量进行练习。 第四部分：探索统计交互作用 本节将讨论统计交互作用（也称为调节效应）的基本概念。在统计学中，当两个变量之间的关系依赖于第三个变量时，就会发生调节效应。调节变量的影响通常可以通过统计交互作用表征，即影响解释变量（X）和响应变量（Y）之间关系的方向和强度的第三个变量。您的任务是在一个或多个潜在调节变量的背景下检验自己的研究问题。
Part: 1
Title:Hypothesis Testing and ANOVA
Description:This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. If your research question does not include a categorical variable, you can categorize one that is quantitative.
Part: 2
Title:Chi Square Test of Independence
Description:This session shows you how to test hypotheses in the context of a Chi-Square Test of Independence (when you have two categorical variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a Chi-Square Test of Independence. Note that if your research question only includes quantitative variables, you can categorize those just to get some practice with the tool.
Part: 3
Title:Pearson Correlation
Description:This session shows you how to test hypotheses in the context of a Pearson Correlation (when you have two quantitative variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a correlation coefficient. Note that if your research question only includes categorical variables, you can choose other variables from your data set just to get some practice with the tool.
Part: 4
Title:Exploring Statistical Interactions
Description:In this session, we will discuss the basic concept of statistical interaction (also known as moderation). In statistics, moderation occurs when the relationship between two variables depends on a third variable. The effect of a moderating variable is often characterized statistically as an interaction; that is, a third variable that affects the direction and/or strength of the relation between your explanatory (X) and response (Y) variable. Your task will be to test your own research question in the context of one or more potential moderating variables.
In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.
