关于此课程： 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
制作方： 杜克大学
教学方： Mine Çetinkaya-Rundel, Associate Professor of the Practice
Department of Statistical Science
| 基本信息 | 课程 2（共 5 门，Statistics with R Specialization ） |
| 级别 | 初级 |
| 承诺学习时间 | 5 weeks of study, 5-7 hours/week |
| 语言 | 英语（English） |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
About the Specialization and the Course
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!
2 个阅读材料
- Reading: About Statistics with R Specialization
- Reading: More about Inferential Statistics
Central Limit Theorem and Confidence Interval
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.
7 个视频, 4 个阅读材料, 1 个练习测验
- Reading: Lesson Learning Objectives
- 视频: Sampling Variability and CLT
- 视频: CLT (for the mean) examples
- Reading: Lesson Learning Objectives
- 视频: Confidence Interval (for a mean)
- 视频: Accuracy vs. Precision
- 视频: Required Sample Size for ME
- 视频: CI (for the mean) examples
- Reading: Week 1 Suggested Readings and Practice Exercises
- 练习测验: Week 1 Practice Quiz
- Reading: Week 1 Lab Instructions
已评分: Week 1 Quiz
已评分: Week 1 Lab
第 2 周
Inference and Significance
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.
7 个视频, 4 个阅读材料, 1 个练习测验
- Reading: Lesson Learning Objectives
- 视频: Hypothesis Testing (for a mean)
- 视频: HT (for the mean) examples
- Reading: Lesson Learning Objectives
- 视频: Inference for Other Estimators
- 视频: Decision Errors
- 视频: Significance vs. Confidence Level
- 视频: Statistical vs. Practical Significance
- Reading: Week 2 Suggested Readings and Practice Exercises
- 练习测验: Week 2 Practice Quiz
- Reading: Week 2 Lab Instructions
已评分: Week 2 Quiz
已评分: Week 2 Lab
第 3 周
Inference for Comparing Means
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.
11 个视频, 4 个阅读材料, 1 个练习测验
- Reading: Lesson Learning Objectives
- 视频: t-distribution
- 视频: Inference for a mean
- 视频: Inference for comparing two independent means
- 视频: Inference for comparing two paired means
- 视频: Power
- Reading: Lesson Learning Objectives
- 视频: Comparing more than two means
- 视频: ANOVA
- 视频: Conditions for ANOVA
- 视频: Multiple comparisons
- 视频: Bootstrapping
- Reading: Week 3 Suggested Readings and Practice Exercises
- 练习测验: Week 3 Practice Quiz
- Reading: Week 3 Lab Instructions
已评分: Week 3 Quiz
已评分: Week 3 Lab
第 4 周
Inference for Proportions
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?”.
11 个视频, 4 个阅读材料, 1 个练习测验
- Reading: Lesson Learning Objectives
- 视频: Sampling Variability and CLT for Proportions
- 视频: Confidence Interval for a Proportion
- 视频: Hypothesis Test for a Proportion
- 视频: Estimating the Difference Between Two Proportions
- 视频: Hypothesis Test for Comparing Two Proportions
- Reading: Lesson Learning Objectives
- 视频: Small Sample Proportions
- 视频: Examples
- 视频: Comparing Two Small Sample Proportions
- 视频: Chi-Square GOF Test
- 视频: The Chi-Square Independence Test
- Reading: Week 4 Suggested Readings and Practice Exercises
- 练习测验: Week 4 Practice Quiz
- Reading: Week 4 Lab Instructions
已评分: Week 4 Quiz
已评分: Week 4 Lab
第 5 周
Data Analysis Project
In this week you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment.
1 个阅读材料
- Reading: Project Information
已评分: Data Analysis Project
常见问题解答
运作方式
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
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证书
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制作方
杜克大学
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
价格
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评分和审阅
Excellent teaching style with enough practice to get a good grip on whatever is taught
This is a very well structured and taught course. It is difficult so make sure to dedicate lots of time for videos, practice, and revisions.
I thought this course did a great job of incorporating R code into the lecture and hope that continues in future courses.
The course is very useful and helps me understand the formal testing process of data analysis. I just hope it would cover more of non-parametric testing techniques and dive into a bit more into effect size testing. Anyway, It also provides a lot of insights into important statistical measures of information, which could potentially be extended to the field of predictive modeling and machine learning.