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
提供方
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 20 分鐘
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 個閱讀材料
2 個閱讀材料
About Statistics with R Specialization10分鐘
More about Inferential Statistics10分鐘
完成時間為 2 小時
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 個視頻 （總計 65 分鐘）, 4 個閱讀材料, 3 個測驗
7 個視頻
Introduction4分鐘
Sampling Variability and CLT20分鐘
CLT (for the mean) examples10分鐘
Confidence Interval (for a mean)11分鐘
Accuracy vs. Precision7分鐘
Required Sample Size for ME4分鐘
CI (for the mean) examples5分鐘
4 個閱讀材料
Lesson Learning Objectives10分鐘
Lesson Learning Objectives10分鐘
Week 1 Suggested Readings and Practice Exercises10分鐘
Week 1 Lab Instructions10分鐘
3 個練習
Week 1 Practice Quiz12分鐘
Week 1 Quiz14分鐘
Week 1 Lab12分鐘
週
2完成時間為 2 小時
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 個視頻 （總計 59 分鐘）, 4 個閱讀材料, 3 個測驗
7 個視頻
Hypothesis Testing (for a mean)14分鐘
HT (for the mean) examples9分鐘
Inference for Other Estimators10分鐘
Decision Errors8分鐘
Significance vs. Confidence Level6分鐘
Statistical vs. Practical Significance7分鐘
4 個閱讀材料
Lesson Learning Objectives10分鐘
Lesson Learning Objectives10分鐘
Week 2 Suggested Readings and Practice Exercises10分鐘
Week 2 Lab Instructions10分鐘
3 個練習
Week 2 Practice Quiz10分鐘
Week 2 Quiz16分鐘
Week 2 Lab12分鐘
週
3完成時間為 3 小時
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 個視頻 （總計 84 分鐘）, 4 個閱讀材料, 3 個測驗
11 個視頻
Introduction4分鐘
t-distribution7分鐘
Inference for a mean9分鐘
Inference for comparing two independent means8分鐘
Inference for comparing two paired means9分鐘
Power11分鐘
Comparing more than two means6分鐘
ANOVA9分鐘
Conditions for ANOVA2分鐘
Multiple comparisons6分鐘
Bootstrapping8分鐘
4 個閱讀材料
Lesson Learning Objectives10分鐘
Lesson Learning Objectives10分鐘
Week 3 Suggested Readings and Practice Exercises10分鐘
Week 3 Lab Instructions10分鐘
3 個練習
Week 3 Practice Quiz16分鐘
Week 3 Quiz28分鐘
Week 3 Lab14分鐘
週
4完成時間為 4 小時
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 個視頻 （總計 118 分鐘）, 4 個閱讀材料, 3 個測驗
11 個視頻
Introduction3分鐘
Sampling Variability and CLT for Proportions15分鐘
Confidence Interval for a Proportion9分鐘
Hypothesis Test for a Proportion9分鐘
Estimating the Difference Between Two Proportions17分鐘
Hypothesis Test for Comparing Two Proportions13分鐘
Small Sample Proportions10分鐘
Examples4分鐘
Comparing Two Small Sample Proportions5分鐘
Chi-Square GOF Test14分鐘
The Chi-Square Independence Test11分鐘
4 個閱讀材料
Lesson Learning Objectives10分鐘
Lesson Learning Objectives10分鐘
Week 4 Suggested Readings and Practice Exercises10分鐘
Week 4 Lab Instructions10分鐘
3 個練習
Week 4 Practice Quiz18分鐘
Week 4 Quiz24分鐘
Week 4 Lab26分鐘
4.8
241 個審閱31%
完成這些課程後已開始新的職業生涯
22%
通過此課程獲得實實在在的工作福利
熱門審閱
Great course. If you put in a little effort, you will come out with a lot of new knowledge. I recommend using the book after you have seen the movies. It gives a deeper picture of how it works. Great!
This course by Professor Çetinkaya-Rundel is awesome because it is taught in a very clear and vivid way. Lab section and forum are so dope that I love them so much! Definitely strong recommendation!!!
講師
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
關於 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.
關於 Statistics with R 專項課程
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
常見問題
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
Cost of the Course
If you want to complete the course and earn a Course Certificate by submitting assignments for a grade, you can upgrade your experience by subscribing to the course for $49/month. You can also apply for financial aid if you can't afford the course fee.
When you enroll in a course that is part of a Specialization (which this course is), you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses or cancel your subscription once you complete the single course.
Can I just enroll in a single course? I'm not interested in the entire Specialization.
To enroll in an individual course, search for the course title in the catalog.
To get full access to a course, including the option to earn grades and a Course Certificate, you'll need to subscribe. New subscribers will start with a full access subscription, which includes full access to every course in the Coursera catalog. Existing Specialization subscribers will be given the option to update to a full access subscription when enrolling in a new Specialization or course.
When you enroll in a course that is part of a Specialization, you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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