课程信息
64,473

第 2 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

初级

完成时间大约为26 小时

建议:5 weeks of study, 5-7 hours/week...

英语(English)

字幕:英语(English)

您将获得的技能

Statistical InferenceStatistical Hypothesis TestingR Programming

第 2 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

初级

完成时间大约为26 小时

建议:5 weeks of study, 5-7 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 20 分钟

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
2 个阅读材料
About Statistics with R Specialization10分钟
More about Inferential Statistics10分钟
完成时间为 3 小时

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 (总计 65 分钟), 6 个阅读材料, 3 个测验
7 个视频
Sampling Variability and CLT20分钟
CLT (for the mean) examples10分钟
Confidence Interval (for a mean)11分钟
Accuracy vs. Precision7分钟
Required Sample Size for ME4分钟
CI (for the mean) examples5分钟
6 个阅读材料
Lesson Learning Objectives10分钟
Lesson Learning Objectives10分钟
Week 1 Suggested Readings and Practice Exercises10分钟
About Lab Choices10分钟
Week 1 Lab Instructions (RStudio)10分钟
Week 1 Lab Instructions (RStudio Cloud)10分钟
3 个练习
Week 1 Practice Quiz12分钟
Week 1 Quiz14分钟
Week 1 Lab12分钟
2
完成时间为 2 小时

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 (总计 59 分钟), 5 个阅读材料, 3 个测验
7 个视频
Hypothesis Testing (for a mean)14分钟
HT (for the mean) examples9分钟
Inference for Other Estimators10分钟
Decision Errors8分钟
Significance vs. Confidence Level6分钟
Statistical vs. Practical Significance7分钟
5 个阅读材料
Lesson Learning Objectives10分钟
Lesson Learning Objectives10分钟
Week 2 Suggested Readings and Practice Exercises10分钟
Week 2 Lab Instructions (RStudio)10分钟
Week 2 Lab Instructions (RStudio Cloud)10分钟
3 个练习
Week 2 Practice Quiz10分钟
Week 2 Quiz16分钟
Week 2 Lab12分钟
3
完成时间为 3 小时

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 (总计 84 分钟), 5 个阅读材料, 3 个测验
11 个视频
t-distribution7分钟
Inference for a mean9分钟
Inference for comparing two independent means8分钟
Inference for comparing two paired means9分钟
Power11分钟
Comparing more than two means6分钟
ANOVA9分钟
Conditions for ANOVA2分钟
Multiple comparisons6分钟
Bootstrapping8分钟
5 个阅读材料
Lesson Learning Objectives10分钟
Lesson Learning Objectives10分钟
Week 3 Suggested Readings and Practice Exercises10分钟
Week 3 Lab Instructions (RStudio)10分钟
Week 3 Lab Instructions (RStudio Cloud)10分钟
3 个练习
Week 3 Practice Quiz16分钟
Week 3 Quiz28分钟
Week 3 Lab14分钟
4
完成时间为 4 小时

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 (总计 118 分钟), 5 个阅读材料, 3 个测验
11 个视频
Sampling Variability and CLT for Proportions15分钟
Confidence Interval for a Proportion9分钟
Hypothesis Test for a Proportion9分钟
Estimating the Difference Between Two Proportions17分钟
Hypothesis Test for Comparing Two Proportions13分钟
Small Sample Proportions10分钟
Examples4分钟
Comparing Two Small Sample Proportions5分钟
Chi-Square GOF Test14分钟
The Chi-Square Independence Test11分钟
5 个阅读材料
Lesson Learning Objectives10分钟
Lesson Learning Objectives10分钟
Week 4 Suggested Readings and Practice Exercises10分钟
Week 4 Lab Instructions (RStudio)10分钟
Week 4 Lab Instructions (RStudio Cloud)10分钟
3 个练习
Week 4 Practice Quiz18分钟
Week 4 Quiz24分钟
Week 4 Lab26分钟
4.8
245 个审阅Chevron Right

31%

完成这些课程后已开始新的职业生涯

22%

通过此课程获得实实在在的工作福利

热门审阅

创建者 MNMar 1st 2017

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!

创建者 ZCAug 24th 2017

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!!!

讲师

Avatar

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

关于 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....

关于 Statistics with R 专项课程

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....
Statistics with R

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