課程信息
4.8
1,261 個評分
240 個審閱

第 2 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

初級

完成時間大約為25 小時

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

英語(English)

字幕:英語(English)

您將獲得的技能

Statistical InferenceStatistical Hypothesis TestingR Programming

第 2 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

初級

完成時間大約為25 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

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
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
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
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
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
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
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
240 個審閱Chevron Right

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

創建者 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!

創建者 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!!!

講師

Avatar

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department 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....
Statistics with R

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