About this Course
65,465

다음의 2/5개 강좌

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 26시간 필요

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

영어

자막: 영어

귀하가 습득할 기술

Statistical InferenceStatistical Hypothesis TestingR Programming

다음의 2/5개 강좌

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 26시간 필요

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

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 20분 필요

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 readings
2개의 읽기 자료
About Statistics with R Specialization10m
More about Inferential Statistics10m
완료하는 데 3시간 필요

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 videos (Total 65 min), 6 readings, 3 quizzes
7개의 동영상
Sampling Variability and CLT20m
CLT (for the mean) examples10m
Confidence Interval (for a mean)11m
Accuracy vs. Precision7m
Required Sample Size for ME4m
CI (for the mean) examples5m
6개의 읽기 자료
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 1 Suggested Readings and Practice Exercises10m
About Lab Choices10m
Week 1 Lab Instructions (RStudio)10m
Week 1 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 1 Practice Quiz12m
Week 1 Quiz14m
Week 1 Lab12m
2
완료하는 데 2시간 필요

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 videos (Total 59 min), 5 readings, 3 quizzes
7개의 동영상
Hypothesis Testing (for a mean)14m
HT (for the mean) examples9m
Inference for Other Estimators10m
Decision Errors8m
Significance vs. Confidence Level6m
Statistical vs. Practical Significance7m
5개의 읽기 자료
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 2 Suggested Readings and Practice Exercises10m
Week 2 Lab Instructions (RStudio)10m
Week 2 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 2 Practice Quiz10m
Week 2 Quiz16m
Week 2 Lab12m
3
완료하는 데 3시간 필요

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 videos (Total 84 min), 5 readings, 3 quizzes
11개의 동영상
t-distribution7m
Inference for a mean9m
Inference for comparing two independent means8m
Inference for comparing two paired means9m
Power11m
Comparing more than two means6m
ANOVA9m
Conditions for ANOVA2m
Multiple comparisons6m
Bootstrapping8m
5개의 읽기 자료
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 3 Suggested Readings and Practice Exercises10m
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 3 Practice Quiz16m
Week 3 Quiz28m
Week 3 Lab14m
4
완료하는 데 4시간 필요

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 videos (Total 118 min), 5 readings, 3 quizzes
11개의 동영상
Sampling Variability and CLT for Proportions15m
Confidence Interval for a Proportion9m
Hypothesis Test for a Proportion9m
Estimating the Difference Between Two Proportions17m
Hypothesis Test for Comparing Two Proportions13m
Small Sample Proportions10m
Examples4m
Comparing Two Small Sample Proportions5m
Chi-Square GOF Test14m
The Chi-Square Independence Test11m
5개의 읽기 자료
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 4 Suggested Readings and Practice Exercises10m
Week 4 Lab Instructions (RStudio)10m
Week 4 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 4 Practice Quiz18m
Week 4 Quiz24m
Week 4 Lab26m
4.8
245개의 리뷰Chevron Right

31%

이 강좌를 수료한 후 새로운 경력 시작하기

22%

이 강좌를 통해 확실한 경력상 이점 얻기

최상위 리뷰

대학: 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!

대학: 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!!!

강사

Avatar

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

듀크대학교 정보

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....

Statistics with R 전문 분야 정보

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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