Об этом курсе: 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
Автор: Университет Дьюка
Преподаватели: Mine Çetinkaya-Rundel, Associate Professor of the Practice
Department of Statistical Science
| Основные сведения | Course 2 of 5 in the Statistics with R Specialization |
| Уровень | Beginner |
| Выполнение | 5 weeks of study, 5-7 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
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 материалов для самостоятельного изучения
- 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 видео, 4 материалов для самостоятельного изучения, 1 practice quiz
- Reading: Lesson Learning Objectives
- Видео: Introduction
- Видео: Sampling Variability and CLT
- Видео: CLT (for the mean) examples
- Reading: Lesson Learning Objectives
- Видео: Confidence Interval (for a mean)
- Видео: Accuracy vs. Precision
- Видео: Required Sample Size for ME
- Видео: CI (for the mean) examples
- Reading: Week 1 Suggested Readings and Practice Exercises
- Practice Quiz: Week 1 Practice Quiz
- Reading: Week 1 Lab Instructions
Оцениваемый: Week 1 Quiz
Оцениваемый: Week 1 Lab
НЕДЕЛЯ 2
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 видео, 4 материалов для самостоятельного изучения, 1 practice quiz
- Reading: Lesson Learning Objectives
- Видео: Another Introduction to Inference
- Видео: Hypothesis Testing (for a mean)
- Видео: HT (for the mean) examples
- Reading: Lesson Learning Objectives
- Видео: Inference for Other Estimators
- Видео: Decision Errors
- Видео: Significance vs. Confidence Level
- Видео: Statistical vs. Practical Significance
- Reading: Week 2 Suggested Readings and Practice Exercises
- Practice Quiz: Week 2 Practice Quiz
- Reading: Week 2 Lab Instructions
Оцениваемый: Week 2 Quiz
Оцениваемый: Week 2 Lab
НЕДЕЛЯ 3
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 видео, 4 материалов для самостоятельного изучения, 1 practice quiz
- Reading: Lesson Learning Objectives
- Видео: Introduction
- Видео: t-distribution
- Видео: Inference for a mean
- Видео: Inference for comparing two independent means
- Видео: Inference for comparing two paired means
- Видео: Power
- Reading: Lesson Learning Objectives
- Видео: Comparing more than two means
- Видео: ANOVA
- Видео: Conditions for ANOVA
- Видео: Multiple comparisons
- Видео: Bootstrapping
- Reading: Week 3 Suggested Readings and Practice Exercises
- Practice Quiz: Week 3 Practice Quiz
- Reading: Week 3 Lab Instructions
Оцениваемый: Week 3 Quiz
Оцениваемый: Week 3 Lab
НЕДЕЛЯ 4
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 видео, 4 материалов для самостоятельного изучения, 1 practice quiz
- Reading: Lesson Learning Objectives
- Видео: Introduction
- Видео: Sampling Variability and CLT for Proportions
- Видео: Confidence Interval for a Proportion
- Видео: Hypothesis Test for a Proportion
- Видео: Estimating the Difference Between Two Proportions
- Видео: Hypothesis Test for Comparing Two Proportions
- Reading: Lesson Learning Objectives
- Видео: Small Sample Proportions
- Видео: Examples
- Видео: Comparing Two Small Sample Proportions
- Видео: Chi-Square GOF Test
- Видео: The Chi-Square Independence Test
- Reading: Week 4 Suggested Readings and Practice Exercises
- Practice Quiz: Week 4 Practice Quiz
- Reading: Week 4 Lab Instructions
Оцениваемый: Week 4 Quiz
Оцениваемый: Week 4 Lab
НЕДЕЛЯ 5
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 материал для самостоятельного изучения
- Reading: Project Information
Оцениваемый: Data Analysis Project
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Авторы
Университет Дьюка
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.
Стоимость
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Рейтинги и отзывы
Good course, gives you a solid foundation.
我认为是一个非常棒的课程，明了清晰。但是如果会在最后来一些总结的话就更好了
The subjects are presented very clearly and the lectures very enjoyable to watch. Although I have a basic knowledge on the subject this course improved my understanding on the subject.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
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Excellent course with a lot of real life examples.