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
Этот курс входит в специализацию ''Специализация Statistics with R'
Статистика вывода
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Об этом курсе
71,704
71,704Приобретаемые навыки
Statistical InferenceStatistical Hypothesis TestingR Programming
Программа курса: что вы изучите
Неделя
120 минуты на завершение
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
2 материала для самостоятельного изучения
About Statistics with R Specialization10мин
More about Inferential Statistics10мин
3 ч. на завершение
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 ((всего 65 мин.)), 6 материалов для самостоятельного изучения, 3 тестов
7 видео
Introduction4мин
Sampling Variability and CLT20мин
CLT (for the mean) examples10мин
Confidence Interval (for a mean)11мин
Accuracy vs. Precision7мин
Required Sample Size for ME4мин
CI (for the mean) examples5мин
6 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Week 1 Suggested Readings and Practice Exercises10мин
About Lab Choices10мин
Week 1 Lab Instructions (RStudio)10мин
Week 1 Lab Instructions (RStudio Cloud)10мин
3 практического упражнения
Week 1 Practice Quiz12мин
Week 1 Quiz14мин
Week 1 Lab12мин
Неделя
22 ч. на завершение
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 ((всего 59 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
7 видео
Hypothesis Testing (for a mean)14мин
HT (for the mean) examples9мин
Inference for Other Estimators10мин
Decision Errors8мин
Significance vs. Confidence Level6мин
Statistical vs. Practical Significance7мин
5 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Week 2 Suggested Readings and Practice Exercises10мин
Week 2 Lab Instructions (RStudio)10мин
Week 2 Lab Instructions (RStudio Cloud)10мин
3 практического упражнения
Week 2 Practice Quiz10мин
Week 2 Quiz16мин
Week 2 Lab12мин
Неделя
33 ч. на завершение
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 ((всего 84 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
11 видео
Introduction4мин
t-distribution7мин
Inference for a mean9мин
Inference for comparing two independent means8мин
Inference for comparing two paired means9мин
Power11мин
Comparing more than two means6мин
ANOVA9мин
Conditions for ANOVA2мин
Multiple comparisons6мин
Bootstrapping8мин
5 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Week 3 Suggested Readings and Practice Exercises10мин
Week 3 Lab Instructions (RStudio)10мин
Week 3 Lab Instructions (RStudio Cloud)10мин
3 практического упражнения
Week 3 Practice Quiz16мин
Week 3 Quiz28мин
Week 3 Lab14мин
Неделя
44 ч. на завершение
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 ((всего 118 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
11 видео
Introduction3мин
Sampling Variability and CLT for Proportions15мин
Confidence Interval for a Proportion9мин
Hypothesis Test for a Proportion9мин
Estimating the Difference Between Two Proportions17мин
Hypothesis Test for Comparing Two Proportions13мин
Small Sample Proportions10мин
Examples4мин
Comparing Two Small Sample Proportions5мин
Chi-Square GOF Test14мин
The Chi-Square Independence Test11мин
5 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Week 4 Suggested Readings and Practice Exercises10мин
Week 4 Lab Instructions (RStudio)10мин
Week 4 Lab Instructions (RStudio Cloud)10мин
3 практического упражнения
Week 4 Practice Quiz18мин
Week 4 Quiz24мин
Week 4 Lab26мин
31%
начал новую карьеру, пройдя эти курсы
22%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
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!!!
Преподаватели
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
О 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.
О специализации ''Statistics with R'
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.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
Что я получу, оформив подписку на специализацию?
Записавшись на курс, вы получите доступ ко всем курсам в специализации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
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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