Бакалавриат
2021/2022
Язык R: статистические выводы
Статус:
Курс по выбору (Фундаментальная и компьютерная лингвистика)
Направление:
45.03.03. Фундаментальная и прикладная лингвистика
Кто читает:
Школа лингвистики
Где читается:
Факультет гуманитарных наук
Когда читается:
3-й курс, 3 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
20
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
2
Course Syllabus
Abstract
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. Instructor - Mine Çetinkaya-Rundel, Associate Professor of the Practice, Department of Statistical Science, Duke University. https://www.coursera.org/learn/inferential-statistics-intro
Learning Objectives
- 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.
Expected Learning Outcomes
- Knows how to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest.
- Knows how to set up and perform hypothesis tests, interpret p-values, and report the results of the analysis in a way that is interpretable for clients or the public.
Assessment Elements
- Online course
- Discussion with a HSE instructor
- Online course
- Discussion with a HSE instructor
Bibliography
Recommended Core Bibliography
- Pace L., Hlynka M. Beginning R an introduction to statistical programming. New York: Apress, 2012.
Recommended Additional Bibliography
- Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.363067604