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Course Syllabus

Master 2020/2021

Inferential Statistics

Type: Elective course (Linguistic Theory and Language Description)
Area of studies: Fundamental and Applied Linguistics
Delivered by: School of Linguistics
Where: Faculty of Humanities
When: 2 year, 3 module
Mode of studies: distance learning
Instructors: Yury Lander
Master’s programme: Linguistic Theory and Language Description
Language: English
ECTS credits: 3
Contact hours: 2

Course Syllabus

Full 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

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

Expected Learning Outcomes

  • 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.
  • Knows how to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest.
Course Contents

Course Contents

  • Inferential Statistics
    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.
Assessment Elements

Assessment Elements

  • non-blocking Online course
  • non-blocking Discussion with a HSE instructor
  • non-blocking Online course
  • non-blocking Discussion with a HSE instructor
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * Discussion with a HSE instructor + 0.7 * Online course
Bibliography

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