Statistical Inference

2019/2020

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Школа лингвистики

Статус:

Курс по выбору

Когда читается:

2-й курс, 3 модуль

Course Syllabus

Abstract

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data. Instructors: Brian Caffo, PhD, Professor, Biostatistics, Bloomberg School of Public Health; Roger D. Peng, PhD, Associate Professor, Biostatistics, Bloomberg School of Public Health; Jeff Leek, PhD, Associate Professor, Biostatistics,Bloomberg School of Public Health. https://www.coursera.org/learn/statistical-inference

Learning Objectives

• This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

Expected Learning Outcomes

• Understands the process of drawing conclusions about populations or scientific truths from data
• Knows how to use p-values, confidence intervals, and permutation tests
• Can describe variability, distributions, limits, and confidence intervals
• Can make informed data analysis decisions

Course Contents

• Statistical Inference
  Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

Assessment Elements

• Online course
• Discussion with a HSE instructor

Interim Assessment

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

Bibliography

Recommended Core Bibliography

• Statistics and Causality : Methods for Applied Empirical Research, edited by Wolfgang Wiedermann, and Eye, Alexander von, John Wiley & Sons, Incorporated, 2016. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4530803.

Recommended Additional Bibliography

• Rohatgi, V. K., & Saleh, A. K. M. E. (2001). An Introduction to Probability and Statistics (Vol. 2nd ed. Vijay K. Rohatgi, A.K. Md. Ehsanes Saleh). New York: Wiley-Interscience. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=396326