2022/2023

Applied Statistics

Type: Mago-Lego

Delivered by: Big Data and Information Retrieval School

When: 1 module

Online hours: 82

Open to: students of one campus

Instructors: Ksenia Kasianova

Language: English

ECTS credits: 5

Contact hours: 8

Course Syllabus

Abstract

The main objective of statistics is quantification of uncertainty in estimation and inference. Since most, if not all, real-world datasets are noisy or sampled, data analyses need statistics. In this 6 week course you’ll learn about common and useful statistical tools and their implementations in Python.

Learning Objectives

• Formulate data analysis question as a statistical hypothesis, and select the most appropriate tool to test it.
• Quantify the uncertainty in estimates of parameters of interest with confidence intervals.
• Estimate causal effects from observational data with the help of causal graphs.

Expected Learning Outcomes

• Remember important statistics.
• Remember important distributions.
• Understand maximum likelihood estimation principle.
• Know properties of maximum likelihood estimates.
• Know how to build asymptotic confidence interval for a mean.
• Know how to build bootstrap confidence intervals.
• Remember how to test hypotheses.
• Know how to test 3 types of hypotheses about proportions.
• Understand 3 ways to test hypotheses using likelihood.
• Know how to test 3 types of hypotheses about normal means.
• Know how to test if the sample comes from a specified distribution.
• Understand and know how to use sign tests.
• Understand and know how to use rank tests.
• Understand and know how to use permutation tests.
• Understand bootstrap tests.
• Know how to test independence of categorical variables.
• Understand how to select the most appropriate test for the problem.

Course Contents

• 1. Estimation techniques
• 2. Parametric hypothesis testing
• 3. Nonparametric hypothesis testing
• 4. Testing hypothesis with many variables
• 5 . Observational causal inference: graphs
• 6. Observational causal inference: adjustments

Assessment Elements

• Peer Review
• SGA Treatment for Malocclusion
• Quizzes5
  Weekly quizzes.
• Quizzes1
  Weekly quizzes.

Interim Assessment

• 2022/2023 1st module
  0.25 * SGA Treatment for Malocclusion + 0.1 * Peer Review + 0.55 * Quizzes5 + 0.1 * Quizzes1

Bibliography

Recommended Core Bibliography

• Computer age statistical inference : algorithms, evidence, and data science, Efron, B., 2017
• Introductory econometrics : a modern approach, Wooldridge J.M., 2006
• Multiple Comparisons Using R, 187 p., Bretz, F., Hothorn, T., Westfall, P., 2011
• Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics : A Primer. Chichester, West Sussex, UK: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1161971

Recommended Additional Bibliography

• Regression and other stories, Gelman, A., 2021