Магистратура
2024/2025
Прикладные линейные модели
Статус:
Курс обязательный (Аналитика данных и прикладная статистика / Data Analytics and Social Statistics)
Направление:
01.04.02. Прикладная математика и информатика
Где читается:
Факультет социальных наук
Когда читается:
1-й курс, 2, 3 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
52
Охват аудитории:
для своего кампуса
Прогр. обучения:
Аналитика данных и прикладная статистика
Язык:
английский
Кредиты:
6
Контактные часы:
12
Course Syllabus
Abstract
The objective of the discipline "Applied Linear Models" is to ensure that students understand topics and principles of applied linear models and includes two parts. The first part is devoted to practical regression analysis introducing basic concepts and providing basic knowledge of regression models building and analysis with SAS. The second part covers theoretical foundations of linear models providing more advanced concepts, considering diversity of regression types, including model selection process both with SAS and R. The course is strongly related and complementary to other compulsory courses provided in the first year of the master's programme and sets a crucial prerequisite for later courses and research projects as well as for the master thesis.
Learning Objectives
- The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes
- Be able to explore the advantages and disadvantages of various linear modeling instruments, and demonstrate how they relate to other methods of analysis
- Be able to work with major linear modeling programs, especially SAS, so that they can use them and interpret their output.
- Have an understanding of the basic principles of linear models and lay the foundation for future learning in the area
- Have the skill to meaningfully develop an appropriate model for the research question
- To know modern extensions to applied regression, including working with “problem data”
- To know the basic principles behind working with all types of data for building regression models
- To know the theoretical foundation of applied linear modeling, starting with the univariate models
- Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
- Be able to criticize constructively and determine existing issues with applied linear models in published work
Course Contents
- Section 1. Software for regression analysis. Intro to SAS
- Section 2. Analysis of Variance
- Section 3. Two-Way ANOVA
- Section 4. Simple RegressionDuring this lecture, the learners will get the essence of the linear regression model. After understanding the mathematical meaning of Regression, we will talk about the rules of usage of linear Regression, what tasks it is used for, and what results it can give. Also, we will talk about possible cases when Regression can be used.This lecture will give the learners not only a theoretical understanding of Regression but practical experience. The whole week will be dedicated to working with SAS, from modeling to the finished regression model.
- Section 5. Multiple Regression
- Section 6. How to Choose the Right Regression Model
- Section 7. Theoretical foundations of linear models
- Section 8. Building Regression “by hand”
- Section 9. Linear Regression Assumptions
- Section 10. Categorical Predictors, Effects, Unusual Observations
- Section 11. Categorical Dependent Variables and Generalized Linear Models
- Section 12. Model Building and Model Selection
Interim Assessment
- 2024/2025 3rd module0.26 * Final project + 0.26 * Mid-term project + 0.24 * Quizzes + 0.24 * Quizzes
Bibliography
Recommended Core Bibliography
- Montgomery, D. C., Vining, G. G., & Peck, E. A. (2012). Introduction to Linear Regression Analysis (Vol. 5th ed). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1021709
- Weisberg, S. (2014). Applied Linear Regression (Vol. Fourth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=771773
- Yan, X., Su, X., & World Scientific (Firm). (2009). Linear Regression Analysis: Theory And Computing. Singapore: World Scientific. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=305216
Recommended Additional Bibliography
- Elliott, A. C., & Woodward, W. A. (2016). SAS Essentials : Mastering SAS for Data Analytics (Vol. Second edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1051725
- Hocking, R. R. (2013). Methods and Applications of Linear Models : Regression and the Analysis of Variance (Vol. Third edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=603362