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Regular version of the site

# Applied Linear Models

2023/2024
ENG
Instruction in English
6
ECTS credits
Course type:
Compulsory course
When:
1 year, 2, 3 module

### Course Syllabus

#### Abstract

The objective of the discipline "Applied Linear Models I" is of the course is to ensure that students understand topics and principles of applied linear models, basic level. The course is strongly related and complementary to other compulsory courses provided in the first year (e.g. Applied Linear Models II, Contemporary Data Analysis) 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

• Introduction to the Framework of Regression Analysis
• Simple Linear Regression I
• Simple Linear Regression II
• Statistical Inference in a Simple Linear Regression I
• Statistical Inference in a Simple Linear Regression II
• Multivariate regression I
• Multivariate regression II
• Model Building I
• Model Building II
• Model Building III

#### Assessment Elements

• Quizzes
• Final In-Class or Take-home exam (at the discretion of the instructor)
• Midterm Project

#### Interim Assessment

• 2023/2024 3rd module
0.2 * Final In-Class or Take-home exam (at the discretion of the instructor) + 0.2 * Midterm Project + 0.6 * Quizzes

#### 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