CourseSpecifications
Valid as from the academic year 2024-2025

Statistical Modelling (C003805)

Course size
Credits 8.0(nominal values; actual values may depend on programme)
Study time 240 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 1)
EnglishGent

Lecturers in academic year 2024-2025
Goetghebeur, Els
Loeys, Tom

independent workgroup work
peer teaching
seminarlectureWE02
PP01

Offered in the following programmes in 2024-2025
Master of Science in Statistical Data Analysis

lecturer-in-charge
co-lecturer
crdts
8offeringATeaching languages
EnglishKeywords
Position of the course
The course assumes students come with basic statistical understanding and skills, that is, theymaster the material offered in the course `Principles of statistical data analysis’. It focuses onregression models to describe, explain and predict continuous or categorical outcomes from a
set of measured covariates. Several other courses in the program build in turn on this course,
most directly all other courses involving more advanced regression modeling, such as`Causality and missing data analysis’, `Experimental Design’, ‘Analysis of clustered andlongitudinal data’, ‘Survival Analysis’, 'Big Data Science'.
This course aims to provide the student with a basic insight in and skills for statistical modeling.
This will primarily be developed in the context of generalized linear models, starting from linear
regression and with ample attention given to the analysis of tabulated data and logisticregression.
Contents• The simple and generalized linear regression model for the analysis of continuous and• categorical data
• The (iteratively reweighted) least squares algorithm• The maximum likelihood principle for statistical models• Confidence intervals for conditional means, prediction intervals for new observations• Graphical and formal diagnostic methods for the inspection of residuals• Goodness-of-fit tests, checking the model assumptions, detection of influential observations• The impact of transforming variables• Box-Cox regression for non-linear associations• The link between linear regression and analysis of variance• The multiple regression model with joint predictors that can be binary, categorical and/or• continuous• Confounding and effect modification• Procedures for stepwise building of a regression model• Penalized regression methods• Distribution and inference for categorical data(Approved)1• Analysis of contigency tables• Logistic regression• Generalized additive models
Initial competences
Basic knowledge of probability and statistics, such as that covered in `Principles of Statistical
Data Analysis’; and basic working knowledge of matrix algebra.
Final competences
1 The student can recognize practical problems which can be solved by means of the1 (multivariate) (generalized) linear model.
2 The student knows the distinction between association, prediction and causation, and
1 understands the basic role of confounding in this context.
3 The student can interpret the generalized linear regression model correctly, can fit it to a
1 data set and draw justified conclusions in the theoretical as well as the practical sense.
4 The student can apply diagnostic techniques to check the fit of a (multivariate) (generalized)
1 linear model.
5 The student can adopt appropriate remedial measures when the current regression model
1 does not fit.
6 The student knows the basic properties of the estimators and can work with the distinction
1 between estimation and prediction.
7 The student can link regression analysis with analysis of variance.
8 The student recognizes the limitations of the (generalized) linear model, can suggest
1 appropriate extensions and develop some methods via the maximum likelihood approach.
9 The student has theoretical knowledge about the most frequently used methods of
1 categorical data analysis.
10 The student can correctly interpret and critically assess the results of a categorical data
1 analysis.
11 The student can select appropriate statistical methods for categorical data analysis.
12 The student can adequately report in writing results of the statistical analysis of continuous
1 and categorical data.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Group work, Seminar, Lecture, Independent work, Peer teaching
Study material
Type: Handbook
Name: Applied Linear Statistical Models 5ed
Indicative price: € 43Optional: yes
Language : English
Author : Michael H. Kutner
ISBN : 978-1-25906-474-6
Number of Pages : 1394
Online Available : Yes
Available in the Library : Yes
Available through Student Association : No
Usability and Lifetime after the Study Programme : regularly
Type: Slides
Name: For the part on Analysis of Continuous Data
Indicative price: € 10
Optional: noLanguage : English
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Additional information: The slides are free of charge. The indicated cost is for the printing of these slides.
(Approved)

2

Type: Slides
Name: slides lectures categorical data analysis
Indicative price: Free or paid by faculty
Optional: noAvailable on Ufora : YesReferences• M. Kutner, C. Nachtsheim, J. Neter, W. Li. 'Applied Linear Regression Models', 5th edition.
• McGraw-Hill Education, 2004.
• Agresti A. (2002). Categorical Data Analysis. New York: Wiley.
• McCullagh P. & Nelder J.A (1989). Generalized Linear Models. CRC press• Regression and Other Stories (Analytical Methods for Social Research) 1st Edition, Kindle• Edition by Andrew Gelman (Author), Jennifer Hill (Author), Aki Vehtari (Author), 2021.
• Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric• Regression Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) 2nd• Edition by Julian J. Faraway (Author)••Course content-related study coachingStudents are coached by assistants during PC-labs and regular exercice classes which prepare
students for problem solving, the final analysis project and the open book exam. Through theelectronic learning environment they can exchange questions and answers outside lecture
hours among themselves and with lecturers. A take home project will provide the students withpractical experience in data analysis. The students will receive coaching and feed back on thisprojects through organized sessions.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment with open-ended questions
Examination methods in case of periodic assessment during the second examination period
Written assessment with open-ended questions
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods
The end-of-term exam aims to assess if the student understands the basic theory of Statisticalregression modelling of continuous and categorical outcome data. The exam consists of a
critical understanding of theory and data analysis reports, with exercises that are also
concerned with the design and analysis plan for a statistical study and the interpretation ofgiven software output.
The assessment takes the form of a data analytic project. The written report on the take homeproject is graded.
Calculation of the examination markTo determine the final grade, the following weighting factors are applied: non-periodicevaluation: 3/6; periodic evaluation, continuous data part: 2/6; periodic evaluation, categorical
data part: 1/6, provided that no component may have a grade below 10/20. If a student scoresless than 10/20 in at least one of the components, they cannot pass the entire course
component. If the weighted score is 10 or more out of 20, it will be reduced to the highest failing
grade (i.e., 9/20). Partial results for which the student scored at least half of the points, can betransferred to the next examination period within the same academic year.

(Approved)3