Beschrijving
Linear regression addresses how a continuous dependent variable is associated by one or more predictors of any type. The fact that many practical problems deal with continuous outcomes (e.g. income, blood pressure, temperature, affect) makes linear regression a popular tool, and most of us will be familiar with the concept of drawing a line through a cloud of data points.
The first two sessions of this module introduce the conceptual framework of this method using the simple case of a single predictor. Formulas and technicalities are kept to a minimum and the main focus is on interpretation of results and assessing model validity. This includes confidence statements on the predictor effect (hypothesis tests and confidence intervals), using the regression model to predict future results and verification of model assumptions.
In session 3 and 4 we allow for more than one predictor leading to the multiple linear regression model. We focus on either explanation or prediction. How to come to a parsimonious model starting from a large number of predictors will be discussed in detail. In these complex linear models special attention will be given to interpreting individual predictor effects, as they critically depend on other terms in the model and underlying relations between predictors (confounding).
In the last session a more elaborate data analysis is discussed. We touch on problems where linear regression is not appropriate and replaced by related approaches such as generalized linear models and mixed models.
Different features will be illustrated with case examples from the instructors practical experience, and participants are encouraged to bring examples from their own work.
Hands-on exercises are worked out behind the PC using the R software.
Target audience
This course targets professionals and investigators from all areas who are involved in prediction problems or need to model the relationship between a dependent variable and one or more explanatory variables.
Course prerequisites
Participants are expected to have an active knowledge of the basic principles underlying statistical strategies, at a level equivalent to the Module 4 of this program.
Exam / Certificate
If you attend all 5 sessions you will receive a certificate of attendance via e-mail after the course ends.
Additionally, you can take part in an exam. If you succeed in this test a certificate from Ghent University is issued.
The exam consists of a take home project assignment. You are required to write a report by a set deadline.
Type of course
This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a class on campus.
Schedule
5 Thursday evenings in February and March 2025: February 6, 13, 20, 27 March 13, 2025 from 5.30 pm to 9.30 pm. Each lecture is followed by a hands-on practical session.
Venue
UGent, Faculty of Science, Campus Sterre, Krijgslaan 281, Ghent
Course material
Access to lecture notes and data files
Fees
The participation fee is 1100 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations. An exam fee of 35 EUR will be applied.
- Industry, private sector, profession*: € 1100
- Non profit, government, higher education staff: € 825
- (Doctoral) students, unemployed: € 495 | Full reimbursement by Doctoral Schools is possible (see details below).
*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.
Registration
To register, add the course below to your shopping cart and proceed to checkout.
Is this your first registration for a Beta Academy course? In that case, you will need to create an account first. Afterward, you will receive a confirmation email to activate your account on the academy platform. You do not have to click on the activation link but can immediately return to your shopping cart to complete your course registration. If you do not receive a confirmation email for your course order, please contact our Science Academy at ipvw.ices@ugent.be.
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UGent PhD students
Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents.
We follow the No Show polity of the Doctoral School!
KMO-portefeuille
Information on "KMO-portefeuille": https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo
Organisation
Science Academy (IPVW)
Faculty of Science
ipvw.ices@ugent.be
Website
