M9-Explaining and Predicting Outcomes with Linear Regression

Starts on 02.03.2023

AI and Data Science

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° DA22-23-M9 EN
Tags: Postacademische opleiding

Description

Description

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.

Fees and registration

Fees and registration form are available on the website of the Academy for Lifelong Learning of the Faculty of Sciences (UGent).

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 3 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 March 2023: March 2, 9, 16, 23 and 30, 2023 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, Building S9, Ghent

Course material

Access to lecture notes and data files

Microcredential

This module is part of the microcredential 'Applied Statistics: from Basics to Regression Modelling' that consists of three modules:

  • Module 3 - Drawing Conclusions from Data: an Introduction
  • Module 5 - Exploiting Sources of Variation in your Data: the ANOVA Approach
  • Module 9 - Explaining and Predicting Outcomes with Linear Regression

If you are planning on registering for all three modules, consider enrolling for the microcredential instead. Read more...

Click here to see the overview of all modules in this year's course in Data Analysis

Course number:
DA22-23-M9
Type:
Short- en long-term programmes
Area of interest:
AI and Data Science, Sciences
Language:
EN
Academic year:
2022 - 2023
Starting date:
02.03.2023
Lecturers:
Dries Reynders
Contact person:
ipvw-ices@ugent.be
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