Explaining and Predicting Outcomes with Linear Regression
Practical information:
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- 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.
- Price: €495 - €1100
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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.
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
*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
More information and registration on our Beta-Academy website.
