Linear regression is one of the most ubiquitous statistical methods. Most statistical techniques can be viewed as either special cases of linear regression (e.g., t-tests, ANOVA) or generalizations of linear regression (e.g., multilevel modeling, SEM, neural networks, GLM, survival analysis). In this workshop, participants will learn how to apply linear regression techniques in R. We will cover (multiple) linear regression, categorical predictor variables, moderation, prediction, and diagnostics. Workshop participants will practice what they learn via practical exercises.
All content will be presented via live demonstrations of R programming and data analysis. Workshop participants will practice on-the-fly by following along with the demonstration scripts and completing in-situ practical exercises. If the schedule permits, the participants are also welcome to ask the instructor for advice on how to incorporate R into their own data analyses.
Participants should install both R and RStudio (the free desktop version) on their computers before the beginning of the course.
- R can be downloaded here.
- RStudio can be downloaded here.
- We will not cover basic R usage. Participants should already know how to use R to read and write data, do basic data manipulations, run R functions, and work with the results returned by R functions.
Although we may briefly discuss the theory underlying the methods covered, we will primarily focus on applying these methods in R.
- Participants should already have some familiarity with the theory of linear regression.
- We will not cover any generalizations of linear regression such as logistic regression, multilevel modeling, or SEM.
Lecturers
Kyle Lang
Target audience
Professionals seeking a master-level introduction to linear regression
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Aim of the course
After completing this course, participants can:
1. Describe how to apply linear regression in R and choose the correct functions with which to implement a given analysis.
2. Write basic R scripts to do the following:
- Run a multiple linear regression model
- Manipulate the fitted model object produced when estimated a linear regression model
- Incorporate categorical predictor variables into linear regression models using an appropriate coding scheme
- Test for moderation using linear regression and conduct a simple slopes analysis
- Generate predictions by applying a fitted regression model to new data
- Calculate measures of prediction error to compare the performance of different models
- Check the assumptions of the linear regression model via model diagnostics
Study load
Approximately 8 hours of classroom time.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
Costs
- Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
- The tuition fee for staff off the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS
Utrecht Summer School does not offer scholarships for this course.
Contact details
Irma Reyersen | E: ms.summerschool@uu.nl