Admission requirements
Description
In the study of the effect of one or more explanatory variables on a response variable, linear regression and analysis of variance are important techniques. In linear regression we study how a quantitative variable, like the dose of a medicine, influences a quantitative response variable, like blood pressure. In analysis of variance we compare different groups with respect to a quantitative response, e.g. comparing the yields of different corn varieties. The statistical models that underlie these techniques are special cases of the linear model.
Although linear models are widely used, sometimes alternatives are preferred. Therefore, we discuss how to check the assumptions underlying linear model: independent errors, with a normal distribution and constant variance. When the assumptions of normality and constant variance are violated, the wider class of generalized linear models may be employed. Examples discussed in this course are logistic regression for a binary response (assuming a binomial distribution), and log-linear models for counts (using a Poisson distribution). Data are still assumed to be independent. Emphasis will be on gaining understanding of the models, the kind of data that can be analyzed with these models, and with the statistical analysis of empirical data itself.
Topics:
Simple and multiple linear regression
One and two-way ANOVA
Pairwise testing and multiple comparisons
Model assumptions and checking
Linear models in matrix form
Outliers, influential points, leverages
Model selection and selection criteria
Maximum likelihood
Generalized linear models (logistic and Poisson regression)
Course Objectives
After the completion of this course, students should be able to
understand the basic concepts of linear models (regression, ANOVA, ANCOVA) and generalized linear models, and the proper statistical inference methods.
apply the methods to model empirical data.
formulate and check the underlying assumptions of the model.
analyse data with R Studio, given practical data and a research question.
interpret the results and form conclusions relevant for the actual problem.
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of Instruction
Lecture sessions and mandatory computer practicals.
Assessment method
Computer practical (attendance compulsory) has to result in a pass.
Written exam.
Reading list
Ott and Longnecker (2016). An Introduction to Statistical Methods and Data Analysis.
Fox (2008). Applied Regression Analysis and Generalized Linear Models
Faraway: Practical Regression and ANOVA using R. Text available as PDF at http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf
Faraway (2006). Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Vahe Avagyan E-mail
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.