Statistical Basics for PhD Students - Part II
- Responsible person
- Dr. Irina Kuzyakova
- Language
- English
- Workload
- 15 h attendance time
55 h self-study time
20 h for the final written presentation - Credits
- 3
- Schedule
-
All sessions will take place online within a time frame of one week each. The given date refers to the start of the session. For further information, please have a look at the detailed schedule
S1 16th January 2024 S2 23rd January 2024 S3 30th January 2024 S4 6th February 2024 S5 13th February 2024 - Type of Examination
- Chores (5 pages)
Presentation Requirements - Location
- online
- Number of students
- 20
- Registration
Course content: The aim of the courses is to give PhD students the basic knowledge necessary for statistical data analysis or to refresh it. The focus is on teaching statistical methodology and its application using the software R. The course focuses on the description and application of advanced statistical methods using R software.
The workload amounts to 90 h.
That includes:
15 h of attendance (6 meetings a approx. 2.5 h)
55 h of self-study and homework performance (approx. 11 h weekly)
20 h for the final written presentation
Fulfillment of all course requirements results in 3 Credits.
Examination requirements: Written presentation of the own research project, addressing in particular the choice of appropriate statistical methods depending on data properties and objectives of the study and covering the methods of the 5 Sessions attended.
Sessions overview
S1 Advanced Analysis of Variance with R The session focuses on statistical analysis of multifactorial experiments and model selection. Specific aspects of statistical analysis of unbalanced data are discussed; Type I, Type II, and Type III of sum of squares are explained.
S2 Linear and nonlinear regression in R This session deals with the statistical analysis of relationships between numerical variables. Linear and nonlinear models and their application in R are discussed. Also considered are possibilities of data transformation to reach the models assumptions.
S3 Linear Models with R This session focuses on the use of covariates in a statistical analysis in R. The second issue of the submodule is the introduction to mixed models and their implementation in R.
S4 Nonparametric Statistics with R In this session, nonparametric methods and their applications are considered. Nonparametric alternatives for testing differences between two or more groups and relationships between variables are discussed. Permutation tests are introduced.
S5 Inferential Statistics for Categorical Data in R This session focuses on the analysis of nominal data. In particular, the module considers the statistical analysis of contingency tables and provides an introduction to logistic regression.
Admission requirements: Students of GFA, other PhD students if free places are available
Your registration for courses and workshops offered by the GFA is binding. If you want to cancel your registration later than three weeks prior to the workshop start date, you have to provide a physician's note. A late deregistration without a reason for illness is only possible with the consent of the first supervisor. If non of these two conditions is met, you will be barred from registering for GFA courses for the period of one year.
Please be aware that with a late deregistration you block seats for other PhD students, which otherwise could have taken part in the workshop.
Absence policy
To earn credits for a workshop/course, you must not miss more than 10% of total contact hours. In most cases, this will be less than a day. If it is inevitable that you miss more than a whole day, please notify us well in advance (at least one week).