Statistical Basics for PhD Students - Part I
- Responsible person
- Dr. Irina Kuzyakova
- 15 h attendance time
55 h self-study time
20 h for the final written presentation
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 5th May 2023 S2 12th May 2023 S3 26th May 2023 S4 2nd June 2023 S5 9th June 2023
- Type of Examination
- Chores (5 pages)
- Number of students
- Please send an email until 14.04.2023 to email@example.com
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 provides an overview of the core concepts of statistical analysis and the R software.
The workload amounts to 90 h.
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.
S1 Introduction to R and Descriptive Statistics In this session the participants are introduced to the software R. The focus is on the basic structure of R (syntax structure, object classes, reading data,...) as well as their application for descriptive statistics (calculation of measures of location, measures of dispersion and data visualization).
S2 Introduction to Inference Statistics This session introduces basic concepts of probability theory, on which statistical modelling is based. Most important theoretical distributions (normal distribution, test distributions), calculation of confidence intervals and testing of statistical hypotheses are explained or repeated, t-tests are considered.
S3 Introduction to Analysis of Variance This session covers the basics of variance analysis and its application in R. Post-hoc tests and their applications are addressed.
S4 Introduction to Linear Regression Analysis In this session the basics of regression analysis are explained. Simple and multiple linear regression models are discussed and criteria for model selection are presented.
S5 Introduction to Design of Experiments The session gives an overview of questions of experimental design. Principles of design of experiments:Randomization, replication and blocking are explained. Most important experimental designs: completely randomized design (CRD), randomized complete block design (RCBD) and their statistical analysis based on appropriate linear models are treated. A short introduction to factorial design is given. Furthermore, the planning of sample sizes for experiments is addressed.
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.
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).