Statistical Basics for PhD Students
- Responsible person
- Dr. Irina Kuzyakova
- Workload of 5 sub-modules
- 30 h attendance time
60 h self-study time
All sub-modules will take place from 9:30 to 17:00 (1 h lunchtime included)
First Block S1 2nd April 2020 S2 3rd April 2020 S3 6th April 2020 S4 7th April 2020 S5 8th April 2020 Second Block S6 expected end of September S7 expected end of September S8 expected end of September S9 expected end of September S10 expected end of September
- Type of Examination
- Chores (5 pages)
- Number of students
- 20 per sub-module
- Please fill in the registration form until 11th March 2020.
Course content: The aim of the course in the extent of 80 work units (45 min each; 10 workshop days in total) is to provide PhD students with the necessary basic knowledge of statistical data analysis or to refresh it. The focus is on teaching statistical methodology and its application using the software R. In total 10 sub-modules are offered. 5 sub-modules are offered in the beginning of summer semester and 5 sub-modules at the end of summer semester. The PhD students must take 5 sub-modules of their choice.
Core skills: In detail depending on the chosen sub-modules understanding of the basic methods of statistical data analysis and their application, understanding the basics of the software R and critical examination of its output.
Examination requirements: Written presentation of an own research project, which addresses in particular the choice of appropriate statistical methods depending on data properties and investigation objectives and covers the methods of the attended 5 sub-modules.
Sub-modules at the beginning of the summer semester
S1 Introduction to R and Descriptive Statistics (02.04.2020) In this sub-module 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 (03.04.2020) This sub-module 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 (06.04.2020) This sub-module 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 (07.04.2020) In this course the basics of regression analysis will be explained. Simple and multiple linear regression models are covered, and a number of model selection criteria are introduced.
S5 Introduction to Design of Experiments (08.04.2020) The sub-module gives an overview of questions of experimental design. Principles of design of experiments: Randomisation, replication and blocking are explained. Most important test designs: completely randomised design, block design and their statistical analysis based on appropriate linear models are treated. Furthermore, the planning of sample sizes for experiments will be addressed.
Sub-modules at the end of the summer semester
S6 Analysis of Variance with R (expected end of September) In this sub-module, the most important aspects of inference are repeated by means of variance analyses and their application in R is discussed. Multifactorial experiments are considered, in particular the interactions are discussed. Special features of the statistical analysis of unbalanced design are discussed; Type I, Type II and Type III of sum of squares are explained.
S7 Linear and nonlinear regression in R (expected end of September) This sub-module addresses the most important aspects of the relationships between numerical variables. The basics of linear and nonlinear models and their application in R are discussed
S8 Linear Models with R (expected end of September) This sub-module repeats basic aspects of linear models and their application in R. In particular, the use and interpretation of different variable types as covariables is discussed. In addition, models with repeated measurement are treated. Mixed models are introduced.
S9 Nonparametric Statistics with R (expected end of September) In this sub-module non-parametric methods and their applications are considered. Non-parametric alternatives for testing the differences of two or more groups, as well as relationships of variables, are discussed. Permutation tests are introduced.
S10 Inferential Statistics for Categorical Data in R (expected end of September) This sub-module focuses on the evaluation of nominal data. Evaluation of contingency tables and Chi2-metrics is discussed, permutation tests are explained. The logistic regression is introduced.
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).
Graduiertenschule Forst- und Agrarwissenschaften (GFA)
Tel: +49 - (0)551 - 39 14048
Fax: +49 - (0)551 - 39 96 29