Statistical Basics for PhD Students
 Responsible person
 Dr. Irina Kuzyakova
 Language
 English
 Workload of 5 submodules
 30 h attendance time
60 h selfstudy time  Credits
 3
 Schedule

All submodules will take place online within a time frame of one week each. The given date refers to the start of the module. For further information, please have a look at the detailed schedule
First Block S1 9^{th} April 2021 S2 16^{th} April 2021 S3 23^{rd} April 2021 S4 30^{th} April 2021 S5 7^{th} May 2021 Second Block S6 17^{th} September 2021 S7 24^{th} September 2021 S8 1^{st} October 2021 S9 8^{th} October 2021 S10 15^{th} October 2021  Type of Examination
 Chores (5 pages)
Presentation Requirements  Location
 online
 Number of students
 20 per submodule
 Registration
 Registration is closed
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 submodules are offered. 5 submodules are offered in the beginning of summer semester and 5 submodules at the end of summer semester. The PhD students must take 5 submodules of their choice.
Core skills: In detail depending on the chosen submodules 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 submodules.
Submodules at the beginning of the summer semester
S1 Introduction to R and Descriptive Statistics (09.04.2021) In this submodule 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 (16.04.2021) This submodule 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, ttests are considered.
S3 Introduction to Analysis of Variance (23.04.2021) This submodule covers the basics of variance analysis and its application in R. Posthoc tests and their applications are addressed.
S4 Introduction to Linear Regression Analysis (30.04.2021) 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 (07.05.2021) The submodule 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.
Submodules at the end of the summer semester
S6 Analysis of Variance with R (17.09.2021) In this submodule, 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 (24.09.2021) This submodule 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 (01.10.2021) This submodule 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 (08.10.2021) In this submodule nonparametric methods and their applications are considered. Nonparametric 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 (15.10.2021) This submodule focuses on the evaluation of nominal data. Evaluation of contingency tables and Chi^{2}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.
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).
Graduiertenschule Forst und Agrarwissenschaften (GFA)
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Tel: +49  (0)551  39 14048
Fax: +49  (0)551  39 96 29
gfa@unigoettingen.de