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)
Summer semester S1 27th March 2019 S2 28th March 2019 S3 29th March 2019 S4 1st April 2019 S5 2nd April 2019 Winter semester W1 23rd Sep 2019 W2 24th Sep 2019 W3 25th Sep 2019 W4 26th Sep 2019 W5 27th Sep2019
- Type of Examination
- Chores (5 pages)
- Number of students
- 20 per sub-module
- Please fill in the registration form until 2nd September 2019.
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 (27.03.2019) 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 (28.03.2019) 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 (29.03.2019) 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 (01.04.2019) 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 (02.04.2019) The sub-module gives an overview of questions of experimental design. Principles of design of experiments: Randomization, replication and blocking are explained. Most important test designs: completely randomized design, block design, split-plot 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
W1 Analysis of Variance with R (23.09.2019) 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.
W2 Linear and nonlinear regression in R (24.09.2019) 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
W3 Linear Models with R (25.09.2019) 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.
W4 Nonparametric Statistics with R (26.09.2019) 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.
W5 Inferential Statistics for Categorical Data in R (27.09.2019) 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, otherwise you will be barred from registering for GFA courses for the period of one year.
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).
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