Academic year/semester: 2024/25/1

ECTS Credits: 3

Available for: All OU students

Lecture hours: 1
Seminarium:0
Practice: 0
Laboratory: 2
Consultation: 0

Prerequisites: RKXMA2EBNE

Course Leader: González Mastrapa Henry, Dr.

Faculty: Rejtő Sándor Faculty of Light Industry and Environmental Engineering, 1034 Budapest, Doberdó utca 6.

Course Description:
If he environmental components (air, water, soil) are examined, than a big amount of
measurement data will be collected. The data with a big quantity cannot be interpreted. It
is necessary to evaluate our data. Our data should be processed to interpret them.
In simpler cases data processor programs are used, than e.g. Microsoft Excel.
The principle of data sorting will be presented.
We may need special statistical software packages, that we may establish contexts between
our measured data with their help. We present the statistical basic principles (descriptor
statistics, ANOVA etc.).
Several statistical software packages exist: eg. SPSS, SAS etc..
Origin software package will be presented which is one of the most important program of
scientific data processing.
The freeware software R can be written our necessary procedures for analysis of our data
statistically. We can compare our algorithms which can be manufactured by Matlab help
with the programs written with statistical software packages.

Competences:
Knowledge of general and specific mathematical, natural and social scientific principles, rules, relations, and procedures as required to pursue activities in the special field of environment protection. In possession of state-of-the-art IT skills, being able to use professional databases and certain design, modelling, and simulation software depending on their specialty. Knowledge of the learning, knowledge acquisition, and data collection methods of the special fields of environment protection, their ethical limitations and problem solving techniques. Knowledge of the main methods to examine the quantity and quality features of environmental elements and systems, their typical measuring instruments and limitations thereof, as well as methods for the evaluation of data measured. Able to reveal deficiencies in the technologies applied and process risks and to initiate mitigation measures after getting familiarized with the technology concerned. Efforts to improve knowledge by on-going self-education and continuously update their knowledge of the world. Constantly upgrading their knowledge of environment protection by attending organized professional development training courses.

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Assessment:

Exam Types:

Mid Term Exam

Compulsory bibliography: 1. Matthias Kohl: Introduction to statistical data analysis with R, bookboon.com, 2015, ISBN: 978-87-403-1123-5, 1 edition. Pages : 228 2. Felix C. Veroya: Introduction to Statistical Process Control, A Problem Solving Process Approach, bookboon.com, 2014, ISBN: 978-87-403-0789-4, 1 edition, Pages: 72

Recommended bibliography:

Additional bibliography:

Additional Information: