| Subject name (in Hungarian, in English) | Analysis of Technical and Economical Data | |||
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Analysis of Technical and Economical Data
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| Neptun code | BMEGEHDBSKMGAE-01 | |||
| Type | study unit with contact hours | |||
| Course types and number of hours (weekly / semester) | course type: | lecture (theory) | exercise | laboratory excercise |
| number of hours (weekly): | 2 | 0 | 1 | |
| nature (connected / stand-alone): | - | - | coupled | |
| Type of assessments (quality evaluation) | mid-term grade | |||
| ECTS | 4 | |||
| Subject coordinator | name: | Dr. Wéber Richárd | ||
| post: | adjunct | |||
| contact: | rweber@hds.bme.hu | |||
| Host organization | Department of Hydrodynamic Systems | |||
| https://www.hds.bme.hu/ | ||||
| Course homepage | http://www.hds.bme.hu/ | |||
| Course language | hungarian, english, german | |||
| Primary curriculum type | mandatory | |||
| Direct prerequisites | Strong prerequisite | none | ||
| Weak prerequisite | ||||
| Parallel prerequisite | BMETE94BG02 | |||
| Milestone prerequisite | at least obtained 0 ECTS | |||
| Excluding condition | BMEGEVGBX14, BMEGEVGBM14 | |||
Aim
The aim of teaching the subject is to introduce the methods of statistical data processing and analysis used in engineering practice. Data can come from the quantitative reflection of economic and social processes, or from measurement (research, quality control, etc.), but the basic methods of processing and analysis are independent of the source. By applying these learned statistical methods, the information contained in the observed data set can be condensed, significant variables and effects can be detected, approximate correlations can be established, and hypotheses can be decided using objective methods.
Learning outcomes
Competences that can be acquired by completing the course
Knowledge
Define the basic concepts of probability calculation (probability variable, relative frequency, probability, distribution and density function, expected value, standard deviation). Knows the basic concepts of descriptive statistics (e.g.: mode, median, percentile, quantile, mean, empirical standard deviation, empirical distribution and density function). The student is aware of the distributions of typical variables of notable, technical-economic processes, and the process of standardization. The student knows the concept of estimation, the confidence interval. Informed about measurement principles, measurement errors, direct and indirect measurement, error propagation. The student describes the acceptance methods of series-produced technical products and the basic concepts of quality assurance. The student is familiar with regression analysis, the Gauss-Markov theorem, the method of least squares and the concept of the coefficient of determination. Define Pearson's, Spearman's correlation coefficient and Kendall's concordance. The student distinguishes between the following parametric statistical tests and their areas of application: U-, T-, F-test, Abbe and Grubbs tests. The student distinguishes between the following non-parametric statistical tests and their areas of application: Χ2 test for homogeneity testing, Χ2 test for normality testing, Ryan-Joiner test. Understands the statistical method of analysis of variance and its applicability.
Ability
Plot the data using box plots and histograms. Defines the confidence interval around the mean. The student uses his knowledge to draw conclusions regarding the typical distributions of technical-economic processes. Able to calculate random and regular errors, error propagation in the case of direct and indirect variables. Able to fit a polynomial regression curve using the method of least squares (also using the straight Wald method); and to quantify the goodness of fit. The student establishes a statistical hypothesis about the data set and determines its correctness using an objective method. The student examines the relationships between variables using correlation and rank correlation coefficients. The student calculates the economically justified sample size, scrap number, and quality control curves. Uses the built-in functions of Microsoft Excel correctly and accurately to analyze technical and economic data. The student solves the related problems by applying variance analysis. The student expresses his thoughts in an organized form orally and in writing.
Attitude
Understands the importance and significance of statistics in technical practice. Open to learning about and routinely using information technology tools. The student strives to cooperate with the instructor and fellow students in expanding his knowledge. The student strives for accurate and error-free task solutions. The student expands his knowledge through continuous professional knowledge acquisition.
Independence and responsibility
Independently thinks through tasks and problems of a statistical nature and solves them based on given sources. Accepts well-founded professional critical comments. Based on his knowledge and analysis, he makes a responsible, well-founded decision. Cooperates with the instructor in expanding knowledge. The student is committed to the principles and methods of systems thinking and problem solving.
Teaching methodology
The material of the lectures is primarily used to understand the course material. This is supplemented by example tasks solved with instruction in computer lab exercises held every two weeks. For this, the confident and independent use of IT tools and techniques is essential. Before the closed-room theses, we provide example sets of tasks and consultation opportunities, which require adequate communication both in writing and orally.
Support materials
Textbook
Lukács O.: Matematikai statisztika (Bolyai könyvek) Műszaki Könyvkiadó, Budapest, 1996, ISBN 963 16 0538 8
HALÁSZ G. – HUBA A.: Műszaki mérések, Műegyetemi Kiadó, 2003, ISBN 963420748
Montgomery, Runger.: Applied Statistics and Probability for Engineers Third Edition, 2003, ISBN 978 0471381815
Lecture notes
Online material
http://edu.gpk.bme.hu/
https://mersz.hu/kiadvany/310
Validity of the course description
| Start of validity: | 2025. September 1. |
| End of validity: | 2030. July 15. |
General rules
The learning outcomes of the subject are evaluated on the basis of two mid-year written performance measurements (in-house tests), which measure competence elements of the type of knowledge, ability and independence. The closed-room papers are of a different nature, the 1st is a short multiple-choice test and a written task solution, while the 2nd is a computer-based task solution. The condition for obtaining the mid-semester grade is that the paper-based assessments and the 2nd computerized summative performance evaluation are sufficient, min. 50% completion.
Assessment methods
Detailed description of mid-term assessments
| Mid-term assessment No. 1 | ||
| Type: | summative assessment | |
| Number: | 1 | |
| Purpose, description: | The method of written evaluation of the knowledge and ability-type competence elements of the subject is in the form of a closed paper (Test 1), which can consist of short theoretical questions that assess lexical knowledge, the interpretation of individual concepts and the recognition of the connections between them; from multiple-choice test questions, which require the interpretation of individual concepts and the recognition of the connections between them; from calculation tasks that examine the ability to recognize and solve problems. The course material on which the evaluation is based is determined by the lecturer of the subject. The thesis is organized according to the timetable. The score for Test1 is min. completion of 50% is a condition for obtaining the midterm grade. | |
| Mid-term assessment No. 2 | ||
| Type: | summative assessment | |
| Number: | 1 | |
| Purpose, description: | A complex, written assessment of the knowledge, ability and independence-type competency elements of the subject in the form of a computerized (MS Excel software) test (Test 2), which may consist of test questions that require the interpretation of individual concepts and the recognition of the connections between them; from calculation tasks that examine the ability to recognize and solve problems. The course material on which the evaluation is based is determined by the lecturer of the subject; the available working time is approx. 50 minutes. The score for Test 2 is min. completion of 50% is a condition for obtaining the midterm grade. | |
Detailed description of assessments performed during the examination period
The subject does not include assessment during the examination period.
The weight of mid-term assessments in signing or in final grading
| ID | Proportion |
|---|---|
| Mid-term assessment No. 1 | 60 % |
| Mid-term assessment No. 2 | 40 % |
The condition for signing is that the score obtained in the mid-year assessments is at least 50%.
The weight of partial exams in grade
There is no exam belongs to the subject.
Determination of the grade
| Grade | ECTS | The grade expressed in percents |
|---|---|---|
| very good (5) | Excellent [A] | above 87 % |
| very good (5) | Very Good [B] | 87 % - 87 % |
| good (4) | Good [C] | 74 % - 87 % |
| satisfactory (3) | Satisfactory [D] | 62 % - 74 % |
| sufficient (2) | Pass [E] | 50 % - 62 % |
| insufficient (1) | Fail [F] | below 50 % |
The lower limit specified for each grade already belongs to that grade.
Attendance and participation requirements
The lack of the value means that there is no attendance requirement.
At least 70% of laboratory practices (rounded down) must be actively attended.
Special rules for improving, retaken and replacement
The special rules for improving, retaken and replacement shall be interpreted and applied in conjunction with the general rules of the CoS (TVSZ).
| Need mid-term assessment to invidually complete? | ||
| yes | ||
| The way of retaking or improving a summary assessment for the first time: | ||
| each summative assessment can be retaken or improved | ||
| Is the retaking-improving of a summary assessment allowed, and if so, than which form: | ||
| retake or grade-improving exam not possible | ||
| Taking into account the previous result in case of improvement, retaken-improvement: | ||
| new result overrides previous result | ||
| Completion of unfinished laboratory exercises: | ||
| missed laboratory practices may be performed in the teaching term at pre-arranged appointment, non-mandatory | ||
| Repetition of laboratory exercises that performed incorrectly (eg.: mistake in documentation) | ||
| incorrectly performed laboratory practice (e.g. Incomplete/incorrect report) can be corrected upon improved re-submission | ||
Study work required to complete the course
| Activity | hours / semester |
|---|---|
| participation in contact classes | 42 |
| preparation for laboratory practices | 14 |
| preparation for summary assessments | 32 |
| additional time required to complete the subject | 32 |
| altogether | 120 |
Validity of subject requirements
| Start of validity: | 2025. September 1. |
| End of validity: | 2030. July 15. |
Primary course
The primary (main) course of the subject in which it is advertised and to which the competencies are related:
Mechatronics engineering
Link to the purpose and (special) compensations of the Regulation KKK
This course aims to improve the following competencies defined in the Regulation KKK:
Knowledge
- Student has the sound knowledge of mechanical and electrical metrology and measurement theory in the field of mechatronics, and a sound mathematical and IT background.
- Student has the theoretical and practical knowledge, methodological and practical skills of mechanical engineering for the design, manufacture, modelling, operation and control of equipment, processes and systems integrated synergistically with electronics, electrical engineering and computer control.
- Student has the knowledge of the rules and tools for preparing technical documentation.
Ability
- Student has the ability to process and organise information collected during the operation of mechatronic systems and processes, to analyse it in different ways and to draw theoretical and practical conclusions.
- Student has the ability to ensure the quality of mechatronic systems, technologies and processes, to formulate theoretical and practical solutions to measurement and process control problems.
- Student has the ability to be creative in problem solving and flexible in complex tasks, as well as a lifelong learner, committed to diversity and value-based approaches.
Attitude
- Based on student's acquired knowledge, Student plays an integrative role in the integrated application of engineering disciplines (in particular mechanical, electrical and computer engineering) and in the technical support of all disciplines where engineering applications and solutions are required by professionals in the field.
- Student strives to carry out their work in a complex approach based on a systems and process-oriented mindset.
- Student strives for self-learning and self-development through active, individual and autonomous learning.
Independence and responsibility
- Student takes an independent and proactive approach to solving professional problems.
- Student takes the initiative in solving technical problems.
- Student takes decisions independently and in consultation with other disciplines (in particular law, economics, energy, electrical engineering, IT and medicine) for which Student takes responsibility.
Prerequisites for completing the course
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Knowledge type competencies
(a set of prior knowledge, the existence of which is not obligatory, but greatly facilitates the successful completion of the subject) |
none |
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Ability type competencies
(a set of prior abilities and skills, the existence of which is not obligatory, but greatly contributes to the successful completion of the subject) |
none |