Module also offered within study programmes:
General information:
Name:
Statistics for engineers
Course of study:
2019/2020
Code:
GBUD-2-317-GT-n
Faculty of:
Mining and Geoengineering
Study level:
Second-cycle studies
Specialty:
Geotechnics and special civil engineering
Field of study:
Civil Engineering
Semester:
3
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Part-time studies
Course homepage:
 
Responsible teacher:
dr hab. inż. Jakubowski Jacek (Jacek.Jakubowski@agh.edu.pl)
Module summary

Basic concepts and principles of probablity and statistics for engineers. Problem solving with the use of interactive user environment and/or scripting.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 Is capable of presenting and interpreting results of basic statistical data analysis BUD2A_K02 Activity during classes,
Project,
Report,
Participation in a discussion
Skills: he can
M_U001 Is capable of using selected procedures of statistical inference BUD2A_U03 Activity during classes,
Project,
Report,
Oral answer,
Test
M_U002 Is capable of using statistical computer software for problem solving BUD2A_U03 Activity during classes,
Oral answer,
Project,
Report,
Test
Knowledge: he knows and understands
M_W001 Understands selected basic concepts of probability BUD2A_W01 Activity during classes,
Oral answer,
Test,
Participation in a discussion
M_W002 Understands selected basic concepts of statistics BUD2A_W01 Activity during classes,
Test,
Oral answer,
Participation in a discussion
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
15 9 0 0 6 0 0 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 Is capable of presenting and interpreting results of basic statistical data analysis + - - + - - - - - - -
Skills
M_U001 Is capable of using selected procedures of statistical inference - - - + - - - - - - -
M_U002 Is capable of using statistical computer software for problem solving - - - + - - - - - - -
Knowledge
M_W001 Understands selected basic concepts of probability + - - - - - - - - - -
M_W002 Understands selected basic concepts of statistics + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 75 h
Module ECTS credits 3 ECTS
Udział w zajęciach dydaktycznych/praktyka 15 h
Preparation for classes 30 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 30 h
Module content
Lectures (9h):

Introduction. Probablity and statistics in engineering. Uncertainty in engineering. Events and probability. Random variables and probability distributions. Statistical inference in engineering. Goodness of fit of distribution models. Fundamentals of linear regression analysis. Elements of quality assurance and acceptance sampling. Computer based simulation methods. Geotechnical systems design under uncertainty.

Project classes (6h):

STATISTICA interactive user environment. Matlab statistics scripting user environment. Estimating probabilities. Useful probability distributions. Statistical estimation of parameters. Testing of hipotheses. Confidence intervals. Measurement uncertainty. Testing goodness of fit of distribution. Application of regression analysis in engineering. Correlation analysis. Multiply regression analysis. Monte-Carlo simulation.

Additional information
Teaching methods and techniques:
  • Lectures: Treści prezentowane na wykładzie są przekazywane w formie prezentacji multimedialnej w połączeniu z klasycznym wykładem tablicowym wzbogaconymi o pokazy odnoszące się do prezentowanych zagadnień.
  • Project classes: Studenci wykonują zadany projekt samodzielnie, bez większej ingerencji prowadzącego. Ma to wykształcić poczucie odpowiedzialności za pracę w grupie oraz odpowiedzialności za podejmowane decyzje.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

A pass may be obtained at the primary date or at one resit date. In order to pass the classes, all assignments and projects completion and defense is necessary. The final test includes the range of material from the lectures and classes. Temporary policy and exceptions will be presented at the first lecture. Special circumstances of obtaining a pass will be presented by the tutors at the beginning of term.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego.
  • Project classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Studenci wykonują prace praktyczne mające na celu uzyskanie kompetencji zakładanych przez syllabus. Ocenie podlega sposób wykonania projektu oraz efekt końcowy.
Method of calculating the final grade:

The final grade is the arithmetic mean of the grades of the final theory test and project assesment.

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Justified absences at classes may be made up with a different group providing the material implemented at the classes is the same.

Prerequisites and additional requirements:

Darts training

Recommended literature and teaching resources:

Alfredo H-S. Ang, Wilson H. Tang: Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering (v. 1);
STATISTICA User Manual; Matlab User Manual
Kot S.M., Jakubowski J., Sokołowski A.: Statystyka, Warszawa, Difin 2011

Scientific publications of module course instructors related to the topic of the module:

1. Multivariate linear regression and CART regression analysis of TBM performance at Abu Hamour phase-I tunnel — Analiza wskaźników wydajności drążenia tarczami TBM dla tunelu Abu Hamour etap I, z zastosowaniem wielorakiej regresji liniowej i regresji CART / J. JAKUBOWSKI, J. B. Stypulkowski, F. G. Bernardeau // Archives of Mining Sciences; ISSN 0860-7001. — 2017 vol. 62 no. 4, s. 825–841.
2. Predictive regression models of monthly seismic energy emissions induced by longwall mining — Regresyjne modele predykcyjne miesięcznej emisji energii sejsmicznej indukowanej eksploatacją w ścianie / Jacek JAKUBOWSKI, Antoni TAJDUŚ // Archives of Mining Sciences ; ISSN 0860-7001. — 2014 vol. 59 no. 3
3. Descriptive statistical analysis of TBM performance at Abu Hamour Tunnel Phase I / J. Stypułkowski, F. Bernardeau, J. JAKUBOWSKI //Arabian Journal of Geosciences ; ISSN 1866-7511. 2018 v.11 iss. 9 art. no. 191
4. Platform for data integration and analysis, and publishing medical knowledge as done in a large hospital / Jacek JAKUBOWSKI, Lesław Kułach, Piotr Murawski, // W: Practical predictive analytics and decisioning systems for medicine : informatics accuracy and cost-effectiveness for healthcare administration and delivery including medical research / [eds.] Linda A. Winters-Miner, [et al.]. — Amsterdam, [etc.] : Academic Press, cop. 2015. — ISBN: 978-0-12-411643-6.
5. Probabilistic approach to deformable blocky rock mass modeling / Jacek JAKUBOWSKI, Antoni TAJDUŚ, Jan WALASZCZYK, Peter Starzec // W: International conference on Probabilistics in geotechnics : technical and economic risk estimation : 15–19 September 2002 Graz, Austria / ed. Rudolf Pöttler. — [New York : United Engineering Foundation],

Additional information:

A pass may be obtained at the primary date or at one resit date. If a student misses over 20% of the classes they may not obtain a pass. Presence at lectures is advised and may be rewarded. Presence at the classes is compulsory. In order to pass the classes, all assignments and projects completion and defense is imperative. Justified absences at classes may be made up with a different group given an agreement is made with both tutors and providing the material implemented at the classes is the same. The final test includes the range of material from all the lectures and classes. Temporary policy and exceptions will be presented at the first lecture. Special circumstances of obtaining a pass will be presented by the tutors at the beginning of term.