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Statistics and Safety of Production Systems

Marta RINALDI Statistics and Safety of Production Systems

0612600017
DEPARTMENT OF INDUSTRIAL ENGINEERING
EQF6
INDUSTRIAL ENGINEERING AND MANAGEMENT
2024/2025

OBBLIGATORIO
YEAR OF COURSE 3
YEAR OF DIDACTIC SYSTEM 2018
AUTUMN SEMESTER
CFUHOURSACTIVITY
1STATISTICA E SICUREZZA DEI SISTEMI PRODUTTIVI
660LESSONS
2STATISTICA E SICUREZZA DEI SISTEMI PRODUTTIVI
660LESSONS
MASSIMILIANO DE IULIIS1 T Curriculum
MARTA RINALDI2 Curriculum
CARMINE NOVELLINO2 Curriculum
Objectives
KNOWLEDGE AND UNDERSTANDING
DEFINITIONS OF RANDOM VARIABLE AND MAIN DISTRIBUTIONS AND THEIR MOMENTS; EVENT PROBABILITY ASSESSMENT;
STATISTICAL INFERENCE AND DECISION; ANALYSIS OF VARIANCE AND LINEAR REGRESSION ANALYSIS.
APPLIED KNOWLEDGE AND UNDERSTANDING - ENGINEERING ANALYSIS
ABILITY TO SOLVE PROBLEMS INVOLVING THE EVALUATION OF PROBABILITY OF EVENTS, THE ESTIMATION OF UNKNOWN PARAMETERS AND THE VERIFICATION OF HYPOTHESES CONCERNING NON-DETERMINISTIC PHENOMENA, THE IDENTIFICATION AND THE APPLICATION OF SIMPLE EMPIRICAL MODELS FOR THE QUANTITATIVE ANALYSIS OF PHYSICAL AND / OR TECHNOLOGICAL PHENOMENA.
APPLIED KNOWLEDGE AND UNDERSTANDING - ENGINEERING DESIGN
IN A DESIGN CONTEXT, FIND THE VARIABLES FOR WHICH YOU NEED TO USE THE TOOLS STATISTICAL ANALYSIS AND APPLY THESE TOOLS.
INDEPENDENCE OF JUDGMENT - ENGINEERING PRACTICE
ABILITY TO APPLY METHODS AND TOOLS TO ANALYZE THE EFFECT OF DIFFERENT FACTORS ON A PHENOMENON OF INTEREST
AND MAKE QUANTITATIVE COMPARISONS BETWEEN THEM
ABILITY TO LEARN - ABILITY TO INVESTIGATE
ABILITY TO USE METHODS AND TOOLS TO PLAN DATA COLLECTION IN ORDER TO ALLOW OBJECTIVE ANALYSIS
OF THE PROBLEM TREATED.
TRANSVERSAL SKILLS - COMMUNICATION SKILLS
KNOWING HOW TO PRESENT BOTH ORALLY AND IN WRITING A TOPIC RELATED TO THE PROBABILISTIC EVALUATION OF A
RANDOM PHENOMENON. KNOWING HOW TO PRESENT THE TOPICS OF STATISTICAL DATA ANALYSIS IN A CORRECT AND EXHAUSTIVE WAY.
TRANSVERSAL SKILLS - ABILITY TO LEARN
KNOWING HOW TO APPLY THE KNOWLEDGE ACQUIRED TO CONTEXTS DIFFERENT FROM THOSE PRESENTED DURING THE COURSE. KNOWING USE DIFFERENT SOURCES FOR IN-DEPTH STUDY OF THE METHODOLOGIES INTRODUCED IN THE COURSE.
LEARNING OBJECTIVES:
SAFETY MODULE (MANAGEMENT ENGINEERING)
THE COURSE WILL BE ORIENTED TOWARDS PROBLEM SOLVING, RISK ANALYSIS AND ASSESSMENT, PLANNING OF SUITABLE PREVENTION AND PROTECTION INTERVENTIONS, PAYING ATTENTION TO IN-DEPTH ANALYSIS DUE TO THE DIFFERENT LEVELS OF RISK.
Prerequisites
FOR THE SUCCESSFUL ACHIEVEMENT OF THE SET OBJECTIVES, BASIC MATHEMATICAL KNOWLEDGE AND SET THEORY ARE REQUIRED.
PREPARATORY: MATHEMATICS I.
Contents
- INTRODUCTION TO STATISTICS, DESCRIPTIVE STATISTICS, DATA ORGANIZATION AND DESCRIPTION, MEASURES OF CENTRAL TENDENCY AND DISPERSION, GRAPHICAL REPRESENTATION OF DATA, CHEBYSHEV'S INEQUALITY, NORMAL SAMPLES, BIVARIATE SAMPLE SETS, CORRELATION COEFFICIENT AND REGRESSION LINE (THEORY, EXERCISES, LABORATORY) (4,2,0)
- ELEMENTS OF PROBABILITY CALCULUS: PROBABILITY DEFINITION, VENN DIAGRAMS AND EVENT ALGEBRA, PROBABILITY AXIOMS, CONDITIONAL PROBABILITY, TOTAL PROBABILITY THEOREM, BAYES' THEOREM, INDEPENDENT EVENTS (6,3,0) RANDOM VARIABLES: DISCRETE AND CONTINUOUS VARIABLES, RANDOM VARIABLE VECTORS, JOINT DISTRIBUTION OF RANDOM VARIABLES, EXPECTED VALUE AND VARIANCE, COVARIANCE OF RANDOM VARIABLES, MOMENT GENERATING FUNCTION, WEAK LAW OF LARGE NUMBERS (6,3,0)
- RANDOM VARIABLE MODELS: BERNOULLI AND BINOMIAL VARIABLES, POISSON AND HYPERGEOMETRIC VARIABLES, UNIFORM, GAUSSIAN, AND EXPONENTIAL DISTRIBUTIONS, DISTRIBUTIONS DERIVED FROM NORMAL DISTRIBUTION: CHI-SQUARE, STUDENT'S T, FISHER'S F (6,3,0)
- SAMPLE DISTRIBUTIONS: SAMPLE MEAN, CENTRAL LIMIT THEOREM, SAMPLE VARIANCE, DISTRIBUTION OF STATISTICS FROM NORMAL POPULATIONS, DISTRIBUTION OF STATISTICS RELATED TO PROPORTION IN A POPULATION (6,3,0)
- PARAMETRIC ESTIMATION: MAXIMUM LIKELIHOOD ESTIMATORS, CONFIDENCE INTERVALS FOR MEAN WITH KNOWN AND UNKNOWN VARIANCE, CONFIDENCE INTERVALS FOR NORMAL DISTRIBUTION, FOR MEAN DIFFERENCE, FOR VARIANCE, FOR PROPORTION MEAN, FOR EXPONENTIAL DISTRIBUTION MEAN (6,3,0)
- HYPOTHESIS TESTING: INTRODUCTION TO THE PROBLEM, SIGNIFICANCE LEVELS, HYPOTHESES ON POPULATION MEAN (KNOWN AND UNKNOWN VARIANCE), HYPOTHESES ON EQUALITY OF MEANS, HYPOTHESES ON POPULATION VARIANCE, HYPOTHESES ON PROPORTION, HYPOTHESES ON POISSON DISTRIBUTION MEAN. (6,3,0)
Teaching Methods
THE COURSE COMPRISES 60 HOURS OF TEACHING, INCLUDING LECTURES AND EXERCISES (EQUIVALENT TO 6 ECTS CREDITS). SPECIFICALLY, THERE ARE 40 HOURS OF CLASSROOM LECTURES AND 20 HOURS OF CLASSROOM EXERCISES.
ATTENDANCE AT THE TEACHING SESSIONS IS HIGHLY RECOMMENDED.
Verification of learning
THE EXAM AIMS TO EVALUATE:
1)KNOWLEDGE AND UNDERSTANDING: THE CANDIDATE'S GRASP OF THE CONCEPTS PRESENTED IN THE COURSE.
2)APPLICATION OF KNOWLEDGE: THE ABILITY TO APPLY THESE CONCEPTS TO SOLVE PROBLEMS INVOLVING PROBABILITY ASSESSMENT, ESTIMATION OF UNKNOWN PARAMETERS, AND HYPOTHESIS TESTING CONCERNING NON-DETERMINISTIC PHENOMENA, AS WELL AS THE IDENTIFICATION OF SIMPLE EMPIRICAL MODELS FOR THE QUANTITATIVE ANALYSIS OF PHYSICAL AND/OR TECHNOLOGICAL PHENOMENA.
3)JUDGMENT AUTONOMY: THE CANDIDATE'S ABILITY TO EXERCISE JUDGMENT INDEPENDENTLY.
4)CLARITY OF EXPRESSION: THE CAPACITY TO EXPRESS PROBLEMS CLEARLY AND SUCCINCTLY.
5)LEARNING ABILITY: THE ABILITY TO LEARN.
THE EXAM CONSISTS OF A WRITTEN TEST DESIGNED TO ASSESS THE CANDIDATE'S SKILLS IN SETTING UP AND SOLVING TYPICAL PROBLEMS RELATED TO THE COURSE TOPICS, WITH PARTICULAR EMPHASIS ON:
1)DATA SAMPLE ANALYSIS: CALCULATION OF MAJOR STATISTICAL INDICATORS AND DATA REPRESENTATION.
2)SOLUTION OF PROBLEMATIC SITUATIONS RELATED TO PROBABILITY CONCEPTS AND RANDOM VARIABLE DEFINITIONS.
3)STATISTICAL INFERENCE: CALCULATION OF ESTIMATORS, CONFIDENCE INTERVALS, AND STATISTICAL DECISION-MAKING.
THE WRITTEN TEST IS GRADED ON A SCALE OF THIRTY, TAKING INTO ACCOUNT BOTH THE CORRECTNESS OF PROBLEM SETUP AND THE ACCURACY OF RESULTS. A GRADE OF "INSUFFICIENT" REQUIRES THE CANDIDATE TO RETAKE THE WRITTEN TEST.
AFTER THE WRITTEN TEST, THE CANDIDATE HAS THE OPTION TO UNDERGO AN ADDITIONAL ORAL INTERVIEW. THIS INTERVIEW PRIMARILY AIMS TO ASSESS THE CANDIDATE'S KNOWLEDGE OF THE COURSE MATERIAL, INCLUDING PARTS NOT DIRECTLY COVERED IN THE WRITTEN TEST. IT IS ALSO GRADED ON A SCALE OF THIRTY. THE FINAL OVERALL ASSESSMENT IS CALCULATED BY WEIGHTING THE WRITTEN TEST RESULT BY 60% AND THE ORAL INTERVIEW RESULT BY 40%. FAILURE TO PASS THE ORAL INTERVIEW REQUIRES THE CANDIDATE TO RETAKE THE WRITTEN TEST.
THE LEVEL OF SUFFICIENCY IS DEMONSTRATED BY THE CANDIDATE'S ABILITY TO IDENTIFY THE METHODOLOGICAL TOOLS TO USE, SET UP MODEL EQUATIONS CORRECTLY, AND SUGGEST FEASIBLE PROBLEM-SOLVING APPROACHES.
THE LEVEL OF EXCELLENCE IS ACHIEVED WHEN THE STUDENT DEMONSTRATES SUCCESSFUL HANDLING OF ASPECTS OF PROBLEMS NOT EXPLICITLY COVERED IN THE LECTURES.
THE ASSESSMENT DEPENDS ON THE LEVEL OF EXPOSITION AND THE DEGREE OF CONFIDENCE SHOWN WITH THE COURSE TOPICS AND THE METHODOLOGICAL TOOLS DESCRIBED IN THE COURSE.
Texts
LECTURE NOTES ON PROBABILITY AND COMBINATORIAL CALCULUS (IN ITALIAN).
S. M. ROSS, PROBABILITÀ E STATISTICA PER L’INGEGNERIA E LE SCIENZE, APOGEO.
M. DE IULIIS, ESERCIZI RISOLTI DI STATISTICA E CALCOLO DELLE PROBABILITA’, LIBRERIA UNIVERSITARIA (AVAILABLE STARTING FROM 2024)
COMPLEMENTARY BOOKS
G.E.P. BOX, W.G. HUNTER, J.S. HUNTER, STATISTICS FOR EXPERIMENTERS (AN INTRODUCTION TO DESIGN, DATA ANALYSIS AND MODEL BUILDING), WILEY.
N. DRAPER, H. SMITH, APPLIED REGRESSION ANALYSIS (SECOND EDITION), WILEY
More Information
THE COURSE IS TAUGHT IN ITALIAN
Lessons Timetable

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