Ciro D'APICE | STATISTICA APPLICATA E SISTEMI DI SUPPORTO ALLE DECISIONI
Ciro D'APICE STATISTICA APPLICATA E SISTEMI DI SUPPORTO ALLE DECISIONI
cod. 0622600032
STATISTICA APPLICATA E SISTEMI DI SUPPORTO ALLE DECISIONI
| 0622600032 | |
| DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | |
| EQF7 | |
| MANAGEMENT ENGINEERING | |
| 2016/2017 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2016 | |
| SECONDO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | ||
|---|---|---|---|---|---|
| STATISTICA APPLICATA E SISTEMI DI SUPPORTO ALLE DECISIONI | |||||
| SECS-S/02 | 6 | 60 | LESSONS | ||
| STATISTICA APPLICATA E SISTEMI DI SUPPORTO ALLE DECISIONI | |||||
| MAT/09 | 6 | 60 | LESSONS | ||
| Objectives | |
|---|---|
| KNOWLEDGE AND UNDERSTANDING THE TEACHING AIMS AT THE ACQUISITION OF THE BASIC ELEMENTS OF DECISION SUPPORT SYSTEMS AND SIMULATION TECHNIQUES: DATA MINING, TECHNIQUES AND CONTROL OF PROJECTS, MODELS FOR LOGISTICS AND PRODUCTION, SIMULATION OF COMPLEX SYSTEMS. IN ADDITION, THE TEACHING AIMS TO PROVIDE INSTRUMENTS AND BASIC METHODS FOR DESCRIBING, EVALUATING AND INTERPRETING THE VARIABILITY IN EXPERIMENTAL, INDUSTRIAL AND ENVIRONMENTAL FIELD, WITH THE PURPOSE OF ASSUMING DECISIONS, WITH APPLICATIONS TO THE PRODUCTION PROCESSES, THE MANAGEMENT OF SERVICES AND ENVIRONMENTAL ISSUES; PROVIDE BASIC METHODS AND TOOLS FOR PLANNING FOR DATA COLLECTION IN ORDER TO ALLOW OBJECTIVE ANALYSIS OF THE CONCERNED ISSUE; PROVIDE BASIC METHODS AND TOOLS TO ANALYZE THE EFFECT OF DIFFERENT FACTORS ON A PHENOMENON OF INTEREST AND PERFORM QUANTITATIVE COMPARISONS BETWEEN THEM; PROVIDE BASIC METHODS AND TOOLS TO BUILD AND TO SUBJECT TO EXPERIMENTAL VERIFICATION MODELS OF INTERPRETATION OF A PHYSICAL OR TECHNOLOGICAL PHENOMENON. THE AIM OF THE TEACHING IS THE ACQUISITION OF THE RESULTS AND OF DEMONSTRATION TECHNIQUES, AS WELL AS THE ABILITY TO USE THE CALCULUS INSTRUMENTS. THE THEORETICAL PART OF THE TEACHING WILL BE PRESENTED IN A RIGOROUS BUT CONCISE WAY AND I TWILL BE ACCOMPANIED BY EXERCISE ACTIVITIES DESIGNED TO PROMOTE UNDERSTANDING OF CONCEPTS. APPLYING KNOWLEDGE AND UNDERSTANDING KNOWING HOW TO APPLY THEOREMS AND RULES DESIGNED TO SOLVE PROBLEMS. KNOWING HOW TO CONSISTENTLY BUILD SOME DEMONSTRATIONS. KNOWING HOW TO BUILD METHODS AND PROCEDURES FOR THE RESOLUTION OF PROBLEMS. ABILITY TO ANALYZE SIMPLE NON-DETERMINISTIC PHENOMENA. ABILITY TO PERFORM SIMPLE ESTIMATES OF UNKNOWN QUANTITIES OF A PHENOMENON ON A STATISTICAL BASIS. ABILITY TO PERFORM SIMPLE HYPOTHESIS TESTS ON A STATISTICAL BASIS. ABILITY TO SET UP SIMPLE DETECTION PROBLEMS OF THE MOST SIGNIFICANT FACTORS INFLUENCING A TECHNOLOGICAL PHENOMENON AND TO FORMULATE SIMPLE MATHEMATICAL MODELS FOR ITS QUANTITATIVE DESCRIPTION. KNOWING HOW TO IDENTIFY THE MOST APPROPRIATE METHODS TO EFFICIENTLY SOLVE A PROBLEM ABOUT DECISION CRITERIA AND SIMULATION TECHNIQUES. TO BE ABLE TO FIND SOME OPTIMIZATIONS IN THE PROCESS OF SOLVING PROBLEM USING APPROPRIATE DECISION CRITERIA AND SIMULATION TECHNIQUES. KNOWING HOW TO IDENTIFY THE MOST APPROPRIATE METHODS TO ANALYZE A NON-DETERMINISTIC PHENOMENON. KNOWING HOW TO CHOOSE THE MOST APPROPRIATE STATISTICAL PROCEDURE FOR ESTIMATING UNKNOWNS QUANTITY AND / OR TEST ALTERNATIVE HYPOTHESES. BEING ABLE TO CRITICALLY ANALYZE THE RESULTS PROVIDED BY STATISTICAL PROCESSING SOFTWARE. SKILL OF APPLYING THE ACQUIRED KNOWLEDGE TO DIFFERENT CONTEXTS FROM THOSE PRESENTED DURING THE TEACHING. SKILL TO DEEPEN THE TOPICS DEALT WITH BY USING MATERIALS DIFFERENT FROM THOSE PROPOSED. |
| Prerequisites | |
|---|---|
| PREREQUISITES FOR THE SUCCESSFUL ACHIEVEMENT OF THE GOALS, STUDENT MUST HAVE BASIC KNOWLEDGE OF OPERATIONS RESEARCH, PROBABILITY THEORY, ALGEBRA OF RANDOM VARIABLES AND STATISTICAL INFERENCE. PREPARATORY TEACHINGS NONE |
| Contents | |
|---|---|
| MODULE DECISION SUPPORT SYSTEMS TOOLS FOR DECISION SUPPORT (DSS): INTRODUCTION TO DSS. BUSINESS INTELLIGENCE (DEFINITION. CONSTRUCTION OF A DSS. DATA WAREHOUSE, OLAP, DATA MINING). ARTIFICIAL NEURAL NETWORKS. FUZZY LOGIC. KNOWLEDGE BASED SYSTEM. (HOURS LECTURE/PRACTICE/LABORATORY 10/6/-) ALGORITHM ON GRAPHS: THE PROBLEM OF MAXIMUM FLOW (DEFINITIONS, CUTTING, CUTTING CAPACITY, NET FLOW, AUGMENTING PATHS, OPTIMALITY CONDITIONS, ALGORITHMS OF FORD-FULKERSON AND FOR THE CALCULATION OF AUGMENTING PATHS). COMBINATORIAL OPTIMIZATION (HEURISTICS, GROUND SETS AND SUBSETS SYSTEM; MAXIMUM COUPLING PROBLEM ON BIPARTITE GRAPHS; TRAVELING SALESMAN PROBLEM; PARTITIONING PROBLEM OF A GRAPH; HEURISTICS OF GREEDY TYPE AND APPLICATIONS, CLUSTERING; LOCAL SEARCH ALGORITHM). METAHEURISTICS (TABU SEARCH ALGORITHM, SIMULATED ANNEALING ALGORITHM).(10/5/-) QUEUING THEORY: RANDOM VARIABLES. DENSITY AND DISTRIBUTION FUNCTION OF PROBABILITY. EXPONENTIAL AND POISSON DISTRIBUTIONS. GENERATING FUNCTION. STOCHASTIC PROCESSES. MARKOV CHAINS. BIRTH-DEATH PROCESSES. DISCRETE MARKOV CHAINS. FUNDAMENTAL THEOREMS OF MARKOV CHAINS. ERGODICITY. ASYMPTOTIC PROBABILITY. CONTINUOUS MARKOV CHAINS. QUEUING SYSTEMS. M/M/1, M/M/N/R, PERFORMANCE INDEXES. QUEUE NETWORKS.(13/6/-) MULTI OBJECTIVE OPTIMIZATION: MULTI OBJECTIVE PROGRAMMING. PARETO OPTIMALITY. OPTIMALITY CONDITIONS. SOLUTION METHODS. GOAL PROGRAMMING. -CONSTRAINED ALGORITHM.(7/3/-) MODULE APPLIED STATISTICS RECALLS AND COMPLEMENTS OF THEORY OF PROBABILITY AND ALGEBRA OF RANDOM VARIABLES. FUNCTIONS OF A R.V. COUPLES OF RANDOM VARIABLES. JOINT AND MARGINAL DISTRIBUTIONS. SYNTHETIC INDICATORS FOR COUPLES OF R.V .: MIXED MOMENT, COVARIANCE, CORRELATION. MODELS OF DISCRETE AND CONTINUOUS R.V..(3/2/-) DEFINITIONS AND BASIS CONCEPTS. THE MODERN CONCEPT OF “QUALITY” OF A PRODUCT: THE CORRESPONDENCE TO THE USE IN THE PERSPECTIVE OF THE USER. THE QUALITY CONTROL IN THE PLAN PHASE. MEANING OF “RELIABILITY” AND ITS OPERATIONAL DEFINITION. RELIABILITY AND UNRELIABILITY FUNCTION. RELIABILITY AND UNRELIABILITY OF UNITS USED. DENSITY FUNCTION OF FAILURE PROBAVILITY. AVERAGE LIFE. REMAINING AVERAGE LIFE. FUNCTION “FAILURE RATE” AND “ACCUMULATED FAILURE RATE”. “BATHTUB” CURVE AND ITS TECHNOLOGICAL SIGNIFICANCE. EXPONENTIAL RELIABILITY MODEL. WEIBULL RELIABILITY MODEL. (7/3/-) RELIABILITY ANALYSIS OF MULTI-COMPONENT SYSTEMS . LOGIC DIAGRAM OF A SYSTEM. STRUCTURES OF " SERIES " AND " PARALLEL" TYPE . PHYSICAL AND LOGICAL STRUCTURE OF A SYSTEM . STRUCTURES OF SERIES- PARALLEL TYPE . PARALLEL PARTIAL TYPE STRUCTURES . SYSTEMS WITH PENDING REDUNDANCY. METHOD OF CONDITIONAL PROBABILITY. FAULT TREES. (7/3/-) RELIABILITY ANALYSIS OF REPAIRABLE UNITS. PUNCTUAL STOCHASTIC PROCESSES. COUNTING PROCESSES. TIME BETWEEN FAILURES AND DOWNTIMES . EXPECTED NUMBER OF FAULTS . RATE OF OCCURRENCE OF FAILURES . INTENSITY OF A PUNCTUAL PROCESS. HOMOGENEOUS POISSON PROCESS: CHARACTERISTICS AND RANGE OF APPLICATIONS . PROCESS OF RENEWAL : CHARACTERISTICS AND RANGE OF APPLICATIONS . NOT HOMOGENEOUS POISSON PROCESS : CHARACTERISTICS AND RANGE OF APPLICATIONS . POWER LAW PROCESSES. MARKOV CHAINS AND PROCESSES . AVAILABILITY FUNCTION. AVAILABILITY TO FULL SPEED AND AVERAGE AVAILABILITY. AVAILABILITY OF A SYSTEM . (10/5/-) STATISTICAL METHODS FOR RELIABILITY EVALUATION . TYPES OF RELIABILITY DATA . METHODS OF NON- PARAMETRIC ESTIMATION : GRAPHIC POSITIONS . ANALYTICAL METHODS FOR PARAMETER ESTIMATION : MAXIMUM LIKELIHOOD METHOD , LINEAR ESTIMATION METHODS . METHODS OF PARAMETER ESTIMATION GRAPHICS : CARDS OF PROBABILITY . EVALUATION METHODS FOR REPAIRABLE UNITS : TREND ANALYSIS METHODS OF GRAPHICAL AND ANALYTICAL TYPE. (10/5/-) TOTAL HOURS 80/40/- |
| Teaching Methods | |
|---|---|
| THE TEACHING COVERS THEORETICAL LESSONS, DURING WHICH ALL THE CONTENTS WILL BE PRESENTED BY LECTURES, AND CLASSROOM EXERCISES DURING WHICH THE MAIN TOOLS NECESSARY FOR THE RESOLUTION OF EXERCISES RELATED TO TEACHING CONTENT WILL BE PROVIDE. THERE WILL BE ALSO LECTURES WITH COMPUTER FOR INTRODUCING THE USE OF SOFTWARE TOOLS FOR SIMPLE STATISTICAL ANALYSIS. |
| Verification of learning | |
|---|---|
| THE FINAL EXAM IS DESIGNED TO EVALUATE AS A WHOLE: -THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED DURING THE TEACHING -THE ABILITY TO APPLY THIS KNOWLEDGE TO THE RESOLUTION OF PROBLEMS INVOLVING THE EVALUATION OF THE PROBABILITY OF EVENTS, THE ESTIMATION OF UNKNOWN PARAMETERS AND VERIFICATION OF HYPOTHESES REGARDING NON-DETERMINISTIC PHENOMENA, THE IDENTIFICATION OF SIMPLE EMPIRICAL MODELS FOR THE QUANTITATIVE ANALYSIS OF PHYSICAL AND/OR TECHNOLOGICAL PHENOMENA. -THE MASTERY OF THE MATHEMATICAL LANGUAGE IN THE WRITTEN AND ORAL PROOFS -THE SKILL OF PROVING THEOREMS -THE SKILL OF SOLVING EXERCISES -THE SKILL TO IDENTIFY AND APPLY THE BEST AND EFFICIENT METHOD IN EXERCISES SOLVING -THE ABILITY TO APPLY THE ACQUIRED KNOWLEDGE TO DIFFERENT CONTEXTS FROM THOSE PRESENTED DURING THE TEACHING. THERE WILL BE ALSO EVALUATED: THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO HIGHLIGHT THE PROBLEMS IN A CLEAR AND COMPREHENSIVE FORM AND THE ABILITY TO LEARN. THE EXAM CONSISTS OF A WRITTEN PROOF AND AN ORAL INTERVIEW (FOR EACH MODULE). MODULE “DECISION SUPPORT SYSTEMS” WRITTEN PROOF: THE WRITTEN PROOF CONSISTS IN SOLVING TYPICAL PROBLEMS PRESENTED IN THE LECTURES. IN THE CASE OF A SUFFICIENT PROOF, IT WILL BE EVALUATED BY THREE SCALES. ORAL INTERVIEW: THE INTERVIEW IS DEVOTED TO EVALUATE THE DEGREE OF KNOWLEDGE OF ALL THE TOPICS OF THE TEACHING, AND COVERS DEFINITIONS, THEOREMS PROOFS, EXERCISES SOLVING. FINAL EVALUATION: THE FINAL MARK, EXPRESSED IN THIRTIETHS, DEPENDS ON THE MARK OF THE WRITTEN PROOF, WITH CORRECTIONS IN EXCESS OR DEFECT ON THE BASIS OF THE ORAL INTERVIEW. MODULE “APPLIED STATISTICS” WRITTEN PROOF: THE WRITTEN PROOF IS AIMED AT TESTING THE SKILLS OF THE STUDENT IN SETTING AND IN SOLVING TYPICAL PROBLEMS REGARDING THE TOPICS PRESENTED IN THE TEACHING, WITH PARTICULAR REFERENCE TO: 1) AN ASSESSMENT OF THE PROBABILITY OF EVENTS; 2) INFERENCE AND DECISION ON A STATISTICAL BASIS; 3) ANALYSIS OF VARIANCE AND LINEAR REGRESSION ANALYSIS. THE WRITTEN TEST ARE GIVEN SCORES OF THIRTY, WHICH TAKES INTO ACCOUNT BOTH THE CORRECT SETTING OF THE PROBLEM AND THE ACCURACY OF THE RESULTS. THE " INSUFFICIENT" ASSESSMENT IMPLIES THE NEED TO REPEAT THE WRITTEN TEST. ORAL INTERVIEW: THE STUDENT CAN ASK TO PERFORM, AFTER THE PASSING OF THE WRITTEN TEST, AN INTEGRATIVE INTERVIEW. THIS INTERVIEW WILL BE MAINLY AIMED TO VERIFY THE KNOWLEDGE OF THE SUBJECT MATTER OF THE TEACHING ALSO ON PARTIES NOT DIRECTLY INVOLVED IN THE WRITTEN TEST, AND IT WILL BE GIVEN AN ASSESSMENT OF THIRTY. THE OVERALL FINAL EVALUATION WILL BE OBTAINED BY WEIGHING THE RESULTS OF THE WRITTEN TEST FOR THE 60 % AND THE RESULTS OF THE ORAL EXAM FOR THE 40%. FAILURE TO PASS THE ORAL EXAMINATION REQUIRES THE REPETITION OF THE WRITTEN TEST. LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE A FULL GRASP OF BOTH THEORETICAL AND APPLIED ASPECTS OF THE TEACHING TOPICS. OVERALL ASSESSMENT: THE FINAL SCORE WILL TAKE INTO CONSIDERATION THE EVALUATION OF THE SINGLE MODULES. |
| Texts | |
|---|---|
| LECTURE NOTES. EDUCATIONAL CONTENTS ON E-LEARNING PLATFORM IWT. S. M. ROSS, PROBABILITÀ E STATISTICA PER L’INGEGNERIA E LE SCIENZE, APOGEO. OTHER 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 |
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| TEACHING IN ITALIAN. |
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