Pierluigi FURCOLO | STATISTICS APPLIED TO ENGINEERING
Pierluigi FURCOLO STATISTICS APPLIED TO ENGINEERING
cod. 0612100040
STATISTICS APPLIED TO ENGINEERING
| 0612100040 | |
| DEPARTMENT OF CIVIL ENGINEERING | |
| EQF6 | |
| BSC DEGREE IN CIVIL ENGINEERING | |
| 2022/2023 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| SECS-S/02 | 6 | 60 | LESSONS |
| Objectives | |
|---|---|
| EXPECTED LEARNING RESULTS AND COMPETENCE TO BE ACQUIRED: KNOWLEDGE OF: TOOLS AND METHODS TO DESCRIBE, EVALUATE AND INTERPRET VARIABILITY IN THE EXPERIMENTAL, ENVIRONMENTAL AND INDUSTRIAL FIELDS IN ORDER TO MAKE DECISIONS UNDER A CONTROLLED RISK, WITH APPLICATIONS TO THE DESIGN, MANAGEMENT OF SERVICES AND LAND USE; METHODS AND TOOLS TO PLAN DATA COLLECTION IN ORDER TO ALLOW OBJECTIVE ANALYSIS OF THE PROBLEM TREATED; METHODS AND TOOLS TO ANALYZE THE EFFECT OF DIFFERENT FACTORS ON A PHENOMENON OF INTEREST AND MAKE QUANTITATIVE COMPARISONS BETWEEN THEM; METHODS AND TOOLS FOR CONSTRUCTING AND TESTING EXPERIMENTAL INTERPRETATIVE MODELS OF A PHYSICAL OR TECHNOLOGICAL PHENOMENON. KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING OF THE DESCRIPTION OF NONDETERMINISTIC PHENOMENA BASED ON THE THEORY OF PROBABILITY. UNDERSTANDING OF THE DESCRIPTION OF THE VARIABILITY OF A PHENOMENON BY MEANS OF RANDOM VARIABLES, THEIR TRANSFORMATIONS AND THEIR PROBABILITY MODELS. UNDERSTANDING OF THE BASIC ELEMENTS OF INDUCTIVE REASONING AND THE BASIC ELEMENTS OF DESCRIPTIVE STATISTICS AND INFERENTIAL STATISTICS. UNDERSTANDING OF THE ANALYSIS AND DESCRIPTION OF A PHENOMENON USING LINEAR REGRESSION MODELS. UNDERSTANDING OF THE BASIC ELEMENTS FOR RELIABILITY ANALYSIS AND RISK ANALYSIS AND ELEMENTS OF THE THEORY OF EXTREME VALUES. ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: KNOWING HOW TO ANALYZE NONDETERMINISTIC PHENOMENA. KNOWING HOW TO ESTIMATE UNKNOWN QUANTITIES OF A PHENOMENON ON A STATISTICAL BASIS. KNOWING HOW TO TEST HYPOTHESES ON A STATISTICAL BASIS. KNOWING HOW TO SET UP SIMPLE DESIGN PROBLEMS ON A PROBABILISTIC BASIS. KNOWING HOW TO CARRY OUT RELIABILITY ASSESSMENTS OF SYSTEMS AND STRUCTURES. ABILITY TO ANALYZE EXTREME PHENOMENA AND ESTIMATE THEIR RETURN PERIODS. AUTONOMY OF JUDGMENT: KNOWING HOW TO IDENTIFY THE MOST APPROPRIATE METHODS TO ANALYZE A NON-DETERMINISTIC PHENOMENON. KNOWING HOW TO CHOOSE THE MOST APPROPRIATE STATISTICAL PROCEDURE TO ESTIMATE UNKNOWN QUANTITIES AND / OR VERIFY ALTERNATIVE HYPOTHESES BETWEEN THEM. KNOWING HOW TO CRITICALLY ANALYZE THE RESULTS PROVIDED BY STATISTICAL PROCESSING SOFTWARE. COMMUNICATION SKILLS: KNOWING HOW TO PRESENT BOTH ORALLY AND IN WRITING A TOPIC RELATED TO THE PROBABILISTIC ASSESSMENT OF A RANDOM PHENOMENON. KNOWING HOW TO EXPOSE THE SUBJECTS OF STATISTICAL ANALYSIS OF DATA CORRECTLY AND COMPREHENSIVELY. ABILITY TO LEARN: KNOWING HOW TO APPLY THE KNOWLEDGE ACQUIRED TO CONTEXTS DIFFERENT FROM THOSE PRESENTED DURING THE COURSE. KNOWING HOW TO USE DIFFERENT SOURCES TO DEEPEN THE METHODOLOGIES INTRODUCED IN THE COURSE. |
| Prerequisites | |
|---|---|
| NONE |
| Contents | |
|---|---|
| DESCRIPTIVE STATISTICS: POPULATION, SAMPLES, INDICES, GRAPHS AND TABLES. PROBABILITY THEORY: DEFINITION OF PROBABILITY, AXIOMS OF THE MATHEMATICAL THEORY, CONDITIONAL PROBABILITY, BAYES' THEOREM. RANDOM VARIABLES: DEFINITION OF RANDOM VARIABLE AND RANDOM VECTOR, DISCRETE AND CONTINUOUS VARIABLES, DISTIRBUTION FUNCTIONS, INDICES, TRANSFORMS OF RANDOM VARIABLES. EXAMPLES OF COMMON MODELS OF RANDOM VARIABLE. INFERENTIAL STATISTICS: DATA SAMPLE, RANDOM MODEL OF THE SAMPLE, STATISTICAL FUNCTIONS, SAMPLE VARIABLES, METHOD OF MOMENTS, MAXIMUM LIKELYHOOD, BAYESIAN ESTIMATION. STATISTICAL TESTS: TESTING AN HYPOTHESIS, STUDENT TEST, CHI-SQUARE TEST, FISHER TEST. LINEAR REGRESSION: DEFINIZION, LEAST SQUARES METHOD, STATISTICAL PROPERTIES OF THE ESTIMATORS, TESTING SIGNIFICANCE. |
| Teaching Methods | |
|---|---|
| LESSONS (40H) AND CLASS EXERCISES (20H). ATTENDANCE IS MANDATORY AND IS VERIFIED BY ELECTRONIC BADGE. IN ORDER TO ACCESS THE FINAL EXAM, AT LEAST 60% OF THE HOURS NEED TO BE ATTENDED |
| Verification of learning | |
|---|---|
| WRITTEN EXAM: 4-6 EXERCISES ON TOPICS PRESENTED IN THE CLASSES, WITH DIFFERENT DIFFICULTIES, WHICH ARE DECLARED FOR EAXG EXERCISE. THE EVALUATION IS BASED ON THE VALUES DECLARED FOR EACH EXERCISE AND THE CORRECTNESS OF THE SOLUTION GIVEN BY THE STUDENT. ORAL EXAM: THE ORAL EXAM CAN BE DONE ONLY IF THE GRADE OS THE WRITTEN TEST IS ABOVE 15/30. IT CONSISTS IN A SHORT INTERVIEW ABOUT FLAWS ENLIGHTED BY THE WRITTEN TEST AND ABOUT OTHE TOPICS INCLUDED IN THE PROGRAMME. THE FINAL SCORE STARTS FROM THAT OF THE WRITTEN TEST AND FURTHER CONSIDERS THE PREPARATION, EXPOSITION AND MATURITY SHOWN BY THE STUDENT AT THE ORAL EXAM. |
| Texts | |
|---|---|
| SHELDON M. ROSS, "INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS", ELSEVIER, 624 PP. |
| More Information | |
|---|---|
| NO OTHER INFORMATION |
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