Cira PERNA | Statistics for enterprise and innovation

cod. 0222200028

STATISTICS FOR ENTERPRISE AND INNOVATION

	0222200028
	DIPARTIMENTO DI SCIENZE ECONOMICHE E STATISTICHE
	EQF7
	ECONOMICS
	2018/2019

CURRICULUM ECONOMIA E MANAGEMENT DELL'INNOVAZIONE (EMI)

	YEAR OF COURSE 1
	YEAR OF DIDACTIC SYSTEM 2018
	PRIMO SEMESTRE

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
	SECS-S/01	10	60	LESSONS	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS

PROFESSORS

	FRANCESCO GIORDANO T Curriculum
	CIRA PERNA Curriculum

	Objectives
	KNOWLEDGE AND UNDERSTANDING THE PURPOSE OF THE COURSE IS TO PRESENT A UNIFIED AND CONCEPTUAL FRAMEWORK FOR LINEAR STATISTICAL MODELLING. MOREOVER, THE MOST IMPORTANT MULTIVARIATE TECHNIQUES ARE SHOWN. THERE IS AN EMPHASIS ON THEIR USE AS TOOL FOR ANALYSING THE RELATIONSHIPS AMONG ECONOMIC VARIABLES. APPLYING KNOWLEDGE AND UNDERSTANDING THE STUDENTS WILL BE ABLE TO APPLY THE PROPOSED STATISTICAL TOOLS IN A ECONOMIC CONTEXT. MAKING JUDGEMENTS THE STUDENTS WILL BE ABLE TO CRITICALLY SELECT THE MODEL AND THE PROCEDURES WHICH ARE MOST APPROPRIATE FOR THE KIND OF THE ANALYZED PROBLEM. COMMUNICATION SKILLS PARTICULAR EMPHASIS IS GIVEN TO THE PRESENTATION OF THE RESULTS OBTAINED FROM THE APPLICATION OF THE PROPOSED MODELS AND THE MULTIVARIATE TECHNIQUES. LEARNING SKILLS THE COURSE AIMS TO PROVIDE THE STUDENTS WITH THE ABILITY TO SUCCESSFULLY APPLIED THE DIFFERENT PROPOSED MODELS IN REAL CONTEXT AND APPLICATIONS. MOREOVER, THE STUDENT WILL BE ABLE TO ANALYZE MULTIVARIATE PROBLEMS.

KNOWLEDGE AND UNDERSTANDING

THE PURPOSE OF THE COURSE IS TO PRESENT A UNIFIED AND CONCEPTUAL FRAMEWORK FOR LINEAR STATISTICAL MODELLING. MOREOVER, THE MOST IMPORTANT MULTIVARIATE TECHNIQUES ARE SHOWN. THERE IS AN EMPHASIS ON THEIR USE AS TOOL FOR ANALYSING THE RELATIONSHIPS AMONG ECONOMIC VARIABLES.

APPLYING KNOWLEDGE AND UNDERSTANDING

THE STUDENTS WILL BE ABLE TO APPLY THE PROPOSED STATISTICAL TOOLS IN A ECONOMIC CONTEXT.

MAKING JUDGEMENTS

THE STUDENTS WILL BE ABLE TO CRITICALLY SELECT THE MODEL AND THE PROCEDURES WHICH ARE MOST APPROPRIATE FOR THE KIND OF THE ANALYZED PROBLEM.

COMMUNICATION SKILLS

PARTICULAR EMPHASIS IS GIVEN TO THE PRESENTATION OF THE RESULTS OBTAINED FROM THE APPLICATION OF THE PROPOSED MODELS AND THE MULTIVARIATE TECHNIQUES.

LEARNING SKILLS

THE COURSE AIMS TO PROVIDE THE STUDENTS WITH THE ABILITY TO SUCCESSFULLY APPLIED THE DIFFERENT PROPOSED MODELS IN REAL CONTEXT AND APPLICATIONS. MOREOVER, THE STUDENT WILL BE ABLE TO ANALYZE MULTIVARIATE PROBLEMS.

	Prerequisites
	BASIC ELEMENTS OF MATHEMATICS AND STATISTICS.

	Contents
	MODULE I (30 HOURS OF LESSONS: 22 HOURS OF THEORETICAL LESSONS AND 8 HOURS OF EXAMPLES AND APPLICATIONS IN COMPUTER ROOM BY R SOFTWARE). INTRODUCTION TO THE LINEAR REGRESSION MODEL. MULTIVARIATE LINEAR REGRESSION MODEL. ITS MATRIX REPRESENTATION. CLASSICAL ASSUMPTIONS. OLS ESTIMATORS AND THEIR PROPERTIES. TEST ON LINEAR COMBINATION OF PARAMETERS. F TEST. EXAMPLES IN R. MODULE II (30 HOURS OF LESSONS: 22 HOURS OF THEORETICAL LESSONS AND 8 HOURS OF EXAMPLES AND APPLICATIONS IN COMPUTER ROOM BY R SOFTWARE). INTRODUCTION TO THE MULTIVARIATE TECHNIQUES. VARIANCE-COVARIANCE AND CORRELATION MATRICES. PRINCIPAL COMPONENT ANALYSIS. DIMENSION REDUCTION. DISTANCE MATRIX. LINKAGES. CLUSTER ANALYSIS. HIERARCHICAL METHODS: AGGLOMERATIVE AND DIVISIVE. NON-HIERARCHICAL METHODS. EVALUATION OF THE RESULTS. EXAMPLES IN R.

Contents

MODULE I (30 HOURS OF LESSONS: 22 HOURS OF THEORETICAL LESSONS AND 8 HOURS OF EXAMPLES AND APPLICATIONS IN COMPUTER ROOM BY R SOFTWARE).

INTRODUCTION TO THE LINEAR REGRESSION MODEL. MULTIVARIATE LINEAR REGRESSION MODEL. ITS MATRIX REPRESENTATION. CLASSICAL ASSUMPTIONS. OLS ESTIMATORS AND THEIR PROPERTIES. TEST ON LINEAR COMBINATION OF PARAMETERS. F TEST. EXAMPLES IN R.

MODULE II (30 HOURS OF LESSONS: 22 HOURS OF THEORETICAL LESSONS AND 8 HOURS OF EXAMPLES AND APPLICATIONS IN COMPUTER ROOM BY R SOFTWARE).

INTRODUCTION TO THE MULTIVARIATE TECHNIQUES. VARIANCE-COVARIANCE AND CORRELATION MATRICES. PRINCIPAL COMPONENT ANALYSIS. DIMENSION REDUCTION. DISTANCE MATRIX. LINKAGES. CLUSTER ANALYSIS. HIERARCHICAL METHODS: AGGLOMERATIVE AND DIVISIVE. NON-HIERARCHICAL METHODS. EVALUATION OF THE RESULTS. EXAMPLES IN R.

	Teaching Methods
	LECTURES (60 HOURS). THEY WILL BE HELD IN CLASSROOM AND IN COMPUTING ROOM.

	Verification of learning
	THE FINAL EXAM IS CHARACTERIZED BY TWO PARTS: THE WRITTEN AND THE ORAL EXAMINATION. THE FIRST PART (1H) INCLUDES FOUR EXERCISES ON THE DIFFERENT SUBJECTS OF THE COURSE WHEREAS THE ORAL PART (ABOUT 20 MIN) IS BASED ON A SHORT DISCUSSION ABOUT THE WRITTEN PART AND AN INTERVIEW. EACH EXERCISE OF THE FIRST PART IS EVALUATED BY THE TEACHER WITH A MARK OF 1 (ONE) AT MAXIMUM. THE ORAL INTERVIEW CAN BE TAKEN IF THE STUDENT HAS PASSED THE WRITTEN EXAM WITH A MINIMUM MARK OF 2/4. THE FINAL MARK IS THE OVERALL MARK OBTAINED BY THE DISCUSSION OF THE FIRST PART AND THE INTERVIEW FOR THE SECOND PART OF THE EXAM.

Verification of learning

THE FINAL EXAM IS CHARACTERIZED BY TWO PARTS: THE WRITTEN AND THE ORAL EXAMINATION.
THE FIRST PART (1H) INCLUDES FOUR EXERCISES ON THE DIFFERENT SUBJECTS OF THE COURSE WHEREAS THE ORAL PART (ABOUT 20 MIN) IS BASED ON A SHORT DISCUSSION ABOUT THE WRITTEN PART AND AN INTERVIEW.
EACH EXERCISE OF THE FIRST PART IS EVALUATED BY THE TEACHER WITH A MARK OF 1 (ONE) AT MAXIMUM.
THE ORAL INTERVIEW CAN BE TAKEN IF THE STUDENT HAS PASSED THE WRITTEN EXAM WITH A MINIMUM MARK OF 2/4.
THE FINAL MARK IS THE OVERALL MARK OBTAINED BY THE DISCUSSION OF THE FIRST PART AND THE INTERVIEW FOR THE SECOND PART OF THE EXAM.

	Texts
	FOR MODULE I, HANDLOUTS WILL BE GIVEN ON THE TEACHER’S WEBSITE. FOR MODULE II, LUIGI FABBRIS, STATISTICA MULTIVARIATA, MCGRAW-HILL CHAPTERS 1, 2, 5 AND 8.

	More Information
	FURTHER MATERIAL WILL BE PUBLISHED ON THE WEBSITE.

BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2019-10-21]

Cira PERNA | Statistics for enterprise and innovation