Cira PERNA | Statistics for enterprise and innovation

cod. 0222200028

STATISTICS FOR ENTERPRISE AND INNOVATION

	0222200028
	DIPARTIMENTO DI SCIENZE ECONOMICHE E STATISTICHE

	ECONOMICS
	2015/2016

CURRICULUM ECONOMIA E MANAGEMENT DELL'INNOVAZIONE (EMI)

	YEAR OF COURSE 1
	YEAR OF DIDACTIC SYSTEM 2014
	PRIMO SEMESTRE

TEACHING UNITS

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

PROFESSORS

	CIRA PERNA T Curriculum
	FRANCESCO GIORDANO 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 ENPHASIS 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 ENPHASIS 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 ENPHASIS 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 ENPHASIS 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
	MATHEMATICS AND STATISTICS.

	Contents
	MODULE I 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 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

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

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, LABORATORY SESSIONS.

	Verification of learning
	WRITTEN TESTS AND ORAL EXAMINATION.

	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: 2016-09-30]

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