Statistics for enterprise and innovation

ECONOMICS Statistics for enterprise and innovation

0222200028
DEPARTMENT OF ECONOMICS AND STATISTICS
EQF7
ECONOMICS
2022/2023

OBBLIGATORIO
YEAR OF COURSE 1
YEAR OF DIDACTIC SYSTEM 2018
AUTUMN SEMESTER
CFUHOURSACTIVITY
1060LESSONS
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.


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.

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.
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.
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