Guida degli insegnamenti
Syllabus
Partially translatedTradotto parzialmente
Mariateresa CIOMMI
Lingua di erogazione: INGLESELessons taught in: ENGLISH
Laurea Magistrale - [EM07] INTERNATIONAL ECONOMICS AND COMMERCE (Curriculum: INTERNATIONAL ECONOMICS AND BUSINESS) Master Degree (2 years) - [EM07] INTERNATIONAL ECONOMICS AND COMMERCE (Curriculum: INTERNATIONAL ECONOMICS AND BUSINESS)
Dipartimento: [040002] Dipartimento Scienze Economiche e SocialiDepartment: [040002] Dipartimento Scienze Economiche e Sociali
Anno di corsoDegree programme year : 1 - Secondo Semestre
Anno offertaAcademic year: 2019-2020
Anno regolamentoAnno regolamento: 2019-2020
Opzionale
Crediti: 6
Ore di lezioneTeaching hours: 44
TipologiaType: B - Caratterizzante
Settore disciplinareAcademic discipline: SECS-S/01 - STATISTICA
INGLESE
English
Univariate and bivariate descriptive statistics. Most relevant inferential concepts (samples, statistics, estimators).
Univariate and bivariate descriptive statistics. Most relevant inferential concepts (samples, statistics, estimators).
The course will be taught through theoretical lessons and hands-on classes, during which the students analyse and synthesize a number of datasets focused on economic and business research. Computer-based analyses will be also performed in the PC-lab using the open-source software GRETL and R.
The course will be taught through theoretical lessons and hands-on classes, during which the students analyse and synthesize a number of datasets focused on economic and business research. Computer-based analyses will be also performed in the PC-lab using the open-source software GRETL and R.
1. Knowledge and Understanding: Students will acquire a good understanding of the statistical tools covered in the course as well as the ability to analyse economic and business datasets using appropriate statistical techniques.
Capacity to apply Knowledge and Understanding.
2. Applying knowledge and understanding: the student must be able to study and to understand how to use statistical software for analysing datasets and preparing reports.
Transversal Skills.
3. Judgement, focus and communication skills: The discussions as well as the practical applications that will take place during the course will enable students to enhance their autonomy and their analytical and communicative skills
Knowledge and Understanding.
1. Knowledge and Understanding: Students will acquire a good understanding of the statistical tools covered in the course as well as the ability to analyse economic and business datasets using appropriate statistical techniques.
Capacity to apply Knowledge and Understanding.
2. Applying knowledge and understanding: the student must be able to study and to understand how to use statistical software for analysing datasets and preparing reports.
Transversal Skills.
3. Judgement, focus and communication skills: The discussions as well as the practical applications that will take place during the course will enable students to enhance their autonomy and their analytical and communicative skills
PROGRAM
The program will be focused on:
- Inferential statistics: point estimators, confidence intervals, hypothesis testing, p-value
- Multivariate linear regression
- Factor analysis
- Cluster analysis
PROGRAM
The program will be focused on:
- Inferential statistics: point estimators, confidence intervals, hypothesis testing, p-value
- Multivariate linear regression
- Factor analysis
- Cluster analysis
Examination: Written exam concerning the methodological issues discussed during the course and a computer-based practical assignment (not mandatory) based on the analysis of a real data set.
Class participation: Weekly computer-based exercises will be also performed in the PC-lab. Weekly home-works will be assigned.
Learning Evaluation Criteria.
1. Assessment criteria
Students will be evaluated in their knowledge and understanding of the most relevant statistical tools for business analysis as well as in their ability to apply them to empirical problems and settings.
Learning Measurement Criteria.
2. Grading scale
Positive grades: from 18 to 30. Cum laude can be bestowed to outstanding performance.
Final Mark Allocation Criteria.
3. Grading method
The final grade will be obtained throught a written exam with practical and theoretical questions.
Learning Evaluation Methods.
Examination: Written exam concerning the methodological issues discussed during the course and a computer-based practical assignment (not mandatory) based on the analysis of a real data set.
Class participation: Weekly computer-based exercises will be also performed in the PC-lab. Weekly home-works will be assigned.
Learning Evaluation Criteria.
1. Assessment criteria
Students will be evaluated in their knowledge and understanding of the most relevant statistical tools for business analysis as well as in their ability to apply them to empirical problems and settings.
Learning Measurement Criteria.
2. Grading scale
Positive grades: from 18 to 30. Cum laude can be bestowed to outstanding performance.
Final Mark Allocation Criteria.
3. Grading method
The final grade will be obtained throught a written exam with practical and theoretical questions.
Mandatory textbook:
- P. Newbold, W. Carlson, B. Thorne “Statistics for Business and Economics”, Prentice Hall (Chapters: 7-8-9)
- R.A. JOHNSON, D.W. WICHERN “Applied multivariate statistical analysis” Pearson International Edition (Chapters: 1, 2, 4, 7, 9, 11, 12)
- Additional material will be available in the e-learning web page
Mandatory textbook:
- P. Newbold, W. Carlson, B. Thorne “Statistics for Business and Economics”, Prentice Hall (Chapters: 7-8-9)
- R.A. JOHNSON, D.W. WICHERN “Applied multivariate statistical analysis” Pearson International Edition (Chapters: 1, 2, 4, 7, 9, 11, 12)
- Additional material will be available in the e-learning web page
Le informazioni contenute nella presente scheda assumono carattere definitivo solo a partire dall'A.A. di effettiva erogazione dell'insegnamento. Academic year 2019-2020
Università Politecnica delle Marche
P.zza Roma 22, 60121 Ancona
Tel (+39) 071.220.1, Fax (+39) 071.220.2324
P.I. 00382520427