Quantitative methods
A.A. 2018/2019
Obiettivi formativi
Non definiti
Risultati apprendimento attesi
Non definiti
Periodo: Secondo trimestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo trimestre
STUDENTI FREQUENTANTI
STUDENTI NON FREQUENTANTI
Informazioni sul programma
Collection, preparation and quality control of data
Review of Iinference and Bivariate Analysis
- Sampling, probability and Inference
- Correlation and regression
- Association
- Conforted medium
Multiple linear regression
Models for categorical response variables
Factor analysis and principal components analysis
Correspondence analysis
Cluster analysis
Case study
SPSS software will be used
Review of Iinference and Bivariate Analysis
- Sampling, probability and Inference
- Correlation and regression
- Association
- Conforted medium
Multiple linear regression
Models for categorical response variables
Factor analysis and principal components analysis
Correspondence analysis
Cluster analysis
Case study
SPSS software will be used
Propedeuticità
The concepts imparted during a basic 9-year Bachelor of Statistics course are considered acquired.
In particular:
- classification of characters and graphic representations
- position and variability indices
- elements of probability calculation
- random variables (see Bernoulli, Binomiale and Normal)
- sample distributions (see sample media, see sample proportion, see sample variance)
- estimation theory
In particular:
- classification of characters and graphic representations
- position and variability indices
- elements of probability calculation
- random variables (see Bernoulli, Binomiale and Normal)
- sample distributions (see sample media, see sample proportion, see sample variance)
- estimation theory
Prerequisiti
The exam will consist of a written test, aimed at verifying the acquired knowledge of the topics covered during the course and, on the other, to demonstrate, in the laboratory, to be able to analyze the data available and identify, implementing them, the methods suitable to solve the proposed questions. In both tests a sufficient score will have to be achieved. On the first module there will be multiple-choice questions and some practical exercises, on the second module a series of analyzes to be performed with SPSS and commented.
Metodi didattici
Each topic will be be presented during the classical lectures and will be applied during the lab lectures.
Real data sets will be considered and case studies presented.
Real data sets will be considered and case studies presented.
Module 1
Programma
Collection, preparation and quality control of data
Review of Iinference and Bivariate Analysis
- Sampling, probability and Inference
- Correlation and regression
- Association
- Conforted medium
SPSS software will be used
Review of Iinference and Bivariate Analysis
- Sampling, probability and Inference
- Correlation and regression
- Association
- Conforted medium
SPSS software will be used
Metodi didattici
Each topic will be be presented during the classical lectures and will be applied during the lab lectures.
Real data sets will be considered and case studies presented.
Real data sets will be considered and case studies presented.
Materiale di riferimento
"Statistics for Marketing and Consumer Research", by M. Mazzocchi, Sage Publications, 2013 - ISBN 978-1-4129-1121-4
Collection, preparation and control of data (Chapter 1, 2, 3, 4)
Sampling, probability and Inference (chapter 5, 6, 7)
Correlation and regression (chapter 8)
Association (chapter 9)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Collection, preparation and control of data (Chapter 1, 2, 3, 4)
Sampling, probability and Inference (chapter 5, 6, 7)
Correlation and regression (chapter 8)
Association (chapter 9)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Module 2
Programma
Multiple linear regression
Models for categorical response variables
Factor analysis and principal components analysis
Correspondence analysis
Cluster analysis
Case study
SPSS software will be used
Models for categorical response variables
Factor analysis and principal components analysis
Correspondence analysis
Cluster analysis
Case study
SPSS software will be used
Metodi didattici
Each topic will be be presented during the classical lectures and will be applied during the lab lectures.
REal data sets will be considered and case studies presented.
REal data sets will be considered and case studies presented.
Materiale di riferimento
"Statistics for Marketing and Consumer Research", by M. Mazzocchi, Sage Publications, 2013 - ISBN 978-1-4129-1121-4
Regression (chapter 8)
Discriminant analysis (chapter 11)
Logistic regression (Chapter 16)
Factor analysis and analysis of the main components (chapter 10)
Correspondence analysis (chapter 14)
Cluster analysis (chapter 12)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Regression (chapter 8)
Discriminant analysis (chapter 11)
Logistic regression (Chapter 16)
Factor analysis and analysis of the main components (chapter 10)
Correspondence analysis (chapter 14)
Cluster analysis (chapter 12)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Prerequisiti
The exam will consist of a written test, aimed at verifying the acquired knowledge of the topics covered during the course and, on the other, to demonstrate, in the laboratory, to be able to analyze the data available and identify, implementing them, the methods suitable to solve the proposed questions. In both tests a sufficient score will have to be achieved. On the first module there will be multiple-choice questions and some practical exercises, on the second module a series of analyzes to be performed with SPSS and commented.
Module 1
Programma
Collection, preparation and quality control of data
Review of Iinference and Bivariate Analysis
- Sampling, probability and Inference
- Correlation and regression
- Association
- Conforted medium
SPSS software will be used
Review of Iinference and Bivariate Analysis
- Sampling, probability and Inference
- Correlation and regression
- Association
- Conforted medium
SPSS software will be used
Materiale di riferimento
"Statistics for Marketing and Consumer Research", by M. Mazzocchi, Sage Publications, 2013 - ISBN 978-1-4129-1121-4
Collection, preparation and control of data (Chapter 1, 2, 3, 4)
Sampling, probability and Inference (chapter 5, 6, 7)
Correlation and regression (chapter 8)
Association (chapter 9)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Collection, preparation and control of data (Chapter 1, 2, 3, 4)
Sampling, probability and Inference (chapter 5, 6, 7)
Correlation and regression (chapter 8)
Association (chapter 9)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Module 2
Programma
Multiple linear regression
Models for categorical response variables
Factor analysis and principal components analysis
Correspondence analysis
Cluster analysis
Case study
SPSS software will be used
Models for categorical response variables
Factor analysis and principal components analysis
Correspondence analysis
Cluster analysis
Case study
SPSS software will be used
Materiale di riferimento
"Statistics for Marketing and Consumer Research", by M. Mazzocchi, Sage Publications, 2013 - ISBN 978-1-4129-1121-4
Regression (chapter 8)
Discriminant analysis (chapter 11)
Logistic regression (Chapter 16)
Factor analysis and analysis of the main components (chapter 10)
Correspondence analysis (chapter 14)
Cluster analysis (chapter 12)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Regression (chapter 8)
Discriminant analysis (chapter 11)
Logistic regression (Chapter 16)
Factor analysis and analysis of the main components (chapter 10)
Correspondence analysis (chapter 14)
Cluster analysis (chapter 12)
Slides, Excercises, Lab tutorial with SPSS will be available on the web page
Moduli o unità didattiche
Module 1
SECS-S/01 - STATISTICA - CFU: 6
Lezioni: 40 ore
Docente:
Salini Silvia
Module 2
SECS-S/03 - STATISTICA ECONOMICA - CFU: 6
Lezioni: 40 ore
Docente:
Leorato Samantha
Docente/i
Ricevimento:
Orario prossimi ricevimenti: giovedì 17.04 solo online (su appuntamento); giovedì 24.04 ore 9:30-12:30 online (su appuntamento); martedì 29.04 ore 10:30-12, mercoledì 30.04 ore 14-15:30
Stanza 32 terzo piano
Ricevimento:
Il ricevimento studenti è il martedì dalle 10.00 alle 13.00 o in presenza o via Teams (meglio fissare un appuntamento) - Il ricevimento di martedì prossimo, per altri impegni accademici, non sarà svolto. Contattare il docente per un altro appuntamento.
DEMM, stanza 30, 3° p oppure su Teams