The course provides basic concepts of sampling and regression. Particular attention is devoted to the comparison of types of sampling, and to the comparison between bivariate and multivariate approaches. During the course students will be introduced to the use of statistical software for computers, solving problems in new areas, placed in interdisciplinary contexts. By autonomously managing complex knowledge, the student will learn how to make critical judgments and communicate the results obtained to specialists and non-specialist interlocutors.
By the study of STATISTICAL METHODS OF DATA ANALYSIS the student will be able to achieve the following training objectives.
Knowledge and understanding:
- have acquired in-depth disciplinary knowledge in the field of statistics such as to allow him an adequate approach to the problems of planning and carrying out socio-educational and socio-welfare activities.
- possess advanced methodological and technical knowledge, able to allow him to reflect on even complex situations with adequate data analysis and interpretation tools.
Applying knowledge and understanding:
- possession of skills in the use of the operating systems of the new data processing methods
- competent use of communication strategies with professional partners and users.
Making judgements:
- elaborate an independent judgment on the situations in which it is called to intervene, making decisions in complex situations, even in the face of partial data and information.
- show reflexive abilities on their own methods of intervention, supporting their decisions with objective information.
Communication skills:
- drafting documents aimed at programming and managing services, preparing research / monitoring / evaluation reports and preparing and presenting operational intervention proposals.
- know how to communicate in public and manage institutional communication.
Learning skills:
- appropriate acquisitions skills to allow any further post-graduate training courses (second level master's degree, research doctorate)
- ability to continue independently in the process of updating the knowledge necessary for the professional profile.
By the study of STATISTICAL METHODS OF DATA ANALYSIS the student will be able to achieve the following training objectives.
Knowledge and understanding:
- have acquired in-depth disciplinary knowledge in the field of statistics such as to allow him an adequate approach to the problems of planning and carrying out socio-educational and socio-welfare activities.
- possess advanced methodological and technical knowledge, able to allow him to reflect on even complex situations with adequate data analysis and interpretation tools.
Applying knowledge and understanding:
- possession of skills in the use of the operating systems of the new data processing methods
- competent use of communication strategies with professional partners and users.
Making judgements:
- elaborate an independent judgment on the situations in which it is called to intervene, making decisions in complex situations, even in the face of partial data and information.
- show reflexive abilities on their own methods of intervention, supporting their decisions with objective information.
Communication skills:
- drafting documents aimed at programming and managing services, preparing research / monitoring / evaluation reports and preparing and presenting operational intervention proposals.
- know how to communicate in public and manage institutional communication.
Learning skills:
- appropriate acquisitions skills to allow any further post-graduate training courses (second level master's degree, research doctorate)
- ability to continue independently in the process of updating the knowledge necessary for the professional profile.
Curriculum
teacher profile teaching materials
Chapters and sections to study on the text of Corbetta, Gasperoni and Pisati
Chapter 6: all (except piece-wise linear regression on page. 171) chapter 7: all (not function and use of ratios and not dxy section 5.) Chapter 8: all (except for section 2.3) chapter 10 all (in sections 3.1 e 3.2 excluded the formulas that do not regard the estimate of the population mean).
Programme
Arguments considered are: sampling and non sampling error, simple random sampling, cluster and stratified sampling, confidence interval, introduction to simple and multiple regression.Core Documentation
CORBETTA P., GASPERONI G., PISATI M., STATISTICA PER LA RICERCA SOCIALE, IL MULINO, BOLOGNA, 2001.Chapters and sections to study on the text of Corbetta, Gasperoni and Pisati
Chapter 6: all (except piece-wise linear regression on page. 171) chapter 7: all (not function and use of ratios and not dxy section 5.) Chapter 8: all (except for section 2.3) chapter 10 all (in sections 3.1 e 3.2 excluded the formulas that do not regard the estimate of the population mean).
Type of delivery of the course
CLASSType of evaluation
Single final written examination, lasting one hour, composed of 15 questions (closed and open form), concenrning the whole program. teacher profile teaching materials
Chapters and sections to study on the text of Corbetta, Gasperoni and Pisati
Chapter 6: all (except piece-wise linear regression on page. 171) chapter 7: all (not function and use of ratios and not dxy section 5.) Chapter 8: all (except for section 2.3) chapter 10 all (in sections 3.1 e 3.2 excluded the formulas that do not regard the estimate of the population mean).
Programme
Arguments considered are: sampling and non sampling error, simple random sampling, cluster and stratified sampling, confidence interval, introduction to simple and multiple regression.Core Documentation
CORBETTA P., GASPERONI G., PISATI M., STATISTICA PER LA RICERCA SOCIALE, IL MULINO, BOLOGNA, 2001.Chapters and sections to study on the text of Corbetta, Gasperoni and Pisati
Chapter 6: all (except piece-wise linear regression on page. 171) chapter 7: all (not function and use of ratios and not dxy section 5.) Chapter 8: all (except for section 2.3) chapter 10 all (in sections 3.1 e 3.2 excluded the formulas that do not regard the estimate of the population mean).
Type of delivery of the course
CLASSType of evaluation
Single final written examination, lasting one hour, composed of 15 questions (closed and open form), concenrning the whole program.