Unit PROBABILITY AND STATISTICS FOR INVESTIGATION
- Course
- Socioanthropological studies for integration and social security
- Study-unit Code
- GP004351
- Curriculum
- Gestione dei rischi sociali
- Teacher
- Fabio Pasticci
- Teachers
-
- Fabio Pasticci
- Hours
- 36 ore - Fabio Pasticci
- CFU
- 6
- Course Regulation
- Coorte 2020
- Offered
- 2020/21
- Learning activities
- Caratterizzante
- Area
- Discipline matematico-statistiche ed economiche
- Academic discipline
- MAT/06
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- The course is divided into three parts, each of approximately 6 lectures (12 hours) for a total of 18 lectures (36 hours): Descriptive Statistics, Probability, Inferential Statistics.
The first part presents the principal tools of the Descriptive Statistics that are used to provide a concise and expressive representation of a collection of data.
The second part provides some basic Probability Calculation concepts that are a prerequisite for understanding the third part.
The third part presents some basic Inferential Statistics techniques that allow you to draw conclusions on a population of interest from a sample of individuals. - Reference texts
- A. Agresti, B. Finlay: Statistica per le scienze sociali, Pearson, 2009. (ENGLISH VERSION: Statistical methods for the social science 4/ed, Pearson Prentice Hall, 2009).
I. Diamond, J. Jefferies: Introduzione alla statistica per le scienze sociali, 2/ed. McGraw-Hill, 2006. (ENGLISH VERSION: Beginning Statistics: An Introduction for Social Scientists, SAGE Publications, 2001).
E. Ivaldi: Elementi introduttivi alla statistica e al calcolo delle probabilità, Impressioni Grafiche, 2019.
Lecture notes/slides provided by the tenured professor.
(Substitute material in English will be provided if needed). - Educational objectives
- At the end of the course the student will be able to understand and use the most common descriptive statistics tools such as tables, graphs, frequencies, averages and dispersion indices.
She/he will understand the meaning of probabilistic evaluations, use probability to sketch some simple random phenomena and draw simple probabilistic inferences (Bayesian).
The student will be able to draw conclusions about a population of interest (in the form of confidence intervals and significance tests) using a sample of its individuals. She/he will also be able to quantify the correlation between two variables and make predictions on a variable by knowing another. - Prerequisites
- Basic mathematical skills.
- Teaching methods
- Face-to-face lectures and interactive videoconference.
Lectures material available on the UniStudium e-learning platform: https://www.unistudium.unipg.it/unistudium/. - Other information
- Office hours: by appointment (email fabio.pasticci@unipg.it )
- Learning verification modality
- Written exam with questions (exercises and/or theoretical questions) related to topics covered in class, with possible oral discussion (optional) in case the student was not satisfied with the vote.
- Extended program
- The course is divided into three parts, each of approximately 6 lectures (12 hours) for a total of 18 lectures (36 hours): Descriptive Statistics, Probability, Inferential Statistics.
The first part presents the principal tools of Descriptive Statistics that are used to provide a concise and expressive representation of a collection of data:
Data, statistical variables (numeric and categorical), frequency distributions, tabular representations;
Cumulative frequency distributions, the most common graphic representations (bar graphs, pie charts, histograms, line diagrams);
Average values (arithmetic and weighing average, mode and median), quartiles;
Dispersion indices (inter-quartile range, standard deviation and variance), box-plots.
The second part provides some basic Probability concepts that are a prerequisite for understanding the third part:
Events, probability of an event as measure of the degree of confidence and its possible estimate as a frequency;
Conditional probability, stochastic independence, Bayes theorem;
Random variables, normal distribution.
The third part presents some basic techniques of Inferential Statistics that allow you to draw conclusions about a population of interest from a sample of individuals:
Distinction between population and sample, random sample;
Confidence intervals, significance tests;
Correlation and regression (regression line).