Student Group Details

STATISTICA - Cognomi A-K

EA007

Course
STATISTICA - Cognomi A-K
Code
EA007
Academic Year
2026/2027
Curriculum Year
2026/2027
Degree Programme
BUSINESS AND MANAGEMENT
Curriculum
000 - CORSO GENERICO
Course coordinator
Aldo GOIA
Lecturers
Aldo GOIA
Credits
8
Lecture Hours
60
Scientific Disciplinary Sector (SSD)
STAT-01/A - Statistics
Course Type
Single-subject learning activity
Course Delivery
OBB - Obbligatoria
Year
1
Teaching period
Secondo Semestre
Campus
NOVARA
Teaching language
Italian
Course Contents
Presentation of statistical methodology for the analysis of one-dimensional and two-dimensional data. Introduction to probability theory and to sample statistics with special attention to inference for large samples.
Reference Texts
Lecture notes and exercises are available in the web page of the course at the URL: https://dir.uniupo.it
Learning Outcomes
The goal of the course is to introduce some basic techniques of univariate and bivariate data analysis and some elements of inferential statistics. The learning process focuses on:
1. definition of concepts by using examples and applications;
2. mastery of technical and formal language;
3. development of data analysis skills and of the ability to comment on and communicate the results.

Prerequisites
Basic elements of mathematics and calculus.
Teaching Methods
The course is delivered through lectures in which the following are presented and discussed: basic concepts, definitions, the steps leading to formalisation, and initial application exercises. The lectures are complemented by a series of classroom-based practical sessions aimed at applying the concepts introduced, selecting the most appropriate methods, and critically interpreting the results. Supplementary classroom activities (statistics workshops) and individual online activities to be carried out on the DIR platform are included. The lecture-based and interactive components are integrated into the course, accounting for approximately 3/4 and 1/4 of the total, respectively.
Additional Information
Some on-line supports are available on the web page of the course.

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.
Assessment Methods
The evaluation is based on a compulsory written exam and an oral exam.
The written exam includes:
- theoretical questions whose scope is to verify the knowledge level,
- exercises to verify the abilities in applying the introduced numerical tools,
- structured problems to test abilities in providing coherent comments on the results and communicating them with a proper statistical language.
The written examination allows students to achieve a maximum mark of 26/30.
The oral examination may be purely confirmatory (in which case it serves to verify the level achieved in the written examination) or supplementary/improvement-oriented (in which case it is designed to assess linguistic proficiency and the depth of understanding attained regarding theoretical and formal aspects).
Registration for the examination in accordance with the prescribed procedures and deadlines is compulsory.
Detailed Syllabus

Part I – Statistics for Populations The population and the variables. Frequency distributions and graphics. Analysis of quantitative variables: the cumulative distribution functions and the quantiles. Measures of the central tendency. Measures of absolute and relative variability. Measures of shape. Analysis of qualitative variables: the heterogeneity. The joint distributions, the marginal and the conditional distributions. The study of the dependence and the Chi square index. Total mean and variance theorems. Correlation and eta square index. Linear correlation and covariance. The regression model. The ordinary least square (OLS) method for the linear model. Analysis of residuals and goodness of fit. Part II – Elements of Probability and Inference The random experiments and the probability space. Probability measure on finite sample spaces. Conditional probability and independence. Bayes rule. The random variables and probability distributions. Moments. Some useful probability distributions: Uniform, Bernoulli, Binomial, Geometric, Poisson; Rectangular, Exponential). Normal r.v.: standard normal, linear transformations, computation of moments. The inferential paradigm. Parameters, statistics and estimators. Asymptotic statistics: consistency, the Central Limit Theorem and the plug-in principle. The point estimation and the asymptotic confidence intervals for mean, proportion, variance. Introducing hypothesis testing (for a mean and a proportion). P-value: calculation and interpretation.

Expected Learning Outcomes
Students will need to know and be able to use uni- and bi-variate statistical tools: descriptive statistics, measures of dependence (in mean), measure of linear correlation, regression model and linear regression model. Students will need to know and be able to use probability and inference tools: random experiments, notable distributions (bernoulli, binomial, uniform, negative exponential, Gaussian, etc.), point estimators (correct, efficient, consistent), confidence intervals, hypothesis tests. Students will be able to apply these tools to small examples of data and learn to interpret the results.
Fragmentary knowledge or knowledge containing substantial errors, an inability to set and solve exercises/problems correctly, and inadequate scientific language will result in a fail mark.
Essential and predominantly descriptive knowledge, with the application of concepts demonstrated through the solution of basic exercises; the resolution of simple problems, albeit with some uncertainties but presented in a comprehensible manner, will ensure a pass mark.
Complete mastery of the subject matter, combined with a high degree of independence in formulating and solving problems; effective analysis and synthesis with relevant connections and a rigorous presentation enable students to achieve excellent marks.
Last update:21-09-2026 00:13:16