Teaching code

4S00522

Academic staff

Riccardo D'Alberto, Elena Dalla Chiara

Coordinator

Riccardo D'Alberto

Credits

9

Language

Italian

Scientific Disciplinary Sector (SSD)

SECS-S/03 - ECONOMIC STATISTICS

Period

Primo semestre LM dal Sep 30, 2024 al Dec 23, 2024.

Courses Single

Not Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

During the course, the main sources of official data will be studied and the main sampling techniques analyzed. The linear regression model will be introduced as one of the main statistical tools to examine survey data and for sales forecasting. Students will be provided with the cutting-edge statistical theory of sampling and linear regression model. These tools will then be applied to carry out market researches.

Prerequisites and basic notions

The following knowledge related to basic descriptive statistics and statistical inference is assumed to be acquired prior to the course by the students:
• Frequency distributions (univariate, bivariate, multivariate, frequency distributions in classes).
• Measures of central tendency (arithmetic mean, geometric mean, weighted mean, mean for frequency distributions in classes, median, mode, quantiles, and quartiles).
• Measures of variability (range, interquartile range, variance).
• Normal distribution.
• Point estimation (definition and properties of estimators, point estimation of the mean, proportion, variance).
• Interval estimation (mean, proportion, variance).
• Hypothesis testing (theory of tests, tests on the mean, tests on the variance, p-value).

Program

1. Distributions and random variables in the business and marketing contexts.
2. Sources of data and statistical information
• Primary data
• Secondary data
• Open data
3. Principles of statistical sampling for business processes and market surveys
• Sample and population parameters
• Probabilistic and non-probabilistic sampling
• Statistics and sampling distributions
• Sampling from finite and infinite populations
• Simple Random Sampling
• Stratified Sampling
4. Preparation and management of business statistical data and strategic information
• Data preparation, data management, data cleaning
• Data Quality Assessment
• Management and use of metadata
5. Representation and visualization of information
• Data visualization
• Graphical data analysis
• Graphical representations (histograms, area charts, pie charts, box plots, scatter plots)
6. Cluster Analysis and PCA
7. Summary indicators and comparison of business statistical data
• Statistical ratios
• Simple index numbers
8. Regression analysis
• Correlation and association between variables
• Simple linear regression model
• Multiple linear regression model
• Logistic model
9. Market Basket Analysis
• Associative rules and related metrics
• Algorithms
• Application in R
For detailed references to the teaching texts and their relation to the program topics, students are invited to carefully review the document "BUSINESS STATISTICS PROGRAM A.Y. 2024/2025" available on the course's Moodle platform.

Bibliography

Didactic methods

Classroom lectures are conducted with the support of the teaching materials provided (slides, exercises, OneNote notes, etc.) and examples and exercises (also carried out with Excel and R).

Learning assessment procedures

The assessment of learning is conducted through a written exam structured as follows:
 15 short-answer, multiple-choice, or calculated questions
 3 open-ended questions
There may be up to three short-answer, multiple-choice, or calculated questions (of the 15 questions provided) and up to one open-ended question (of the 3 questions provided) related to the syllabus of module 2 (3 CFU).
The questions can concern theoretical or methodological aspects, require the solution of exercises, or ask to discuss, comment on, and analyze applied problems based on the knowledge acquired during the course.
With the exception of supplementary materials, all topics presented in the lectures by the instructor and the sections of the textbooks indicated in the bibliography related to the course syllabus are an integral part of the learning assessment.
If there are inconsistencies in the answers provided in the written exam or if it is not possible to formulate a coherent evaluation, the instructor reserves the right to summon individual candidates for a supplementary oral exam (this is only provided in the aforementioned circumstance).
The supplementary oral exam may cover any topic from the course syllabus and may result in a final evaluation equal to, higher than, or lower than that achieved in the written exam, potentially altering the outcome even in relation to the pass/fail assessment.
The written exam is compulsory in person, on the days and times scheduled in the course's exam calendar. Only students who have registered for the exam through the dedicated virtual platform are allowed to take the written test.
The structure of the written exam will be very similar to the mock exams made available on the course's Moodle platform (see “mock_exam”).
For further information and details on the structure of the written exam, the access and conduct procedures, and the methods of acceptance/refusal of the grade and the recording of the results, students are invited to carefully review the document "BUSINESS STATISTICS EXAM INSTRUCTIONS A.Y. 2024/2025" available on the course's Moodle platform.

Evaluation criteria

The written exam (as well as any supplementary oral exam) aims to verify the knowledge of the course topics, the mastery of technical language, the candidate's clarity of presentation, the ability to independently apply the statistical methods learned during the course, the ability to approach the statistical analysis of business and consumer phenomena, and the use of the main statistical techniques in marketing, providing a correct interpretation of the results obtained.

Criteria for the composition of the final grade

The grade is expressed in a scale up to 30 Lode.

Exam language

Italiano