QUANTITATIVE METHODS FOR BUSINESS DATA ANALYSIS-MOD1 | Università degli studi di Bergamo - Didattica e Rubrica

QUANTITATIVE METHODS FOR BUSINESS DATA ANALYSIS-MOD1

Modulo Generico
Codice dell'attività formativa: 
87173-MOD1

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2020/2021
Insegnamento (nome in italiano): 
QUANTITATIVE METHODS FOR BUSINESS DATA ANALYSIS-MOD1
Insegnamento (nome in inglese): 
QUANTITATIVE METHODS FOR BUSINESS DATA ANALYSIS-MOD1
Tipo di attività formativa: 
Attività formativa Affine/Integrativa
Tipo di insegnamento: 
Opzionale
Settore disciplinare: 
STATISTICA ECONOMICA (SECS-S/03)
Anno di corso: 
3
Anno accademico di offerta: 
2022/2023
Crediti: 
3
Responsabile della didattica: 

Altre informazioni sull'insegnamento

Ciclo: 
Primo Semestre
Obbligo di frequenza: 
No
Ore di attività frontale: 
24
Ore di studio individuale: 
51
Ambito: 
Attività formative affini o integrative
Prerequisiti

No prerequisites, but a base knowledge of statistics is preferable

Obiettivi formativi

The course introduces the main statistical, mathematical, and computational tools used for the analysis of business and financial data. We start from an introduction about the data collection process and with the evaluation of data quality. Then, we focus on the main analytical tools for dealing with cross-sectional data (inference, one-way ANOVA, multiple regression analysis). In the following part, we introduce the most common models to analyse time series data, with a particular focus on financial markets (ARMA, ARCH and GARCH models).
Throughout the whole course, we will emphasize the practical implementation of the introduced techniques by means of some of the most common software (SAS and R). In our laboratories we will work on real datasets highlighting how to interpret the obtained output.

In particular, in the first module we introduce the main principles of survey methodology (data collection methods, sample selection, questionnaire design). We will also highlight how to detect and to fix the main potential issues of collected data (data editing). A brief introduction of the main statistical tests will follow. In the last part of the course we provide an overview of two statistical techniques commonly used in cross-sectional data processing: the one-way ANOVA and the multiple regression estimation.

The student will learn how to implement and run a data collection process (e.g., a survey project) as well as to treat issues linked to the quality of collected data. The student will also gain ability in applying the main statistical tests (e.g. the test for one or two groups) and in interpreting a statistical test output. Finally, the student will be able to set, run and interpret, for practical purposes, one-way ANOVA and multiple regression analysis outputs.

Contenuti dell'insegnamento

• Data collection: survey methods, sample selection, questionnaire design.
• Data quality: how to detect and treat data issues.
• Base inference: main statistical distributions and base of hypothesis testing, mean and proportion testing, testing group differences.
• One-way ANOVA for comparing group means and for experimental studies.
• Multiple regression analysis: setting and running the analysis, interpreting the output; introduction of logistic regression.

Metodi didattici

The course consists of traditional lectures and laboratory sessions where the introduced techniques are practically implemented and interpreted using a software.

Modalità verifica profitto e valutazione

The exam is made by two parts: one for each module. Each exam awards up to 30 points.
The students have to reach a positive (i.e., at least 18) score for both modules. The final full course evaluation is computed as simple average of the two scores.
For both modules, the exam is written and includes an evaluation about the ability acquired by the student in working with the introduced software. There are no differences for attending and for non-attending students.

Altre informazioni

The module 87173-MOD 1 (3 CFU) is part of the 6 CFU course.

"In case of provisions of the competent authority on containment and management of epidemiological emergency, the teaching may be subject to changes from what is declared in the syllabus in order to make the course and examinations in line with the regulations."

Prerequisites

No prerequisites, but a base knowledge of statistics is preferable

Educational goals

The course introduces the main statistical, mathematical, and computational tools used for the analysis of business and financial data. We start from an introduction about the data collection process and with the evaluation of data quality. Then, we focus on the main analytical tools for dealing with cross-sectional data (inference, one-way ANOVA, multiple regression analysis). In the following part, we introduce the most common models to analyse time series data, with a particular focus on financial markets (ARMA, ARCH and GARCH models).
Throughout the whole course, we will emphasize the practical implementation of the introduced techniques by means of some of the most common software (SAS and R). In our laboratories we will work on real datasets highlighting how to interpret the obtained output.

In particular, in the first module we introduce the main principles of survey methodology (data collection methods, sample selection, questionnaire design). We will also highlight how to detect and to fix the main potential issues of collected data (data editing). A brief introduction of the main statistical tests will follow. In the last part of the course we provide an overview of two statistical techniques commonly used in cross-sectional data processing: the one-way ANOVA and the multiple regression estimation.

The student will learn how to implement and run a data collection process (e.g., a survey project) as well as to treat issues linked to the quality of collected data. The student will also gain ability in applying the main statistical tests (e.g. the test for one or two groups) and in interpreting a statistical test output. Finally, the student will be able to set, run and interpret, for practical purposes, one-way ANOVA and multiple regression analysis outputs.

Course content

• Data collection: survey methods, sample selection, questionnaire design.
• Data quality: how to detect and treat data issues.
• Base inference: main statistical distributions and base of hypothesis testing, mean and proportion testing, testing group differences.
• One-way ANOVA for comparing group means and for experimental studies.
• Multiple regression analysis: setting and running the analysis, interpreting the output; introduction of logistic regression.

Teaching methods

The course consists of traditional lectures and laboratory sessions where the introduced techniques are practically implemented and interpreted using a software.

Assessment and Evaluation

The exam is made by two parts: one for each module. Each exam awards up to 30 points.
The students have to reach a positive (i.e., at least 18) score for both modules. The final full course evaluation is computed as simple average of the two scores.
For both modules, the exam is written and includes an evaluation about the ability acquired by the student in working with the introduced software. There are no differences for attending and for non-attending students.

Further information

The module 87173-MOD 1 (3 CFU) is part of the 6 CFU course.

"In case of provisions of the competent authority on containment and management of epidemiological emergency, the teaching may be subject to changes from what is declared in the syllabus in order to make the course and examinations in line with the regulations."