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37321 - Statistics for Data Analysis

Academic Year 2024/2025

                
                        Docente:
                        Anna Vesely
                    
                        Credits:
                        6
                    
                        SSD:
                        SECS-S/01
                    
                        Language:
                        Italian
                    
                        Teaching Mode:
                        Traditional lectures
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Economics, Consultancy and Accounting (cod. 5981)

                                    Course Timetable
                                
from Nov 14, 2024 to Dec 17, 2024

Learning outcomes

At the end of the course students will have acquired knowledge of the main tools used in auditing for statistical sampling and basic concepts of prediction and classification. They will be able to study the dependence of a selected variable from a set of explanatory variables through a multiple regression model; to tackle problems of classification both through discriminant analysis and logistic regression.

Course contents

Introduction to R and RStudio.
Data structures in R. Creation and management of variables and dataframes. Data importing.
Descriptive analysis of data and graphical representations.
Statistical inference for the mean of a gaussian population and for a proportion. Comparison of means and proportion of two population.
Simple and multiple linear regression model. Residual analysis.

Readings/Bibliography

Materials (slides and Python scripts) will be provided by the lecturer.

Follow-up materials:

Hadley Wickham, Garrett Grolemund, R for Data Science, 2017.
Måns Thulin, Modern statistics with R, 2021.
Alan Agresti, Maria Kateri, Foundations of statistics for data scientists with R and Python, Taylor & Francis, 2021.

The first two resources are freely available online.

Teaching methods

Class lectures. Students should bring their own laptop.

In view of the type of activities and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in Modules 1 and 2 of safety training in the workplace https://elearning-sicurezza.unibo.it/, in e-learning mode.

Assessment methods

The exam will be written and will be a practical test of data analysis in a computer laboratory.

Teaching tools

Material provided by the lecturer will be available on Virtuale https://virtuale.unibo.it/.

Students with disability or specific learning disabilities (DSA) are required to make their condition known to find the best possibile accomodation to their needs.

Office hours

See the website of Anna Vesely