37321 - Statistics for Data Analysis

Learning outcomes

At the end of the course the student will have acquired knowledge of the main tools used in auditing for statistical sampling and basic concepts of prediction and classification. The student 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.

Arithmetics, mathematics and logic in R. 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 of two population.

Linear (simple and multiple) regression.

Logistic regression.

Readings/Bibliography

The following books are freely available on the internet.

Wickham, Hadley, and Grolemund, Garrett. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Stati Uniti, O'Reilly Media, 2016. https://r4ds.had.co.nz

Måns Thulin, Modern statistics with R, 2021. http://modernstatisticswithr.com/

 

The following book is available in bookshops:

Alan Agresti, Maria Kateri, Foundations of Statistics for Data Scientists with R and Python, Taylor & Francis, 2021

https://www.taylorfrancis.com/books/mono/10.1201/9781003159834/foundations-statistics-data-scientists-alan-agresti-maria-kateri

Teaching methods

Class lectures.

Each student will need to bring his/her own laptop after installing R and RStudio in this order:

install R from https://www.cran.r-project.org/

install RStudio from https://www.rstudio.org/download/desktop

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, in e-learning mode.

Assessment methods

The exam will be written and will be a practical test of data analysis in a computer laboratory. The structure of the exam will be similar to the one of the examples given in Virtuale.

Grade reject

The grade can be rejected by the student only once. To reject the grade, the student must send an email to piergiovanni.bissiri@unibo.it by the specified date.

Grading policy

insufficient <18; sufficient 18-23; good 24-27; very good 28-30; excellent 30 cum laude.

Teaching tools

• material provided by the lecturer on virtuale.unibo.it
• statistical software R https://www.r-project.org
• integrated development environment RStudio https://www.rstudio.com

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 Pier Giovanni Bissiri