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

The contents of the course will include:

1) Review of Statistical Inference tools

Brief review of the main concepts of point estimation, hypothesis testing and confidence intervals.

2) Review of the simple linear regression model

Motivation and definition of the simple linear mode. Parameter estimation and hypothesis testing. Goodness-of-fit of the model.

3) Multiple linear regression model

Motivation and definition of multiple linear regression. Parameter estimation and hypothesis testing. Goodness-of-fit of the model.

4) Logistic regression model

Motivation and definition of logistic regression model. Parameter estimation and hypothesis testing. Application of the logistic regression to classification problems.

5) Applications through the software R

Each topic will be completed with the analysis of case studies through the statistical software R.

Readings/Bibliography

• Textbook used for the Statistics course of the Bachelor degree and possible other textbooks used in subsequent courses of Statistics (e.g. Business Statistics).
  
  
• Lectures notes that will be made available online at the start of the course
  
  
• For R:
  1. E-Book: "R Programming", tutorialspoint, Website https://www.tutorialspoint.com/r/index.htm
  2. E-Book: "Get started in R: a complete beginners workbook", R Statistics.Net. Website: http://rstatistics.net/r-tutorial-exercise-for-beginners/

Teaching methods

Traditional lectures in the classroom of in the virtual room (according to the current regulations related to the pandemic situations). For the applications, students are asked to bring their own portable computer to the classroom on the days that will be specified. Students should have installed the packages R and RStudio on their computers. (See Teaching tools for details on the installation.)

Assessment methods

EXAMS

Students can choose between two assessment methods:

1)

- 100% by a written exam, which will deal with the use of software R, and theoretical or interpretation questions on the statistical methods seen during lectures, also starting from an R output;

2)

- 75% by a written exam, which will deal with the use of software R, and theoretical or interpretation questions on the statistical methods seen during lectures, also starting from an R output;

- 25% by a project report to be produced by pairs of students, 5-page long, containing a data analysis on a free topic. The report must be delivered by the end of the class.

The points obtained by the project report will be valid by September 2022 and will sum up to the grade of the written exam, if sufficient.

NOTA BENE: Exam modes could change due to the pandemic situation. Anyway, those who will desire, if allowed, to take the online test, will be able to enter a virtual Zoom room and take the same exam of the students in the classroom.

GRADE REJECT

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

GRADE MEANING

• <18 insufficient
• 18-23 sufficient
• 24-27 good
• 28-30 very good
• 30 e lode excellent

Teaching tools

• The software R will be used for the application of the taught tools. R can be freely downloaded from the web site http://www.r-project.org/.
• The package RStudio will be used as an interface for R. RStudio can be freely downloaded from https://www.rstudio.com/products/rstudio/download/ (Make sure that the free version suitable for the operating system used is downloaded.)
• The teaching tutor Valentino Moscariello  (valentin.moscariello@unibo.it) is available for questions and deepenings.

Office hours

See the website of Matteo Farnè