37321 - Statistics for Data Analysis

Academic Year 2019/2020

  • Docente: Paola Bortot
  • Credits: 6
  • SSD: SECS-S/01
  • Language: Italian
  • Teaching Mode: Traditional lectures
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Economics and Accounting (cod. 0900)

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, Websidte 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. For the applications, students are asked to bring their own portable computer to the classroom on the days that will be specified. Working in pairs is allowed, although working individually is recommended. Students should have installed the packages R and RStudio on their computers. (See Teaching tools for details on the installation.)

Assessment methods

Students can choose between two methods of assessment:

1) by a written examination that will focus on all the topics covered during the course (including questions on R) whose result will determine the final mark;

2) by solving a home assignment on a topic that will be selected by the teacher whose result will count for the 30% of the final mark and by a written examination  whose result will count for the remaining 70% of the final mark. The home assignment solutions should be handed in at the time of the written examination.

 

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 material (including lectures notes) will be available on Piazza at the web site piazza.com/unibo.it/fall2019/37321/home

 

Office hours

See the website of Paola Bortot