85166 - Statistical Software

Academic Year 2021/2022

  • Moduli: Lorenzo Mancini (Modulo 1) Paola Scrigner (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)

    Also valid for Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)

Learning outcomes

By the end of the course, the student acquires the operational tools for the analysis of case studies and he is able to prepare reports by using SAS and to present them with the use of Microsoft Power Point.

Course contents

  • Overview of SAS
  • Datasets, Libname, Syntax
  • Datasets import and structure
  • Proc Contents/Proc Print
  • Data types
  • Selection of observations, Operations on variables
  • Proc Sort, first., last.
  • Format, Informat, Proc Format
  • Proc Freq, Proc Univariate, Proc Means
  • Merge, Transpose
  • Introduction to stat procedures in SAS

Readings/Bibliography

Handouts provided by the teacher.

Suggested reading:

  • Lora D. Delwiche, Susan J. Slaughter, The Little SAS Book: A Primer, Fifth Edition (English Edition) 5th Edition, 2012.

Teaching methods

  • Computer laboratory sessions using SAS.

Assessment methods

Students are requested to create work groups made of 3-4 individuals max. Each group will choose among a list of projects provided by the end of the course. Each project will consists on a statistical analysis to be performed using SAS. A powerpoint document with analysis will be provided to the teacher before the exam.

The document must contain:

  • analysis objectives;
  • description of the used dataset;
  • used SAS code; and
  • interpretation of the results.

The oral exam will consists of the powerpoint presentation of the analysis. The evaluation will be made on an individual basis (i.e. not group basis). In order to evaluate each student individually, extra questions on SAS programming can be asked during the session.

Non-attending students can perform the task by choosing a dataset among those provided (RMK: write an e-mail to agree on the project before the exam).

Teaching tools

  • Lab tutorials
  • Slides

Office hours

See the website of Lorenzo Mancini

See the website of Paola Scrigner