Statistical Software Lab

Course objectives

Learning goals. The main goal of this lab course is the acquisition of the logic and the fundamentals of one statistical package for the analysis of real data. Moreover, at the end of the course students should be able to formalize simple questions on real practical problems using standard statistical tools. The focus is also on the theoretical framework together with the computational details related to real applications, giving special attention to the interpretation of the results from the statistical software output. Knowledge and understanding. When completing the course, students will have learnt the logic and basics of programming to import and manipulate data, perform standard statistical analyses on real data. Moreover, they will have learnt the basics of empirical checking of statistical laws and inferential theoretical properties by simulation. Applying knowledge and understanding. When completing the course, students will be able to formalize a selected statistical problem, make standard statistical analyses in autonomy, interpret and explain the results. Moreover, they will be able to carry on simple simulations. Making judgements. Students develop the critical thinking by applying standard methodologies learnt in their curricula which they are able to use in autonomy by means of a statistical software. The skill to process data and produce the output by themselves helps to learn how to interpret the results taking into considerations theoretical criteria. Communication skills. By processing data and interpreting the results, students will learn the correct use of the technical language which is required in both coursework and final exam. Special attention is given to the skill of communicating results to non-specialists by using a rigorous but understandable language. Learning skills. Students passing the exam have learnt how to perform standard statistical analyses in autonomy and the logic to study and apply different methodologies in other applicative cases which are the premises to continue their studies in a Second Cycle Degree in either Statistics or Applied Statistics.

Channel 1
ROBERTA VARRIALE Lecturers' profile

Program - Frequency - Exams

Course program
Topics: Reading data, data structures; univariate descriptive statistics and univariate graphical representations; variable transformations; bivariate descriptive statistics and bivariate graphical representations; programming - functions, if and for, simulations on bias and mean square error; testing; regression - model building; cluster analysis; principal component analysis.
Prerequisites
In order to understand the topics covered and to pass the examination, it is essential to have a basic knowledge of descriptive statistics (in particular: all concepts and analytical tools for the statistical analysis of univariate and bivariate data), statistical inference (in particular: sample distributions, point estimate theory, confidence intervals and hypothesis testing) and multivariate statistics (principal component analysis; cluster analysis). In the programme to which this course belongs, these concepts are acquired through the examinations of the curricular subjects Basic Statistics, Statistical Inference and Multivariate Statistics.
Books
Documentation provided by the lecturer Software documentation also available online A Tiny Handbook of R. Mike Allerhand. Springer Introductory Statistics with R. Peter Dalgaard. Springer
Frequency
Course attendance is strongly recommended.
Exam mode
Students have to pass a final practical test in the computer room, in which they have to a) Process and analyse real data, select the necessary output to answer the required questions and comment on it; b) empirically verify deduced theoretical laws or properties by simulation. These tests make it possible to assess competence in the use of the software; they also make it possible to test the ability to formalise a practical problem, the ability to carry out basic statistical analyses by independently processing data and interpreting the results, and to demonstrate learning of the basic concepts of statistics acquired in the curriculum courses.
Lesson mode
Lessons in the computer laboratory alternate between lectures and independent work by the student, who is asked to apply the methods and procedures independently to situations similar to those presented. Analyses of real case studies help to link the statistical theory, which is the student's background, with real analysis situations in order to arrive at a critical interpretation of the results obtained from the processing.
ROBERTA VARRIALE Lecturers' profile

Program - Frequency - Exams

Course program
Topics: Reading data, data structures; univariate descriptive statistics and univariate graphical representations; variable transformations; bivariate descriptive statistics and bivariate graphical representations; programming - functions, if and for, simulations on bias and mean square error; testing; regression - model building; cluster analysis; principal component analysis.
Prerequisites
In order to understand the topics covered and to pass the examination, it is essential to have a basic knowledge of descriptive statistics (in particular: all concepts and analytical tools for the statistical analysis of univariate and bivariate data), statistical inference (in particular: sample distributions, point estimate theory, confidence intervals and hypothesis testing) and multivariate statistics (principal component analysis; cluster analysis). In the programme to which this course belongs, these concepts are acquired through the examinations of the curricular subjects Basic Statistics, Statistical Inference and Multivariate Statistics.
Books
Documentation provided by the lecturer Software documentation also available online A Tiny Handbook of R. Mike Allerhand. Springer Introductory Statistics with R. Peter Dalgaard. Springer
Frequency
Course attendance is strongly recommended.
Exam mode
Students have to pass a final practical test in the computer room, in which they have to a) Process and analyse real data, select the necessary output to answer the required questions and comment on it; b) empirically verify deduced theoretical laws or properties by simulation. These tests make it possible to assess competence in the use of the software; they also make it possible to test the ability to formalise a practical problem, the ability to carry out basic statistical analyses by independently processing data and interpreting the results, and to demonstrate learning of the basic concepts of statistics acquired in the curriculum courses.
Lesson mode
Lessons in the computer laboratory alternate between lectures and independent work by the student, who is asked to apply the methods and procedures independently to situations similar to those presented. Analyses of real case studies help to link the statistical theory, which is the student's background, with real analysis situations in order to arrive at a critical interpretation of the results obtained from the processing.
  • Lesson codeAAF1454
  • Academic year2025/2026
  • CourseStatistics, Finance and Actuarial sciences
  • CurriculumEconomia e finanza
  • Year3rd year
  • Semester2nd semester
  • CFU3