Workshop: Software Tools for Statistics
A.Y. 2024/2025
Learning objectives
The course aims to train students to manage theoretical and modern computational tools of statistics, by analyzing simulated and real data sets. We introduce and use the open source R software, which is the state-of-art in the scientific community and widely used in many industrial and commercial environments. Students will improve their computing and modelling abilities as well as their probem solving attitudes.
Expected learning outcomes
The student will be able to perform a simple statistical data analysis and to produce reports, using the R software. S/he shall have acquired the ability to use basic R tools and to extract relevant information from the available data, still keeping the consciousness of the uncertainty of the results.
Lesson period: First semester
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
During the course the basic theory and the implementation in R of the following statistical techniques will be introduced:
1. Distributions, and parameters estimates
2. Confidence intervals
3. Hypotheses tests
4. Linear regression
The implementation in R will be explained on the basis of suitable case studies.
1. Distributions, and parameters estimates
2. Confidence intervals
3. Hypotheses tests
4. Linear regression
The implementation in R will be explained on the basis of suitable case studies.
Prerequisites for admission
The students should be familiar with basic math, probability and statistics. They should also be familiar with basic operations on a PC (at least one among macOS, Windows, and Linux), such as how to install and use programs.
Teaching methods
Frontal lectures and online sessions. Frontal lectures introduce the statistical techniques to be implemented using R. Online sessions with RStudio are devoted to case studies: students will create reports using mainly their laptops (macOS, Windows, Linux allowed).
Teaching Resources
1. R.V.Hogg, E.A. Tanis, D.L.Zimmerman, Probability and Statistical Inference, Pearson, Nineth Edition or more recent ones
2. H.Smith, N.R.Draper, Applied regression analysis, Wiley, 3rd edition or more recent ones
3. Nicholas J. Horton, Ken Kleinman, Using R and RStudio for Data Management, Statistical Analysis and Graphics, CRC Press, Second edition or more recent ones.
4. Notes of the teachers
2. H.Smith, N.R.Draper, Applied regression analysis, Wiley, 3rd edition or more recent ones
3. Nicholas J. Horton, Ken Kleinman, Using R and RStudio for Data Management, Statistical Analysis and Graphics, CRC Press, Second edition or more recent ones.
4. Notes of the teachers
Assessment methods and Criteria
The evaluation of the achievements of the training objectives will consist in the individual production of reports made using R source code. Each report will be focused on a case study of statistical data analysis proposed during the course.
The student will be eligible to be evaluated only after delivering all the required reports, each one containing all the listed points properly implemented.
The student will be eligible to be evaluated only after delivering all the required reports, each one containing all the listed points properly implemented.
Professor(s)