94339 - Statistical Methods for Natural Sciences

Course Unit Page

  • Teacher Dora Melucci

  • Credits 6

  • SSD CHIM/01

  • Teaching Mode Traditional lectures

  • Language Italian

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Teaching and Communication of Natural Sciences (cod. 5704)

  • Teaching resources on Virtuale


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2023/2024

Learning outcomes

At the end of the course, the student has the theoretical and practical basis of univariate and multivariate statistics for the processing data obtained from experimental measurements of natural variables. In particular, the student can design experiments and can process data using modern software packages. Finally, the student knows how to apply the skills acquired for the design of didactic experiences or dissemination communications, also by searching for information on the web.

Course contents


The student who accesses this course must have a good preparation in the fundamentals of basic laboratory techniques of biology, chemistry and geology.


The course aims to provide the student with the ability to design an educational experience or an informative communication concerning the natural science laboratory, starting from the design of experiments to arrive at the correct data processing and presentation of results.

With these objectives, the following mathematical and statistical knowledge are provided: elements of statistical analysis; data exploration methods; modeling methods: classification and regression; Design of experiments (DOE).


Error propagation. Significant figures. Confidence interval. Significance test. Calibration by regression. Quality parameters of the experimental data. Validation of experimental methods. Data pretreatment. Transformation of variables. Data exploration: cluster analysis and principal component analysis. Statistical models: control parameters, classification by discriminant analysis, regression by least squares method and by principal components. Design of experiments: multivariate methods for the choice of standard samples and variables for the construction of the models. Use of software packages (type "Office") for the management of spreadsheets, for the preparation of written documents and for the preparation of informative presentations; use of software packages (type "R") for writing programs for the application of statistics to scientific problems. Use of on-line databases for bibliographic research.


J.C. Miller, J.N. Miller, Statistics and Chemometrics for Analytical Chemistry, Pearson Education, 2010.

- Richard G. Brereton, Applied Chemometrics for Scientists, Wiley, 2007.

- Richard Kramer, Chemometric techniques for quantitative analysis, Marcel Dekker, 1998

- Ron Wehrens, Chemometrics with R, Spinger, 2011

Teaching methods

Lecturing and computer exercises.

Teaching material published on virtuale.unibo.it

Attendance in presence is strongly recommended for all teaching activities; please note that it will no longer be possible to follow the teaching activities live on TEAMS.
In any case, all lessons and exercises will be recorded and made visible on virtuale.unibo.it, in order to facilitate students who are unable to attend in person or who want to review the lessons to verify unclear passages.

The course participates in the University's didactic experimentation project: INTEGRATIVE DIGITAL TEACHING.

Assessment methods

Students produce a report on an assigned experimental problem.

The report is discussed during an oral exam, in which questions about the general theory are also asked.

Teaching tools

Blackboard, video projector, computer, software.

Office hours

See the website of Dora Melucci