Academic year
2020/2021 Syllabus of previous years
Official course title
Course code
CT0131 (AF:320624 AR:172495)
On campus classes
ECTS credits
Degree level
Bachelor's Degree Programme
Educational sector code
1st Semester
Course year
Go to Moodle page
The course is one of the basic quantitative training activities of the Bachelor's Degree Programme in Environmental Sciences. The aim is to make the student familiar with the main statistical tools for use in environmental sciences.

The course provides basic knowledge of descriptive statistics, probability, statistical inference, linear regression.

At the end of the course, the student will be able to select the most suitable statistical methods for the data at hand. Moreover he will learn how to interpret and communicate the results to the various stakeholders.
1. Knowledge and understanding:
1.1 To know the statistical jargon.
1.2 To know the basic concepts of descriptive statistics.
1.3 To know the basic concepts of probability.
1.4 To know the basic concepts of statistical inference.
1.5 To know the basic concepts of linear regression.

2. Ability to apply knowledge and understanding:
2.1 To know how to use the statistical jargon.
2.2 To know how to apply descriptive statistical methods in environmental sciences.
2.3 To know how to apply probability in environmental sciences.
2.4 To know how to apply statistical inference in environmental sciences.
2.5 To know how to apply linear regression in environmental sciences.

3. Ability to judge:
3.1 To be able to select the most suitable statistical methods in environmental data analysis.
3.2 To be able to formulate statistical hypotheses.
3.3 To be able to test statistical hypotheses.

4. Communication skills:
4.1 To be able to communicate the results to the various stakeholders.
4.2 To be able to interact with the lecturer and the other students during the theoretical lessons and the data analyses.

5. Learning skills:
5.1 To be able to take lecture notes to integrate and clarify the content of the referral book.
5.2 To be able to self evaluate by addressing the lecturer’s questions and solving exercises.
Basic knowledge of set theory and combinatorics.
0 Introduction. Statistics today: data mining, data science, big data, open data. Statistics and environmental sciences. The statistical jargon.

1 Descriptive statistics. Variable types, charts and tables. Statistical distributions. Measures of location and scale.

2 Probability. Definitions. Conditional probability. Discrete and continuous random variables. The normal random variable. Mean and proportion sampling distributions.

3 Statistical inference. Point estimation. Confidence intervals for mean and proportion. One sample and two sample tests of hypotheses for mean and proportion. A brief account of nonparametric tests.

4 Linear regression. Linear correlation. The univariate linear model: definition and parameter estimation. Goodness of fit.

5 During the course some seminars will be held concerning: environmental sampling with examples taken from scientific publications and the validation of an analytical chemistry method used to study an environmental matrix (water, air, soil, biota, etc.).
Fondamenti di statistica per le discipline biomediche, by Marc M. Triola - Mario F. Triola, 2017. Pearson editore.
Teaching materials on moodle.
The achievement of the course objectives is assessed through a written exam. The exam includes both questions and exercises related to descriptive statistics, probability, inferential statistics, linear regression.

A list of formulas can be used during the exam as well as numerical tables. A pocket calculator can be used. Notes and books are forbidden.

Detailed information about the exam will be given during the first lesson. At the end of the course some simulated exams will be performed. Previous exams are available on moodle.
The course consists of
a) theoretical lessons describing the various concepts and methods
b) practicals with data analyses and result discussion and communication
c) seminars.
Definitive programme.
Last update of the programme: 21/04/2020