OVERVIEW

Data analysis and extraction of useful information contained in data is assuming a growing interest in the scientific field and beyond. In fact, laboratory analytical instrumentation nowadays provides a large amount of data for the characterization of the analysed samples and it is therefore essential to know how to manage and process such data in order to obtain reliable results and be able to represent them effectively and immediately understandable.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide tools for processing experimental data, such as those obtained during the activity of the analytical-chemistry laboratory. These tools include both the theoretical basis of statistical tests and their practical implementation through the use of spreadsheets and dedicated software. Particular attention will be paid to the graphical representation of the data and to their interpretation. The course includes practical computer exercises in order to make the student autonomous in the extraction of useful information from chemical and other type of data. The skills acquired after this course will be fundamental during the degree program and in future professional tasks, especially in the context of laboratory activities.

AIMS AND LEARNING OUTCOMES

At the end of the course the student will be able to:

- Define the concepts of location, dispersion and the descriptive parameters for a probability distribution.

- Illustrate the different types of standardized distributions, being able to represent them graphically and provide a proper interpretation.

- Understand principles of the statistical inference, describe and implement its use in statistical tests, set in the context of a chemical-analytical laboratory.

- Knowing how to present the key concepts of variance analysis (ANOVA) and possible applications.

- Use the basic functions of the MS-Excel software

- Knowing how to correctly and effectively represent the results obtained from data processing

PREREQUISITES

There are no prerequisites for this course

TEACHING METHODS

50% of the lessons of the course is composed by frontal lessons: the teacher, with the help of multimedia presentations and demonstrations using software, will illustrate the contents of the course.

The remaining 50% of the course will be carried out in accordance with the most innovative participatory teaching strategies, in order to stimulate student learning. In particular, it is foreseen to carry out exercises in the classroom and to assign exercises as homework through the use of software.

The last two lessons of the course will be dedicated to the correction of the assigned exercises, as well as to the execution of exam simulations for improving self-assessment.

Depending on the University provisions, the teaching activity can be carried out remotely with the help of MS-Teams. The course program and teaching methods will not change.

Any Student with documented Specific Learning Disorders (SLD), or with any special needs, shall reach out to the Lecturer(s) and to the dedicated SLD Representative in the Department ( Prof. Luca Raiteri, Luca.Raiteri@unige.it ) before class begins, in order to liaise and arrange the specific learning methods and ensure proper achievement of the learning aims and outcomes. VERY IMPORTANT: any request for compensatory tools and adaptations in the exam MUST be done within 10 working days before the date of the exam according to the instructions that can be found at https://unige.it/disabilita-dsa/comunicazioni

SYLLABUS/CONTENT

Applications of statistics in analytical chemistry:

Bases of descriptive statistics. Random, population and sample experiments. Absolute and relative frequency. Statistical regularity. Frequency and probability distributions. Central limit theorem. Normal distribution and cumulative distribution. Lilliefors normality test. Location and dispersion: descriptive parameters of a probability distribution. Standard normal distribution z. Probability of trust and meaning. Intervals of meaning. Propagation of variances. Distributions of the mean, chi-square, Student's t, Fisher's F. Statistical inference and main meaning tests based on the z, t, chi-square and F distributions. Comparison of two analytical methods. Analysis of variance (ANOVA). Q and G tests for the detection of anomalous data. Bivariate confidence intervals and associated tests.

RECOMMENDED READING/BIBLIOGRAPHY

The material provided by the teacher, in the form of multimedia presentations, contains all the information necessary for the study of the subject as well as for passing the exam. The material, updated annually, can be downloaded in .pdf format from the course website for the relevant academic year, on the AulaWeb platform.

In addition, the following texts are indicated to refer to in order to learn more about what the teachers presented:

- Applicazioni della statistica in chimica analitica: Michele Forina, "Fondamenta per la Chimica Analitica", Edizioni SISNIR, ISBN 9788890406461, freely downloadable from the website of the Italian Society of NIR Spectroscopy (http://www.sisnir.org/sisnir/download/fondamenta-per-la-chimica-analitica) and made available through the AulaWeb platform.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Classes will start in accordance with the academic calendar. Any changes to the timetable will be communicated on the course dashboard on the AulaWeb platform.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam consists of a written test with multiple choice questions.

Each student will be asked 20 questions presented in random order and different for each candidate (the test texts are generated by the teacher using software, in order to produce diversified exercises, but of comparable complexity, for each of the students enrolled) . The questions will be divided as follows: 10 relating to the understanding and learning of the basic concepts of univariate and bivariate statistics applied in the context of the analytical chemical laboratory, 5 which involve carrying out exercises on the MS-Excel spreadsheet (duly indicated in the text of the exam as "star exercises"), 5 relating to the graphical representation of the results obtained from data processing.

The written test is considered passed if at least 12 questions are answered correctly, of which at least 2 must be related to exercises involving the use of MS-Excel (duly indicated in the text of the exam as "star exercises"). The time available is one hour.

In order to take part in the written exam, it is mandatory to register through the student portal.

The outcome of the written exam will be communicated by the teachers by e-mail to each candidate, individually, in accordance with the rules and directives for the respect of privacy.

Depending on the current situation and the University provisions, the exam can be carried out remotely, without any change in the modalities, via the MS-Teams telematic platform, in a dedicated channel which will be communicated to the candidates in the days immediately preceding the carrying out the written test. To allow the teachers to monitor the progress of the tests, the candidate must keep the microphone and webcam active for the entire duration of the exam.

ASSESSMENT METHODS

The written exam will allow to verify the knowledge acquired by the student both from a conceptual and procedural point of view, thanks to the carrying out of numerical exercises, of content and complexity comparable to those carried out in the classroom. For the purposes of the evaluation, only the accuracy of the final result presented for each exercise will be considered by choosing the correct answer from those available in the question.

Exam schedule

FURTHER INFORMATION

For any updates that may become necessary during the academic year, students are referred to the course dashboard on the AulaWeb platform.

For students with DSA, disability or other special educational needs certification, it is requested to contact the teachers at the beginning of the course to agree on teaching and exam methods which, in compliance with the teaching objectives, take into account the methods learning opportunities and provide suitable compensatory tools.