Chemometrics
A.Y. 2024/2025
Learning objectives
This Chemometrics course aims to give students the skills to design chemical experiments that effectively collect useful data. It also aims to introduce them to statistical and modeling techniques for data analysis using the R software. A key objective is to provide students with the skills needed to design and analyze experiments to evaluate the effect of factors on a response variable, highlighting the importance of randomization, replication, and blocking.
Expected learning outcomes
"At the end of the Chemometrics course, the student will be able to:
Knowledge and Understanding: Demonstrate a solid understanding of the statistical principles underlying statistical analysis techniques (with particular emphasis on significance tests and the regression model) and experimental design.
Application: Design experiments using the principles of randomization, blocking, and replication. Correctly apply the appropriate statistical tests to evaluate the effect of two or more factors on a response variable, and regression models. Analyze and interpret the results of statistical analyses.
Communication: Communicate the results of statistical analyses effectively, both in written and oral form, using appropriate language and supported by clear graphs and tables.
IT Skills: Use the R software for data analysis, implementing the learned techniques and interpreting the outputs."
Knowledge and Understanding: Demonstrate a solid understanding of the statistical principles underlying statistical analysis techniques (with particular emphasis on significance tests and the regression model) and experimental design.
Application: Design experiments using the principles of randomization, blocking, and replication. Correctly apply the appropriate statistical tests to evaluate the effect of two or more factors on a response variable, and regression models. Analyze and interpret the results of statistical analyses.
Communication: Communicate the results of statistical analyses effectively, both in written and oral form, using appropriate language and supported by clear graphs and tables.
IT Skills: Use the R software for data analysis, implementing the learned techniques and interpreting the outputs."
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
1) Review of probability and statistics: random variables; Gaussian distribution; point estimation of the parameters of a probability distribution; the linear regression model and the least squares method for estimating parameters; the principal component analysis.
2) Reference distribution, the concepts of blocking and randomization. Hypothesis testing and confidence intervals.
3) The ANOVA technique for comparing k different treatments in a completely randomized design and in a randomized block design.
4) The 2-level factorial design
5) The fractional factorial design
6) The split-plot design (if we have time enough)
2) Reference distribution, the concepts of blocking and randomization. Hypothesis testing and confidence intervals.
3) The ANOVA technique for comparing k different treatments in a completely randomized design and in a randomized block design.
4) The 2-level factorial design
5) The fractional factorial design
6) The split-plot design (if we have time enough)
Prerequisites for admission
Basic knowldge in mathematics and linear algebra is required. It is desirable to have acquired the notions of probability and random variables, but not strictly necessary because they will be briefly recalled during the lectures.
Teaching methods
The lectures relating to the theory will mainly be carried out on the blackboard without the use of slides.
The examples of statistical analyses, presented during the lectures,, will be developed through the use of the statistical software R (which is open source).
Students are invited to intervene to express their doubts, in addition, the professor will propose questions and exercises to be carried out in the classroom, in order to make the lessons more interactive and useful.
The examples of statistical analyses, presented during the lectures,, will be developed through the use of the statistical software R (which is open source).
Students are invited to intervene to express their doubts, in addition, the professor will propose questions and exercises to be carried out in the classroom, in order to make the lessons more interactive and useful.
Teaching Resources
Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building. By Box, George E. P; Hunter, William Gordon; Hunter, J. Stuart. New York : Wiley.
Notes on principal component analysis, written by the professor and available on the platform Myariel
Notes on principal component analysis, written by the professor and available on the platform Myariel
Assessment methods and Criteria
Written exam lasting one hour, consisting of 5 multiple choice theoretical questions and 2 data analysis exercises to be carried out with the software R.
CHIM/01 - ANALYTICAL CHEMISTRY - University credits: 3
SECS-S/01 - STATISTICS - University credits: 3
SECS-S/01 - STATISTICS - University credits: 3
Lessons: 48 hours
Professor:
Tommasi Chiara
Shifts:
Turno
Professor:
Tommasi ChiaraEducational website(s)
Professor(s)
Reception:
Wednesday from 9:00 to 12:00
Via Conservatorio, III floor, Room n. 35