Chemometrics

The 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 aspect 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.
The primary objective of the course is to equip students with the necessary skills to:
· Understand the fundamental principles of statistics and probability, including point estimation and significance tests.
· Choose and apply appropriate statistical tests for comparing two means (independent and dependent samples).
· Use analysis of variance (ANOVA) to compare the means of multiple groups and understand the importance of randomization and experimental designs (blocking, Latin square).
· Design and analyze factorial experiments to evaluate the effect of multiple factors on a response, considering the importance of randomization.
· Build, validate, and interpret linear regression models.
· Apply principal component analysis (PCA) for dimensionality reduction and pattern identification in data.
· Design optimal experiments (D-optimal) to maximize information obtained with limited resources, considering the importance of randomization.
· Implement and interpret statistical analyses using the R software.

At the end of the Chemometrics course, the student will be able to 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.
In particular, the student will be able to:
1. design experiments using the principles of randomization, blocking, and replication;
2. correctly apply the appropriate statistical tests to evaluate the effect of two or more factors on a response variable, and regression models;
3. analyze and interpret the results of statistical analyses;
In addition, the student will learn how to communicate the results of statistical analyses effectively, both in written and oral form, using appropriate language and supported by clear graphs and tables.
Finally, the student will develop the ability of using the R software for data analysis, implementing the learned techniques and interpreting the outputs.