Statistics for food and nutrition sciences
Learning outcomes of the course unit
Students will learn about the philosophy of classical statistics as the science of induction, the main concepts in descriptive statistics, the normal distribution, significance testing, linear regression.
Students will be introduced to study design and analysis in epidemiology, to the use of ANOVA in experimental design and to statistical methods particularly relevant for food and nutrition sciences.
Frequency course in Mathematics.
Course contents summary
Foundations of statistics:
Population and sample; descriptive statistics; probability distributions; some classical tests of hypotheses on means and proportions; correlation and linear regression.
Introduction to design of experiments and epidemiological studies.
Experimental versus observational data; analysis of variance for simple experimental designs.
Types of epidemiological studies; confounding and effect modification; association and causation ; evidence synthesis by meta-analysis (tentative).
Analysis of multivariate systems:
Principal component analysis, cluster analysis, discriminant analysis. Examples from nutrition and food chemistry.
B.R. Kirkwood, J.A.C. Sterne, Essentials of Medical Statistics (2nd Edition), Blackwell Publishing, 2003
Traditional. Lectures with the use of audiovisual.
Assessment methods and criteria