First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
BIOENGINEERING
Course unit
STATISTICAL METHODS FOR BIOENGINEERING
INP9087105, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
BIOENGINEERING
IN0532, Degree course structure A.Y. 2011/12, A.Y. 2019/20
N0
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL METHODS FOR BIOENGINEERING
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2019-IN0532-000ZZ-2019-INP9087105-N0
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ALESSANDRA BERTOLDO ING-INF/06

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/06 Electronic and Information Bioengineering 9.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2011 course timetable

Examination board
Board From To Members of the board
1 A.A. 2019/2020 01/10/2019 15/03/2021 BERTOLDO ALESSANDRA (Presidente)
DALLA MAN CHIARA (Membro Effettivo)
DEL FAVERO SIMONE (Supplente)
FACCHINETTI ANDREA (Supplente)
PEDERSEN MORTEN GRAM (Supplente)
SACCOMANI MARIAPIA (Supplente)
SAWACHA ZIMI (Supplente)
SCHIAVON MICHELE (Supplente)
SPARACINO GIOVANNI (Supplente)
VISENTIN ROBERTO (Supplente)

Syllabus
Prerequisites: Elements of Probability and Statistics
Target skills and knowledge: Use the descriptive statistics for the representation of experimental data; understand the main statistical tests; knowing how to use the method of linear least squares with familiarity; make the student able to perform regression and correlation analysis, principal component analysis (PCA), independent component analysis (ICA), clustering.
Examination methods: The verification of knowledge and skills will be evaluated with a written exam, based on the material presented in the course and a computer test based on the material presented in the laboratory.
Assessment criteria: The written examination is divided into two parts. In the first, students must answer simple questions aimed at verifying the knowledge of a single technique or property discussed in the course.
The second part is of a more theoretical nature and concerns the knowledge of the theoretical bases of the main methods presented in class.
The computer test verifies the learning of both the most relevant notions and the processing techniques, which were presented during the laboratory hours carried out during the course.
The overall grade is the sum of the marks obtained in the individual parts.
Course unit contents: Descriptive statistics: frequency distributions, percentiles, graphical representations, position, dispersion and shape indices, average and variance calculation for grouped data, boxplots, comparative analyzes, variable correlations
Random variables: expected value, variance, confidence intervals, Chebychev inequality. The theorem of expectation. Discrete probability distributions: binomial, negative binomial, Poisson. Continuous probability distributions: exponential, gamma, normal. Vector random variables: independence, multivariate normal distribution.
Data acquisition: random sampling, distortions, experimental and observational studies, randomized controlled clinical trials, double-blind, crossover studies.
Statistical tests: Normality test. Test to evaluate the difference between groups: Test T (coupled or not), ANOVA. The meaning of the p-value. Non-parametric tests. Repeated measurements and post-hoc comparisons: Bonferroni correction, False Discovery Rate, correction with permutations.
Calculation of the power of a test: sample power and amplitude
Experiment project: type of design (2 or more parallel groups, 1 paired group (pre-post drawing), 2 crossed groups (cross-over drawing), 2x2 parallel groups (factorial design), ranomized), exchangeability, systematic bias
Statistical inference: Linear regression; the linear least squares method, properties, bias, MSE, residuals, estimation error (variance of the estimator)
Verification of the relationship between variables: regression and correlation, rank correlation (Spearman). Bland-Altman method.
Principal component analysis (PCA)
Regularization methods for linear models: Ridge regression, Lasso regression, Elastic Net, logistic regression
Independent component analysis (ICA)
Clustering: K-means, Fuzzy c-means, Spectral, Hierarchical
Planned learning activities and teaching methods: The course will be perfomed by:
- lectures (around 65% of the total lesson hours)
- "hands on" lessons developing codes in Matlab (35%) and working on concrete case studies.
Additional notes about suggested reading: All the teaching material presented during the lectures (in pdf format) is made available using the Elearning platform.
Textbooks (and optional supplementary readings)
  • Hastie, Trevor J.; Tibshirani, Robert, The elements of statistical learningdata mining, inference, and predictionTrevor Hastie, Robert Tibshirami, Jerome Friedman. New York: Springer, 2009. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Case study
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education