First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY
Course unit
STATISTICS FOR BRAIN AND COGNITIVE SCIENCES
PSO2044208, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY
PS1932, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination STATISTICS FOR BRAIN AND COGNITIVE SCIENCES
Department of reference Department of General Psychology
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit CANNOT be attended under the option Single Course unit attendance
Optional Course unit The Course unit is available ONLY for students enrolled in COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY

Lecturers
Teacher in charge GIULIO VIDOTTO M-PSI/03

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses M-PSI/03 Psychometrics 6.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 42 108.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Syllabus
Prerequisites: Probability, random variables, descriptive and inferential statistics, confidence intervals, t-tests, F-tests, basic issues in experimental design. The students can easily find materials on Internet (e.g. a comprehensive list of prerequisites is provided by the course of Statistical Methods in Brain and Cognitive Science on the MIT website).
Target skills and knowledge: The course is aimed at providing the understanding of the underlying theory and the practical problems required for the successful application of linear models. In particular, the course will provide students with skills on:

1. Theories and models on multiple linear regression, ANOVA, and generalized linear models.
2. Ability of interpreting data obtained by means of regression analysis and ANOVA.
3. Evaluation of quality and goodness of fit of the models.
Examination methods: Type of examination: Written .
Written examination: Excercises on data analisys by means of the statiticals software R.
Assessment criteria: The assessment of student performance will be based on the understanding of proposed statistical methods and on their ability to apply them independently in a research context. The final mark will be calculated by summing the socres obtained in each exercise.
Course unit contents: Matrix Algebra (an introduction). Simple Linear Regression: An algebraic and geometrical approach. Linear Models: Simple and Multiple Regression, Regression with Dummy Variables, ANOVA for Factorial Designs, Repeated Measures ANOVA, Analyses of Covariance, Contrasts and Multiple Comparisons. Generalized Linear Models (an introduction). Moreover, upon completion of the course the students should also be experienced in the use of the R Packages.
Planned learning activities and teaching methods: Frontal lessons and laboratory practices.
Additional notes about suggested reading: Course materials:
The slides to be used in the lectures and lab lessons.

Julian J. Faraway (2005). Linear models with R. Chapman & Hall/CRC.
Textbooks (and optional supplementary readings)
  • Faraway, Julian James, Linear models with R. Boca Raton [etc.]: Chapman & Hall/CRC, 2005. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory

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

Sustainable Development Goals (SDGs)
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