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. 2017/18

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course Second cycle degree in
COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY (Ord. 2014)
PS1932, Degree course structure A.Y. 2014/15, A.Y. 2017/18
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
E-Learning website https://elearning.unipd.it/scuolapsicologia/course/view.php?idnumber=2017-PS1932-000ZZ-2016-PSO2044208-N0
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 (Ord. 2014)

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

Mode of delivery (when and how)
Period First semester
Year 2nd Year
Teaching method frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours of
Individual study
Shifts
Lecture 6.0 42 108.0 No turn

Calendar
Start of activities 09/10/2017
End of activities 12/01/2018

Examination board
Board From To Members of the board
5 2018 01/10/2017 30/09/2018 VIDOTTO GIULIO (Presidente)
BOTTESI GIOIA (Membro Effettivo)
NUCCI MASSIMO (Membro Effettivo)
SPOTO ANDREA (Membro Effettivo)

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 provides the understanding of the underlying theory and the practical problems required for the successful application of linear models. Topics are multiple linear regression, ANOVA, and generalized linear models.
Objectives are: Understand statistical foundation of regression model and ANOVA; Interpret the results of regression analysis and ANOVA; Assessing the quality of the models.
Examination methods: Type of examination: Written and possible oral test.
Written examination: Open questions.
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.
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.

For further deepening:
George H. Dunteman (1984), Introduction to Linear Models. Sage Publications.
John Fox (1997). Applied Regression Analysis, Linear Models and Related Methods. Sage Publications.
Textbooks (and optional supplementary readings)
  • Faraway, Julian James, Linear models with R. Boca Raton [etc.]: Chapman & Hall/CRC, 2005. Cerca nel catalogo