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Course module: 822187-B-6
822187-B-6
Statistics for CSAI I
Course info
Course module822187-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSH: Department Cognitive Science and AI;
Is part of
B Communication and Information Sciences
B Communication and Information Sciences (Spc.Cog.Sc & AI)
B Cognitive Science and Artificial Intelligence
Lecturer(s)
Lecturer
dr. E.M.J. Huis in 't Veld
Other course modules lecturer
Lecturer
dr. M. Postma
Other course modules lecturer
Lecturer
T.J. Wiltshire
Other course modules lecturer
Academic year2019
Starting block
SM 2
Course mode
Full-time
Remarks-
Registration openfrom 15/01/2020 up to and including 21/08/2020
Aims
  • Demonstrate competence with the basics of the R statistical programming language including, but not limited to, loading and working with data, finding and installing packages, and effectively applying functions
  • Comprehend and interpret effective visualizations of data using R
  • Interpret descriptive statistics to understand data using R
  • Describe and recognize the steps for null hypothesis significance testing as well as it’s limitations
  • Conduct and interpret output from non-parametric analyses using R
  • Conduct and interpret means comparison-based analyses including t-tests and analysis of variance using R
Content

This course involves a combination of lecture, demonstration, and practical exercises designed to introduce students to the basics of both the statistical programming language R as well basic statistical methods. The topics in the course include the basics of the R statistical programming language and navigating the R Studio environment, creating and interpreting different visualizations of data, understanding and utilizing functions that provide descriptive statistics about data, as well when and how to applying different inferential statistics including t-tests, analysis of variance, and non-parametric tests. 

Course Specifics:
This course is primarily assessed using individual midterm and final exams that are administered in person, which are each worth 40% of the final grade. Both the midterm and the final exam consist of multiple choice questions. Because these exams are not cumulative, students need to pass both the midterm and the final exam to pass the course. In addition, students must complete 10 practical exercises, which are worth a total of 20% of the final course grade. Practical exercises are only marked as complete/incomplete to gain those points and students are expected to self-evaluate their submissions based on an answer key that will be uploaded shortly after the deadline.
 
CSAI Bachelor students have to take part in "proefpersoonuren" for 10 participant pool credits. These hours can be completed by participating in human experiments (check out http://www.tilburguniversity.edu/students/studyding/regulations/oer/humanities/) and by participating in computational studies and hackathons announced by the study program director throughout the course of the academic year. For human experiments, students can enroll via this link for research: http://uvt.sona-systems.com/. This requirement does not apply to DSS premaster students.
 
Required Materials:
The required text for the course is D. Navarro, Learning Statistics with R and it is available to download for free from the following link:
http://compcogscisydney.org/learning-statistics-with-r/

You must complete the required course readings before our class meets that week. Check the course schedule weekly to be prepared for class.
 
Recommended Materials:
Field, A., Miles, J., & Field, Z. Discovering Statistics with R. 4th edition. Sage.

Course available for exchange students
Conditions of admission apply
Contact person
T.J. Wiltshire
Timetable information
Statistics for CSAI I
Written test opportunities
DescriptionTestBlockOpportunityDate
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Midterm / MidtermMIDTERM_01SM 2126-05-2020
Schriftelijk / WrittenEXAM_01SM 2102-06-2020
Midterm / MidtermMIDTERM_01SM 2217-06-2020
Schriftelijk / WrittenEXAM_01SM 2230-06-2020
Required materials
Literature
Selected chapters from Navarro, D. (2015). Learning statistics with R. Retrievable from http://compcogscisydney.org/learning-statistics-with-r/.
Title:Learning statistics with R
Author:D. Navarro
Publisher:Retrievable from http://compcogscisydney.org/learning-statistics-with-r/
Recommended materials
Literature
-
Title:Discovering Statistics
Author:Field, A., Miles, J., & Field, Z
Edition:4
Tests
Written

Midterm

Final Grade

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