Statistics II: probability Theory and inductive Statistics

6 ECTS credits
180 h study time

Offer 2 with catalog number 1023416ANW for working students in the 2nd semester at a (A) Bachelor - preliminary level.

Semester

2nd semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Enrollment Requirements

NOTE: registration for this course is only possible for working students. Day students can register for courses whose code ends with an R. At Inschrijven / studentenadministratie@vub.be you must be registered at the VUB as a working student for the current academic year.

Taught in

Dutch

Partnership Agreement

Under agreement for exchange of courses

Faculty

Faculty of Psychology and Educational Sciences

Department

Experimental and Applied Psychology

Educational team

Peter Theuns (course titular)
Alain Isaac
Femke Legroux
Jeroen Frans

Activities and contact hours

26 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals

Course Content

This course provides an introduction to and foundation for statistical analysis.
This course also introduces use of statistical software (students can individually opt for either SPSS or R).
Topics covered in the lectures and WPOs:
Probability theory
• Combinatorics
• Probability models: simulation and theoretical
• Classical definition of probability versus frequential definition
• Basic rules of probability theory
o Summation theorem
o Conditional probabilty
o Product rule
o Bayes' rule
Stochastic variables
• Discrete
• Continuous (area under the curve)
Sampling distributions
• Sampling distributions for numbers and proportions
o Sampling with and without replacement
o Binomial distributions
o Normal approximation of binomial distribution
o Continuity correction
• Sampling distribution of sample means
o Expected value and standard deviation of sample means
o Central limit theorem
Introduction to Statistical Inference
• Reliable estimation
o Point estimates
- Maximum Likelihood
Pure and impure estimators
o Confidence intervals
o Determining a required sample size
• Significance tests
o z test

Statistical software:
Students choose either R or SPSS for themselves. Introduction to the software opted for largely consists of self-study using a instructive reader, supplemented with a number of classical exercises sessions and guided self-study.

Script or syntax-based data-analysis is instructed and required.

Course material

Handbook (Recommended) : Statistiek 1, Syllabus, Prof. Jan De Neve

Additional info

Additional study materials, including lecture recordings and slides (Dutch), are made available via CANVAS. In addition to the Dutch-language material, foreign-language students can rely on an English-language textbook.

Learning Outcomes

General Competences

Apart from a very thorough knowledge of the theory, it is mainly expected that the student can independently apply the learned techniques to realistic data and use them to solve presented problems.

Explain examples of probability models

Apply the laws of probability

Explain a "sampling distribution"

Create and interpret confidence intervals

Explain and motivate the reasoning for significance tests

Performing and interpreting a z-test on sample means and reporting in APA style

Grading

The final grade is composed based on the following categories:
Written Exam determines 70% of the final mark.
Practical Exam determines 30% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

Exam theorie + exercises with a relative weight of 70 which comprises 70% of the final mark.

Within the Practical Exam category, the following assignments need to be completed:

Exam software with a relative weight of 30 which comprises 30% of the final mark.

Additional info regarding evaluation

The evaluation consists of 2 components that contribute the following proportions to the final grade:

· - 70% Written exam (multiple-choice with an elevated threshold)

· - 30% Practical exam software (open questions)

Both components of the exam must be completed; otherwise, the final score is marked as "absent."

If you fail in one of these components, the grade of the lowest scoring component will be considered as the final grade for the entire subject.

If you fail the subject, the partial grade of a component for which at least 60% of the maximum grade was achieved can be transferred from the first to the second exam session. Transfer of (partial) grades to a subsequent academic year is not permitted. If a partial exam is retaken in the second session, the partial score from the first session is void.

Generative AI may not be used in the exam.

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context