Concepts of Probability and Statistics (1798)
  
Coordinating lecturer :Prof. dr. Roel BRAEKERS 
  
Member of the teaching team :Mevrouw Ilaria MISURI 
 Prof. dr. Ivy JANSEN 
 Mevrouw Liz LIMPOCO 
 De heer Pieter GIESEN 

Language of instruction : English
Credits: 5,0
  
Period: semester 1 (5sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Prerequisites

The student has a strong knowledge of mathematics.

In the following mathematical topics, the student has a ready knowledge of the calculation techniques:

  • Set theory
  • Functions (univariate and multivariate)
  • Limits and Infinite Sequences
  • Sums and Series
  • Derivatives (univariate and multivariate) and optimization problems
  • Integrals ((In-)Definite and improper integrals, Gamma and Beta functions).


Content

In this course, we deal with the following topics:

  • Descriptive statistics:
    • different types of variables
    • measures of centrality
    • measures of variability
    • measures of relative standing
    • graphical methods to present data
  • Basic probability theory:
    • sample space, events, probability, combinatorics
    • Law of total probability, Bayes rule
    • stochastic variables, (joint, conditional) distributions, (conditional) expectations
    • transformation of distributions
    • Law of large numbers, Central limit theorem
    • generating samples from a population
  • Statistical inference:
    • Confidence intervals: CI for mean(s), CI for proportion(s), CI for variance(s)
    • hypothesis testing: null-hypothesis, alternative hypothesis, test-statistic, critical value, p-value
      • hypothesis for mean, proportion and variance
      • comparing means, proportions, variances
    • Introduction to estimation methods: maximum likelihood, methods of moments, least squares method


Organisational and teaching methods
Organisational methods  
Collective feedback moment ✔  
Lecture ✔  
Small group session ✔  
Teaching methods  
Exercises ✔  


Evaluation

Semester 1 (5,00sp)

Evaluation method
Written evaluation during teaching period10 %
Homework✔
Written exam90 %
Open questions✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory textbooks (bookshop)
 

Textbook 1:

Mathematical Statistics and Data Analysis, John A. Rice, Third Edition, Cengage

ISBN: 9780495118688
 

Compulsory course material
 

Lecture notes for the lectures will be provided by the lecturer through the electronic platform.

R and R-studio will be used as softwares in the course. 


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
1st year Master Bioinformatics N
1st year Master Bioinformatics - icp N
1st year Master Biostatistics N
1st year Master Biostatistics - icp N
1st year Master Data Science N
1st year Master Quantitative Epidemiology - icp N
1st year Quantitative Epidemiology N
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.