Applied statistics

Course Code :1001WETTST
Study domain:Chemistry
Academic year:2017-2018
Semester:1st semester
Sequentiality:Min. 8/20 for "Applied linear algebra I", "Applied calculus" and "Computer skills and statistical processing of measurements".
Contact hours:25
Credits:3
Study load (hours):84
Contract restrictions: No contract restriction
Language of instruction:Dutch
Exam period:exam in the 1st semester
Lecturer(s)Sandra Van Aert

3. Course contents *

The purpose of statistics is to summarize a large amount of data and from thisto retrieveuseful information.In the course "Applied statistics", the appropriate statistical methods to reach this goal are discussed and applied. The focus in this course is on practical exercises in which statistical methods are applied to a broad range of experimental data.

The course builds on the knowledge and skills acquired in the course "Computer skills and statistical processing of measurements" in BA1 . Knowledge and applications concerning random variables, probability distributions and probability densities, estimation of population parameters, interval estimators and hypotheses testing will be explored in greater detail. Next, the topic of statistical parameter estimation theory is discussed.

The course-content has the following chapters:

  • General introduction : purpose of statistics
  • Descriptive statistics : graphical and numerical representation to summarize the data
  • Probability theory
  • Univariate random variables : discrete and continuous random variables, probability distributions
  • Multivariate random variables : joint probability distributions, covariance, correlation and variance
  • Estimation of parameters : random sample averages, random sample proportions, random sample variance
  • Interval estimation : setting up of reliability intervals
  • Hypothesis testing : after a general introduction on testing, we deal with the most important tests for location, dispersion and distribution for different measurement scales and for 1, 2 or more than 2 populations respectively.
  • Parameter estimation