|Module lecturer||dr Iwona Gulaczyk, prof. dr hab. Marek Kręglewski|
|Lecturer position||Senior Lecturer|
|Faculty||Faculty of Chemistry|
Module aim (aims)
• to deepen students' understanding of statistics
• to teach them how to handle data using statistical tools in order to gain useful information and practical knowledge which is essential for drawing conclusions and making decisions.
Pre-requisites in terms of knowledge, skills and social competences (where relevant)
The course is dedicated to both the students with some basic knowledge of statistics and to students who are not familiar with statistics at all. Basic knowledge of mathematics and MS Excel is required.
Week 1: Describing the data (types of data, graphical tools)
Week 2: Probability, expectation values
Week 3: Probability distributions
Week 4: The binomial distribution
Week 5: The Poisson distribution
Week 6: The Gaussian distribution
Week 7: Sampling distributions and estimation (central limit theorem, standard error of the mean)
Week 8: Student’s t distribution (confidence intervals, determining sample size)
Week 9: Hypothesis testing. One-sample hypothesis tests of the mean (two-sided and one-sided tests)
Week 10: Two-sample hypothesis tests of the mean
Week 11: Hypothesis tests of variance (one-sample test and two-sample test)
Week 12: The F distribution. Chi-square (?2) distribution.
Week 13: The analysis of variance (ANOVA).
Week 14: Linear regression analysis (the straight line fit, covariance, correlation)
Week 15: Polynomial regression
1) Statistics, R. J. Barlow
2) Basic statistics, M. J. Kiemele, S. R. Schmidt, R. J. Berdine