 At the end of this course, you are able to:
 translate univariate problems from scientific practice to statistically meaningful questions and solutions
 perform the following statistical analyses in a correct way, using univariate data:
 parametric hypothesis test
 nonparametric hypothesis test
 linear regression
 Analysis of Variance (ANOVA)
 construct a simple experimental design to evaluate the influence of multiple factors on the outcome of an experiment
 interpret the output from statistical software, given as pvalues or figures

 Repeated measurements often do not lead to identical results due to the occurrence of random errors, already discussed during Chemical Analysis (NWIMOL001). Therefore, a basic knowledge of statistics is indispensable for every student in the molecular sciences. Statistics may for instance be used to draw statistically founded conclusions regarding the influence of experimental conditions (for example "A higher temperature leads to a significantly higher yield"). Also, statistics provides ways to set up a set of experiments such that the most information is obtained with the least amount of effort ('Design of experiments').
When performing experiments and gathering results yourself, as you will do frequently during your studies, it is of the utmost importance to correctly interpret these results. During this course, you will learn to draw conclusions based on the outcome of statistical tests. Most of these are based on univariate data: data where for each object/sample 1 property has been measured. Statistical analyses for data with more than 1 property per object/sample will be discussed during the lectures. We will also take a look at different experimental designs.
The course consists of lectures and workshops. Each week starts with a short workshop in which you get acquainted with the theory using multiplechoice questions. You are expected to have studied the reader prior to this workshop; during the workshop there is not much time to read and study the reader. During the lecture, which follows the introductory workshop, some aspects of that week’s subject will be explained in more detail or deepened. Moreover, extensions to multivariate statistics will be provided. In the second workshop, you will perform statistical analyses yourself and draw conclusions based their outcome. 




You should prepare yourself for each course week by studying the relevant chapter or chapters from the reader. 
• Distributions • Confidence intervals • Statistical tests (both parametric and nonparametric) • Correlation and regression • Analysis of Variance (ANOVA) • Experimental design 
Chemical Analysis (NWIMOL001)This is a course in the theme 'Methods'. 
Obligatory: • Reader and exercises (will be distributed via Blackboard) Recommended: • James N. Miller en Jane C. Miller, Statistics and Chemometrics for Analytical Chemistry, Prentice Hall, 6th ed., ISBN13 9780273730422 • Roxy Peck, Chris Olsen, Jay Devore, Introduction to Statistics and Data Analysis, Brooks/Cole, 4th ed. (International version), ISBN13 9780840068392 
• 8 hours lecture • 24 hours problem session • 52 hours individual study period 
  Verplicht materiaalDictaatReader and exercises (will be distributed via Blackboard) 

 Aanbevolen materiaalBoekJames N. Miller en Jane C. Miller, Statistics and Chemometrics for Analytical Chemistry, Prentice Hall, 6th ed 
ISBN  :   9780273730422 

 WerkvormenComputerpracticum
 Hoorcollege
 Werkcollege

 ToetsenTentamenWeging   1 
Gelegenheden   Blok KW1, Blok KW2 


 