Data analysis and statistical methods
Faculteit  Science and Engineering 
Jaar  2017/18 
Vakcode  WMEE14000 
Vaknaam  Data analysis and statistical methods 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester I a 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Data analysis and statistical methods  
Leerdoelen  At the end of the course, the student is able to: 1) Solve basic problems relating to probability, distributions, error analysis, hypothesis testing, and linear regressions. 2) Explain simpler and more complex statistical methods, such as time series analysis. 3) Apply these techniques to analyze realistic data sets and to assess which techniques are appropriate for a certain problem. 4) Examine the theory behind the statistical methods, which helps to evaluate usefulness and limits of statistical methods depending on context. 5) Use the knowledge gained in this course to investigate and correctly apply new statistical methods that might be encountered during the master research project (not specifically tested at the end) 

Omschrijving  Content: Probability, distributions commonly encountered in environmental science and their properties, estimation of means and other parameters, hypothesis testing, experimental uncertainties and error propagation, linear regression and fitting, introduction to time series analysis After the course students should be able to: (1) Solve basic problems relating to probability, distributions, error analysis, hypothesis testing, and linear regressions and explain more complex statistical methods, such as time series and multivariate analysis. (2) Use these techniques to analyze realistic data sets and to assess which techniques are appropriate for a certain problem. (3) Understand the theory behind the statistical methods, which helps to: (a) Evaluate usefulness and limits of statistical methods depending on context. (b) Use the knowledge gained in this course to investigate and correctly apply new statistical methods that might be encountered during the master research project (not specifically tested at the end) Structure: 2 x 2 hour lecturestutorials / week: The lectures are focused on understanding the theoretical background of a statistical technique and the illustration of the technique with the help of simple examples. The tutorials will be used to discuss the assigned problem sets (Approximately 6 problem sets will be assigned). 1 x 2 hour computer practicum/week: Application of statistical techniques using the statistical programming language R. During the course the students will work on individual projects, where every student analyzes a real data set using methods learned in this course and writes a small report. During the last week of the course extra time will be devoted to finishing this project. 

Uren per week  variabel  
Onderwijsvorm 
Hoorcollege (LC), Opdracht (ASM), Practisch werk (PRC), Werkcollege (T)
(Lectures (17 hours); Tutorial: problem set discussion (5 hours); Assignments: problem sets (42 hours) and final project (25 hours); Practical: computer practicum (14 hours).) 

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE), Verslag (R)
(Written exam, project report, assignment (problem sets)) 

Vaksoort  master  
Coördinator  U. Dusek, PhD.  
Docent(en)  C.B. Davis, PhD. , U. Dusek, PhD.  
Verplichte literatuur 


Entreevoorwaarden  The course unit assumes some basic prior knowledge about probabilities and probability distributions. If necessary this knowledge can be gained by reading the mandatory book.  
Opmerkingen  The exam must be passed to pass the course. Final grade: if exam points < =55 > Gr = fail. if exam points > 55, Gr = sum(problem set points)/number of problem sets*0.3 + exam grade *0.4 + project grade*0.3 Note: Each problem set is worth 10 points 

Opgenomen in 
