# MAT-20306 Advanced Statistics

## Vak

Studiepunten 6.00

 Onderwijstype Contacturen Lecture 24 Tutorial 14 Practical 24 Independent study
 Course coordinator(s) dr. C Dobre dr. EJ Bakker Lecturer(s) drs. LCP Keizer dr. ing. M Knotters SE Wilson dr. EJ Bakker dr. JA Hageman dr. M Malosetti dr. SK Schnabel dr. B Engel dr. EJH Korendijk dr. G Gort dr. LMW Akkermans C Zheng ir. SLGE Burgers dr DJ Brus dr. ir. J van Heerwaarden dr. WT Kruijer G Korontzis dr. JK Kampen prof. dr. FA van Eeuwijk dr. C Dobre dr. ir. E Heuvelink dr. ing. MPH Verouden Examiner(s) dr. EJ Bakker dr. G Gort

English

### Assumed knowledge on:

MAT-15303 Statistics 1 + MAT-15403 Statistics 2 or MAT-14303 Basic Statistics or MAT-15403 Statistics 2.
The student should be familiar with 1) The principles of probability calculus and the subjects: estimation, construction of confidence intervals and hypothesis testing from statistical inference 2) Application of these principles to inference about central values (mean or success probability) for the 1-sample and 2-sample situations, in case of Normal observations and binary (0,1) observations 3) Methods of analysis for simple (one explanatory variable) linear regression.
(To refresh this knowledge, (parts of) chapters 1 to 6 and 11 of the book can be studied.)

### Contents:

Per week we will have 2 lectures of two hours each (for groups up to 120 students) 2 computer practicals of two hours each (for groups of 20 students with 2 teachers / 1 teacher with one student assistant) and one T16 tutorial (of 2 hours) where students will do exercises with pen and paper (max 32 students supervised by 1 teacher and 1 student assistant).

The content will not / only marginally be adapted.

### Learning outcomes:

After the course the student should (within the limits of the subjects treated) be able to:
- translate a research question into a statistical hypothesis: make a plan (type of design or sampling procedure) for the data collection.
- choose an appropriate model with an understanding of the ingredients of the model in relation to the data;
- analyse the data (with SPSS);
- interpret the results and form conclusions relevant for the actual problem.

### Activities:

- lectures: follow classes;
- study the book and make exercises;
- computer practicals (compulsory): (learn how to) use SPSS and PQRS, work on case studies.

### Examination:

- written test with open questions and multiple choice questions, which needs to be passed (contribution to final mark: 100%).
- computer practical (attendance compulsory) has to result in a pass.

### Literature:

R. Lyman Ott; Longnecker, M.T. (2010). An Introduction to Statistical Methods and Data Analysis. 6th ed. 1296p.
Lecture notes available in English. (available: WUR-shop).

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