|Teaching method||Contact hours|
Language of instruction:
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.)
Note: This course can not be combined in an individual programme with MAT-24306 Advanced Statistics for Nutritionists and/or MAT-25303 Advanced Statistics (DL).
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 3 hours) where students will do exercises with pen and paper (max 32 students supervised by 1 teacher and 1 student assistant).
After successful completion of this course students are expected to (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 and R);
- interpret the results and form conclusions relevant for the actual problem.
- lectures: follow classes;
- study the book and make exercises;
- computer practicals (compulsory): (learn how to) use SPSS, R and PQRS, and work on case studies;
- pen and paper practicals: work on exercises and case studies where computer output is given.
- 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.
R. Lyman Ott; Longnecker, M.T. (2016). An Introduction to Statistical Methods and Data Analysis. 7th ed. 1174p.
Lecture notes available in English. (available at the WUR-shop).