Advanced Statistics

This course covers several more advanced statistical models and associated designs, and techniques for statistical inference, as relevant to life science studies. The main topics are categorical data, (multiple) regression, analysis of variance (including multiple comparisons), analysis of covariance, and non-parametric tests. The aims of an analysis, the model assumptions, the properties (and limitations) of the models and associated inferential techniques and the interpretation of results in terms of the practical problem will be discussed. Focus will be upon students gaining an understanding of the model ingredients, an (intuitive) understanding of inferential techniques, insight into data structures and implications for choice of model and analysis. Students will be able to perform analysis of data with statistical software, i.e. with R-Studio.

• Formulate a statistical hypothesis based on a research question

• Recognize a valid experimental design or sampling procedure for data collection

• Select an appropriate statistical model that allows valid estimation, quantification of uncertainty, and hypothesis testing given the research question and properties of the data

• Produce relevant computer output in R (RStudio) for a given data set and research question

• Interpret the outcome of the statistical analysis, draw valid conclusions and argue the relevance for the actual problem

• Written test with open and closed questions (100%) Open book (see specifications on brightspace)
• Performance (0%) We require students to be active and work seriously on the exercises of the computer practicum
• Mandatory attendance (%) Attendance of all 12 computer classes is mandatory

MAT15303 Statistics 1 + MAT15403 Statistics 2 or MAT14303 Basic Statistics or MAT15403 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. or the Brightspace Module 'Ready to Advance in Statistics' can be worked through. Via Brightspace > Discover, enroll yourself.

• R. Lyman Ott; Longnecker, M.T. (2016). An Introduction to Statistical Methods and Data Analysis. 7th ed. 1174p. Lecture notes available in English. You can order your readers at https://wurreadershop.proefschriftmaken.nl/shop/wur. For literature books, please go to https://wur.acco.be/ If you have any questions, contact your course coordinator.