MAT20306

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.

## Learning outcomes

After successful completion of this course students are expected to be able to:

• 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

## Prior knowledge

Assumed Knowledge:
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.

## Resources

• Credits
ECTS 6
• Level
bachelor
If anything remains unclear, please check the FAQ of Wageningen University.

## Offering(s)

• ### Start date

2 September 2024

• Ends
25 October 2024
• Term *
Period 1
• Location
Wageningen
• Instruction language
English
Enrolment period closed
• ### Start date

28 October 2024

• Ends
20 December 2024
• Term *
Period 2
• Location
Wageningen
• Instruction language
English
• Register between
1 Jun, 00:00 - 29 Sept 2024
Enrolment open
• ### Start date

6 January 2025

• Ends
31 January 2025
• Term *
Period 3
• Location
Wageningen
• Instruction language
English
• Register between
1 Jun, 00:00 - 24 Nov 2024
Enrolment open
• ### Start date

10 March 2025

• Ends
2 May 2025
• Term *
Period 5
• Location
Wageningen
• Instruction language
English
• Register between
1 Jun, 00:00 - 9 Feb 2025
Enrolment open
• ### Start date

12 May 2025

• Ends
4 July 2025
• Term *
Period 6
• Location
Wageningen
• Instruction language
English
• Register between
1 Jun, 00:00 - 6 Apr 2025
Enrolment open
These offerings are valid for students of TU Eindhoven