MAT-25303 Advanced Statistics (online)

Course

Credits 3.00

Teaching methodContact hours
Knowledge clip0
Distance group work0
E-learning material0
Course coordinator(s)dr. G Gort
Lecturer(s)dr. G Gort
dr. B Engel
Examiner(s)dr. G Gort
dr. B Engel

Language of instruction:

English

Assumed knowledge on:

Basic Statistics

Continuation courses:

The DL-MSc-specializations will have their own schedule

Contents:

Note: This course can not be combined in an individual programme with MAT-24306 Advanced Statistics for Nutritionists and/or MAT-20306 Advanced Statistics.
Note: This course has a maximum number of participants. The deadline for registration is one week earlier than usual. See Academic Year.(http://www.wageningenur.nl/en/Education-Programmes/Current-Students/Agenda-Calendar-Academic-Year.htm) -> Registration for Courses. 
Note: The period mentioned below is the period in which this course starts. For the exact academic weeks see the courseplanning on www.wur.eu/schedule.


Statistical design and analysis of data using R; statistical methods for analysis comprise simple and multiple regression, one-way and two-way analysis of variance (with and without interaction), analysis of covariance, chi-square tests for contingency tables, logistic regression.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- comprehend basic ideas of statistical inference, experimental design and data collection, such as random sampling, randomisation and blocking, for experimental and observational studies;
- determine an appropriate statistical model and associated statistical inference procedure, given the description of the experiment and research question, for continuous data (in the context of linear regression, analysis of (co)variance) and discrete data (in the context of goodness-of-fit and contingency tables for categorical data and logistic regression for binary data and proportions);
- carry out the analysis, for a given problem, using the statistical program R, check model assumptions, interpret results, and formulate conclusions in terms of the actual problem.

Activities:

Study knowledge clips with theoretical assignments, practical assignments and case studies using R, reporting on results.

Examination:

The exam is an online remotely proctored exam, where the student should provide a suitable computer and room.

Literature:

Ott, RL, Longnecker M (2016) An introduction to statistical methods and data analysis (7th edition), Brooks/Cole ISBN-13 978-1-305-26947-7

ProgrammePhaseSpecializationPeriod
Compulsory for: MPSPlant SciencesMScF: Plant Breeding5DL
MNHNutrition and HealthMScE: Nutritional Epidemiology and Public Health5DL