MAT-50303 R for Statistics

Course

Credits 3.00

Teaching method	Contact hours
Practical	48
Independent study	0

Course coordinator(s)	dr. ing. MPH Verouden
Lecturer(s)	dr. WT Kruijer
	dr. G Gort
	dr. ing. MPH Verouden
	dr. SK Schnabel
	AP Languillaume
	dr. EJ Bakker
	V Avagyan
Examiner(s)	dr. G Gort

Language of instruction:

English

Assumed knowledge on:

MAT-20306 Advanced Statistics or MAT-22306 Quantitative Research Methodology and Statistics or MAT-24306 Advanced Statistics for Nutritionists

Continuation courses:

ABG-30806 Modern statistics for the life sciences, MSc-thesis mathematical and statistical methods (MAT-80418 MAT-80439)

The aim of this course is to provide an introduction to R, a computer language and environment for statistics and graphics. The course will focus on getting familiar with the R environment by means of RStudio, to use R for manipulation and exploration of data and to perform all statistical analyses learned in the basic and advanced statistic courses, i.e. simple linear and multiple linear regression models, analysis of variance for CRD, RCBD and factorial designs, ANCOVA, non-parametric methods and analysis of categorical data. Basic programming as well as visualization of results in R will also be part of this course.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- read data into R from various sources;
- carry out statistical analyses as learned in previous statistical courses, with the help of the R language and environment for statistical computing and when necessary extend its basic functionality with specific packages;
- adapt and combine standard functions from basic R and packages to solve a given problem;
- adequately use standard programming constructs: loops, if-then-else statements, repetition, selection, functions, etc., to write basic program scripts to fully automate the statistical analyses;
- visualize results from statistical analyses, when possible, with the basic R graphics system;
- write reports in Rmarkdown language in which the statistical analyses and results visualization are integrated (as being part of Reproducible Research).

Activities:

Computer practicals (attendance compulsory) analysing practical data with R and R- Studio.

Examination:

Text examination: weekly assignments and an end-assignment.

Literature:

An Introduction to Statistical Methods & Data Analysis by R. Lyman Ott and Michael Longnecker (ISBN 978-1-305-26947-7: 7th edition) ; Lecture notes (WUR shop).