Statistical Programming in R

TESTING AND COURSE AIMS

 

This course has three assignments that together serve as a test to see whether:

a. Students are able to implement and use basic methods for statistical inference, as well as more advanced ones such as the bootstrap and permutation test (Applying)

b. Students have developed fundamental and computationally efficient R programming skills (Applying)

c. Students are familiar with some widely used numerical methods (Knowledge and Understanding)

d. Students are able to translate new statistical methods from the literature into a usable R program (Judgment)

e.Students develop fundamental knowledge and understanding in principles of computational statistical inference.(Knowledge and Understanding)

f. Students are capable of finding solutions to problems from the R help system, discussion fora and Google (Learning skills)

 

 

Statistical inference based on intensive computation or simulation is an important part of the armamentarium of a statistician. Computational statistics concerns the development, implementation and study of computationally intensive statistical methods. Such methods are often used e.g. in the fields of data visualization, the analysis of large datasets, Monte Carlo simulation, resampling methods such as the bootstrap, permutation methods and various numerical methods of equation solving such as the EM algorithm and Newton-Raphson iteration.
This course will present essential methods in computational statistics in a practical manner, using real-world datasets and statistical problems. In addition to a basic introduction to R, it will include evaluating and comparing the performance of different statistical techniques in a specific setting using simulation and implementing the bootstrap to e.g. obtain a standard error estimate which is not available in closed-form. We will also develop more advanced R programming skills and we will explore the use of the tidyverse environment in R.

At of the end of the course the student: