does not ensure that a reasonable answer can be extracted
from a given body of data” – John W. Tukey (1915-2000)
Why should I take this course?
To develop advanced skills in designing experiments, defining and testing hypotheses, and performing statistical data analyses within your field of interest;
If I take it, what will I learn?
By the end of this course, you should be able to:
- Design experiments related to your work;
- Perform the appropriate statistical analyses of the data obtained from the experiment;
- Develop sound conclusions based on the available data;
- Identify the main limitations of your experiments, and suggest improvements;
- Perform critical interpretations of other experimental methodologies and results reported in the literature.
How is this course organized?
The course activities are divided into four main types:
- Lectures (approx. 8);
- Computational case studies (4 or 5);
- Final written exam;
- Final project presentation.
This is a course on applied experimental design and analysis. As such, a large portion of the course is dedicated to case studies in which you will design experiments, collect (simulated) data, perform inference and report on your analysis.
It is strongly reccomended that you come into this course having at least moderate fluency in R. There are a few very good introductory courses you can take for free, such as R Programming or the courses from Data Camp.
Which materials do you use in this course?
The main reference are my Lecture Notes, which are available on GitHub. Douglas Montgomery’s two books, Applied Statistics and Probability for Engineers (written with George Runger) and Design and Analysis of Experiments are also used as references, and I draw heavily from Michael Crawley’s excellent The R Book and from Paul Matthew’s Sample Size Calculations: Practical Methods for Engineers and Scientists. Selected papers from the stats literature, blog posts, and online materials are also used.
Can I audit this course?
If you have any other doubts, please feel free to e-mail me.
Department of Electrical Engineering
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil