Statistical methodology based on ranks, empirical distributions, and runs. One and two sample tests, ANOVA, correlation, goodness of fit, rank regression, \(R\)-estimates and confidence intervals. Comparisons to classical parametric methods will be made throughout. There will be an emphasis on assumptions and interpretation and on computational details and implementation.
We will be using statistical software in this class. Whereas historically a class like this would involve looking up statistical quantiles in the back of a text book, we will be calculating those values ourselves using software. You are welcome to use the software of your choice, but class demonstrations will be in R. All help with software in office hours will be limited to R. Please install R and R Studio as soon as possible.
Grading will nominally follow the typical breakdown on a total percentage scale, e.g., [93-100 A), [90-93 A-), [87-90 B+), [83-87 B), etc. All grades in Canvas will follow this scheme. However the instructor reserves the right to apply a final curve in the students’ favor.
.Rmd file (in addition to the PDF) for extra credit.The Virginia Tech Honor Code will be strictly enforced in this course. All graded assignments must be composed of your own work.
Any student who feels that he or she may need an accommodation because of a disability (learning disability, attention deficit disorder, psychological, physical, etc.), please make an appointment to see me during office hours.