In an effort to increase student retention, many colleges have tried block programs. Suppose 100 students are broken into two groups of 50 at random. One half are in a block program, the other half not. The number of years in attendance is then measured. We wish to test if the block program makes a difference in retention. The data is:
Program | 1yr | 2yr | 3yr | 4yr | 5+yrs |
------- | --- | --- | --- | --- | ----- |
Non-Block | 18 | 15 | 5 | 8 | 4 |
Block | 10 | 5 | 7 | 18 | 10 |
A fish survey is done to see if the proportion of fish types is consistent with previous years. Suppose, the 3 types of fish recorded: parrotfish, grouper, tang are historically in a 5:3:4 proportion and in a survey the following counts are found:
Fish | pf | gr | ta |
----- | -- | -- | -- |
observed | 53 | 22 | 49 |
The R dataset UCBAdmissions contains data on admission to UC Berkeley by gender. We wish to investigate if the distribution of males admitted is similar to that of females.
To do so, we need to first do some spade work as the data set is presented in a complex contingency table. The ftable() (flatten table) command is needed. To use it try
data(UCBAdmissions) # read in the dataset
x = ftable(UCBAdmissions) # flatten
x # what is there## Dept A B C D E F
## Admit Gender
## Admitted Male 512 353 120 138 53 22
## Female 89 17 202 131 94 24
## Rejected Male 313 207 205 279 138 351
## Female 19 8 391 244 299 317We want to compare rows 1 and 2. Treating x as a matrix, we can access these with x[1:2,].
An exit poll by a news station of 900 people in the state of Florida found 440 voting for Bush and 460 voting for Gore.
Load the dataset blood (below).
blood <- structure(list(Machine = c(68, 82, 94, 106, 92, 80, 76, 74, 110, 93,
86, 65, 74, 84, 100), Expert = c(72, 84, 89, 100, 97, 88, 84, 70, 103, 84,
86, 63, 69, 87, 93)), .Names = c("Machine", "Expert"), row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15"),
class = "data.frame")