NAME: Data from the game "Pass the Pigs"
TYPE: Observational
SIZE: 6000 Observations, 5 Variables
DESCRIPTIVE ABSTRACT:
This dataset contains information collected from rolling the pair of
pigs (found in the game "Pass the Pigs") 6000 times. A description of
the rules, scoring configurations, and data collection method are
included in the accompanying paper.
BRIEF DESCRIPTION of "PASS THE PIGS":
A player rolls two pig-shaped rubber dice and earns or loses points
depending on the configuration of the rolled pigs. Players compete
individually to earn 100 points.
VARIABLE DESCRIPTIONS:
Each row of the data set represents information collected on a single
roll of the pigs. There are 6000 rows, and 6 space-delimited columns.
-COLUMN 1 (roll): The roll number.
Values are unique integers 1 through 6000.
-COLUMN 2 (black): The position number of the marked (black) pig.
Values are integers from 1 to 7:
1 = Dot Up
2 = Dot Down
3 = Trotter
4 = Razorback
5 = Snouter
6 = Leaning Jowler
7 = Pigs are touching one another
-COLUMN 3 (pink): The position number of the unmarked (pink) pig.
Values are the integers from 1 to 7 as above.
-COLUMN 4 (score): The score of the roll. All scores are face-value
from the ten integer set {-1, 0, 1, 5, 10, 15, 20, 25, 40, 60}
except the artificial score of -1. A score of -1 means the pigs
came to rest touching each another.
-COLUMN 5 (height): The height from which the pigs were rolled.
Values are the integers 1 = (5 inch) and 0 = (8 inch).
-COLUMN 6 (start): The starting position of the rolled pigs.
Values are the integers 0 = (Both pigs forward)
1 = (Both pigs backward)
2 = (One backward, one forward)
SPECIAL NOTES:
To keep track of the virtually indistinguishable pigs, we marked the
snout of one pig with a black marker. If one pig lands in position 7,
so must the other pig. This outcome ends the turn of the roller, and
sets the total score for the roller to zero. Any outcome yielding a
score of zero points ends the turn of the roller, and sets the score
earned for that turn (and only that turn) to zero.
PEDAGOGICAL NOTES:
These data provide an opportunity for students of Bayesian inference
to estimate the ten scoring probabilities using a multinomial-Dirichlet
model. Of special interest is the availability of prior information:
the accompanying scores allow for transparent prior specification (up
to a single strength-of-prior-belief parameter). The Dirichlet
posterior provides point estimates for the unknown scoring
probabilities, and allows for simulation from the posterior predictive
distribution of the next roll score. Independent realizations from
this predictive distribution are then used to examine the
effectiveness of various strategies.
Classical analyses can be employed to test for the symmetry between
the black an pink pigs, as well as for the effect of the roll height
or starting position.
SUBMITTED BY:
John C. Kern II
Duquesne University
600 Forbes Avenue
440 College Hall
Pittsburgh, PA 15282
kern@mathcs.duq.edu