A dataset demonstrating Simpson's Paradox with a
strongly positively correlated dataset (simpson_1
)
and a dataset with the same positive correlation as simpson_1
,
but where individual groups have a
strong negative correlation (simpson_2
).
Format
A data frame with 222 rows and 4 variables:
simpson_1_x: x-values from the
simpson_1
datasetsimpson_1_y: y-values from the
simpson_1
datasetsimpson_2_x: x-values from the
simpson_2
datasetsimpson_2_y: y-values from the
simpson_2
dataset
References
Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing. CHI 2017 Conference proceedings: ACM SIGCHI Conference on Human Factors in Computing Systems. Retrieved from https://www.research.autodesk.com/publications/same-stats-different-graphs/.#nolint
Examples
#save current settings
state = par("mar", "mfrow")
par(mfrow = c(1, 2))
plot(simpsons_paradox_wide[["simpson_1_x"]],
simpsons_paradox_wide[["simpson_1_y"]],
xlab = "x", ylab = "y", main = "Simpson's Paradox 1")
plot(simpsons_paradox_wide[["simpson_2_x"]],
simpsons_paradox_wide[["simpson_2_y"]],
xlab = "x", ylab = "y", main = "Simpson's Paradox 2")
#reset settings
par(state)