R Dataset / Package HistData / Jevons
| Attachment | Size |
|---|---|
| dataset-16016.csv | 542 bytes |
| Documentation |
|---|
On this Picostat.com statistics page, you will find information about the Jevons data set which pertains to W. Stanley Jevons' data on numerical discrimination. The Jevons data set is found in the HistData R package. Try to load the Jevons data set in R by issuing the following command at the console data("Jevons"). This may load the data into a variable called Jevons. If R says the Jevons data set is not found, you can try installing the package by issuing this command install.packages("HistData") and then attempt to reload the data with library("HistData") followed by data("Jevons"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing Jevons at the command-line which should display the entire dataset. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the Jevons R data set. The size of this file is about 542 bytes. W. Stanley Jevons' data on numerical discriminationDescriptionIn a remarkable brief note in Nature, 1871, W. Stanley Jevons described the results of an experiment he had conducted on himself to determine the limits of the number of objects an observer could comprehend immediately without counting them. This was an important philosophical question: How many objects can the mind embrace at once? He carried out 1027 trials in which he tossed an "uncertain number" of uniform black beans into a box and immediately attempted to estimate the number "without the least hesitation". His questions, procedure and analysis anticipated by 75 years one of the most influential papers in modern cognitive psychology by George Miller (1956), "The magical number 7 plus or minus 2: Some limits on ..." For Jevons, the magical number was 4.5, representing an empirical law of complete accuracy. Usagedata(Jevons) FormatA frequency data frame with 50 observations on the following 4 variables.
DetailsThe original data were presented in a two-way, 13 x 13 frequency table,
SourceJevons, W. S. (1871). The Power of Numerical Discrimination, Nature, 1871, III (281-282) ReferencesMiller, G. A. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information, Psychological Review, 63, 81-97, http://www.musanim.com/miller1956/ Examples
data(Jevons)
# show as tables
xtabs(frequency ~ estimated+actual, data=Jevons)
xtabs(frequency ~ error+actual, data=Jevons)# show as sunflowerplot with regression line
with(Jevons, sunflowerplot(actual, estimated, frequency,
main="Jevons data on numerical estimation"))
Jmod <-lm(estimated ~ actual, data=Jevons, weights=frequency)
abline(Jmod)# show as balloonplots
if (require(gplots)) {with(Jevons, balloonplot(actual, estimated, frequency, xlab="actual", ylab="estimated",
main="Jevons data on numerical estimation\nBubble area proportional to frequency",
text.size=0.8))with(Jevons, balloonplot(actual, error, frequency, xlab="actual", ylab="error",
main="Jevons data on numerical estimation: Errors\nBubble area proportional to frequency",
text.size=0.8))
}# plot average error
if(require(reshape)) {
unJevons <- untable(Jevons, Jevons$frequency)
str(unJevons)require(plyr)
mean_error <- function(df) mean(df$error, na.rm=TRUE)
Jmean <- ddply(unJevons, .(actual), mean_error)
with(Jmean, plot(actual, V1, ylab='Mean error', xlab='Actual number', type='b', main='Jevons data'))
abline(h=0)
}
-- Dataset imported from https://www.r-project.org. |
| Picostat Manual |
|---|
How To Register With a Username
How To Register With Google Single Sign On (SSO)
How To Login With a Username and Password
How To Login With Google Single Sign On (SSO)
How To Import a Dataset
How To Perform Statistical Analysis with Picostat
How To Use Educational Applications with Picostat
|
| Recent Queries For This Dataset |
|---|
|
No queries made on this dataset yet. |
| Title | Authored on | Content type |
|---|---|---|
| R Dataset / Package MASS / Pima.tr2 | March 9, 2018 - 1:06 PM | Dataset |
| OpenIntro Statistics Dataset - ipod | August 9, 2020 - 12:25 PM | Dataset |
| R Dataset / Package boot / frets | March 9, 2018 - 1:06 PM | Dataset |
| R Dataset / Package HSAUR / CYGOB1 | March 9, 2018 - 1:06 PM | Dataset |
| OpenIntro Statistics Dataset - COL | August 9, 2020 - 2:30 PM | Dataset |