R Dataset / Package HistData / Jevons

Webform
Category

Webform
Category

Webform
Category

Webform
Category

Webform
Category

Webform
Category

Visual Summaries

Embed
<iframe src="https://embed.picostat.com/r-dataset-package-histdata-jevons.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
542 bytes
Documentation

W. Stanley Jevons' data on numerical discrimination

Description

In 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.

Usage

data(Jevons)

Format

A frequency data frame with 50 observations on the following 4 variables.

actual

Actual number: a numeric vector

estimated

Estimated number: a numeric vector

frequency

Frequency of this combination of (actual, estimated): a numeric vector

error

actual-estimated: a numeric vector

Details

The original data were presented in a two-way, 13 x 13 frequency table, estimated (3:15) x actual (3:15).

Source

Jevons, W. S. (1871). The Power of Numerical Discrimination, Nature, 1871, III (281-282)

References

Miller, 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
1. Go to the user registration page.
4. Click Submit.
5. Click the link that was sent to the email address you registered with.
6. Clicking the link will open another page on Picostat where you can select a password.
7. Click Save and enter any profile details you wish to enter.
How To Register With Google Single Sign On (SSO)
1. Go to the user login page.
5. Google will redirect you back to Picostat with your new account created and you will be logged in.
6. Enter any profile details you wish to share.
1. Go to the user login page.
3. Click "Login". You will be redirected to your user homepage authenticated.
1. Go to the user login page.
3. If you already registered with Picostat via Google SSO, you will be redirected to your user homepage authenticated.
How To Import a Dataset
1. Create a Picostat account or login with your existing picostat account (see above).
2. Go to the dataset import page.
3. Select a license for the dataset. The default is "No License" but allows Picostat to host a copy of the dataset as per the privacy policy. You may wish to uncheck the "Public" option if you do not wish to share your dataset with others. R Datasets that come by downloading R have a GNU General Public License v3.0 which may also be selected from the Picostat dropdown.
4. Enter a title for the dataset
5. Choose a dataset input methods. Available options include:
• Random data - this populates your dataset with random numbers between 0 and 100. You can specify the number of rows and columns for the random dataset.
• CSV, TSV or TXT file - you will have the option upload a file within the current file size limit and also specify the header and whether or not the dataset is a contingency table. With contingency tables, the first column becomes a label for the rows. Currently with Picostat, there is limited support for contingency tables. Choose "Yes" to the Header option if the first line of the data contains titles for the rows. Also choose the Separator for the dataset. A separator is what breaks the data up. In some cases, a comma would separate data values in a row. You will also have the option to add documentation in the form keyboarded text and also uploaded documentation attachments. You can also specify a license for the documentation.
• Copy and Paste. This selection contains many of the same fields as importing a file with an additional textarea to copy and paste data to.
• Empty dataset. Start with a blank dataset and manually add data with the Picostat dataset editor.
• Excel file - Choose this option if you would like to convert your Excel spreadsheet to a Picostat dataset. With this selection, you will have the option to specify whether to use the first row in the Excel file as column names. If you would like to choose a specific sheet to use, you can also specify with entering its name in the text input.
• sas7bdat file - SAS is a powerful statistical software package that has its own proprietary file format. Choose this option if you are importing a SAS file.
• SPSS sav file - SPSS is a statistical package owned by IBM. You can import SPSS files by choosing this option.
6. Choose whether or not the dataset contains a header. Some of the dataset input methods allow you to specify whether or not a Header exists on the file. Sometimes dataset files contain a Header as the first row which names the columns. If you choose "Yes" to this, the first row in the dataset will become column headers.
7. You can also add documentation and specify a documentation license. This can be used to help explain your dataset to those unfamiliar with it.
8. Choose whether or not to upload an supporting attachments.
9. Pass the captcha. To prevent spam submissions, Picostat has a captcha which is used to prevent automated submissions by bots.
10. Choose a privacy setting for the dataset. You can also specify whether or not the dataset is Public. If you uncheck this setting, only you and the Picostat administrator will be able to view the dataset.
11. Submit the form. Once the form is validated, you will be redirected to the dataset homepage where you can choose to edit or perform statistical operations on the dataset.
How To Perform Statistical Analysis with Picostat
1. Go to any dataset homepage. You can get a full list at the dashboard.
2. Near the top of the page there will be two drop downs. One for analysis and one for education. Here we will choose Analyis. Choose from one of the following:
• Numerical Summaries - Here you can get the:
1. Arithmetic mean
2. Median
3. Quartiles
4. Minimum and Maximum
5. Stem-and-leaf plot
6. Standard deviation and Variance
7. IQR
8. Cumulative frequencies
• Plot - a plot of two columns on the cartesian coordinate system
• Boxplot - a Boxplot (box-and-whisker plot) of a column.
• Correlation Coefficient - Compute the correlation coefficient between two columns.
• Cumulative Frequency Histogram - Display a cumulative frequency histogram
• Dotplot
• Hollow Histogram - Plot two columns on the same histogram with a different color for each column.
• Pie Chart
• Regression - Perform a simple linear regression and compute the p-value and regression line. Also plots the data with the regression line.
• Stem and Leaf Plots - Plot a one or two-sided stem-and-leaf plot from one or two columns respectively.
• Visual Summaries - plots the following:
1. Frequency Histogram
2. Relative Frequency Histogram
3. Cumulative Frequency Histogram
4. Boxplot (Box-and-whisker plot)
5. Dotplot
3. PDF - Check this box if you want to download a PDF of the output from the statistical analysis application.
4. HTML - Check this box if you want a link to the HTML page created with the application.
5. R File - Check this box if you want a link to download the R commands used to generate output from this statistical application. You should be able to copy the commands verbatim into R Studio or R to recreate the analysis.
How To Use Educational Applications with Picostat
1. Go to any dataset homepage. You can get a full list at the dashboard.
2. Near the top of the page there will be two drop downs. One for analysis and one for education. Here we will choose Education. Choose from one of the following:
• How To Create a Barplot - This will show you how to create a bar chart after selecting a column with the mouse.
• How To Create a Stacked Barplot - This application will show you how to create a stacked bar plot from a column vector.
• How To Create a Pie Chart - This application will show you how to create a pie chart from a column of data
• How To Compute the Mean - This application will show you how to compute the mean from a column vector
• How To Create a Plot - This app will show you how to plot two columns in the cartesian coordinate system.
• How To Compute the Media - This statisistical app will show you how to compute the median from a column vector.
3. PDF - Check this box if you want to download a PDF of the output from the education application.
4. HTML - Check this box if you want a link to the HTML page created with the application.
Recent Queries For This Dataset

No queries made on this dataset yet.

Title Authored on Content type
R Dataset / Package wooldridge / mroz March 9, 2018 - 1:06 PM Dataset
R Dataset / Package wooldridge / campus March 9, 2018 - 1:06 PM Dataset
R Dataset / Package Stat2Data / Diamonds March 9, 2018 - 1:06 PM Dataset
R Dataset / Package MASS / chem March 9, 2018 - 1:06 PM Dataset
R Dataset / Package cluster / votes.repub March 9, 2018 - 1:06 PM Dataset