R Dataset / Package vcd / WeldonDice

How To Create a Barplot

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How To Create a Stacked Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Correlation Coefficient

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Cumulative Frequency Histogram

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Numerical Summaries

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Plot

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Regression

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Visual Summaries

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<iframe src="https://embed.picostat.com/r-dataset-package-vcd-weldondice.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-50076.csv 108 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the WeldonDice data set which pertains to Weldon's Dice Data. The WeldonDice data set is found in the vcd R package. You can load the WeldonDice data set in R by issuing the following command at the console data("WeldonDice"). This will load the data into a variable called WeldonDice. If R says the WeldonDice data set is not found, you can try installing the package by issuing this command install.packages("vcd") and then attempt to reload the data. 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 WeldonDice R data set. The size of this file is about 108 bytes.

Documentation

Weldon's Dice Data

Description

Data from Pearson (1900) about the frequency of 5s and 6s in throws of 12 dice. Weldon tossed the dice 26,306 times and reported his results in a letter to Francis Galton on 1894-02-02.

Usage

data("WeldonDice")

Format

A 1-way table giving the frequency of a 5 or a 6 in 26,306 throws of 12 dice where 10 indicates ‘10 or more’ 5s or 6s. The variable and its levels are

No Name Levels
1 n56 0, 1, ..., 10

Source

M. Friendly (2000), Visualizing Categorical Data, pages 20–21.

References

K. Pearson (1900), On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen by random sampling, Philosophical Magazine, 50 (5th series), 157–175.

M. Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.

Examples

data("WeldonDice")
gf <- goodfit(WeldonDice, type = "binomial")
summary(gf)
plot(gf)
--

Dataset imported from https://www.r-project.org.

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