R Dataset / Package MASS / shuttle

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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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

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Pie Chart

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Plot

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Regression

Stem and Leaf Plots

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Visual Summaries

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<iframe src="https://embed.picostat.com/r-dataset-package-mass-shuttle.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-74448.csv 11.52 KB
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 shuttle data set which pertains to Space Shuttle Autolander Problem. The shuttle data set is found in the MASS R package. You can load the shuttle data set in R by issuing the following command at the console data("shuttle"). This will load the data into a variable called shuttle. If R says the shuttle data set is not found, you can try installing the package by issuing this command install.packages("MASS") 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 shuttle R data set. The size of this file is about 11,795 bytes.

Documentation

Space Shuttle Autolander Problem

Description

The shuttle data frame has 256 rows and 7 columns. The first six columns are categorical variables giving example conditions; the seventh is the decision. The first 253 rows are the training set, the last 3 the test conditions.

Usage

shuttle

Format

This data frame contains the following factor columns:

stability

stable positioning or not (stab / xstab).

error

size of error (MM / SS / LX / XL).

sign

sign of error, positive or negative (pp / nn).

wind

wind sign (head / tail).

magn

wind strength (Light / Medium / Strong / Out of Range).

vis

visibility (yes / no).

use

use the autolander or not. (auto / noauto.)

Source

D. Michie (1989) Problems of computer-aided concept formation. In Applications of Expert Systems 2, ed. J. R. Quinlan, Turing Institute Press / Addison-Wesley, pp. 310–333.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

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

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