R Dataset / Package boot / motor

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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Embed
<iframe src="https://embed.picostat.com/r-dataset-package-boot-motor.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-69656.csv 1.46 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 motor data set which pertains to Data from a Simulated Motorcycle Accident. The motor data set is found in the boot R package. You can load the motor data set in R by issuing the following command at the console data("motor"). This will load the data into a variable called motor. If R says the motor data set is not found, you can try installing the package by issuing this command install.packages("boot") 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 motor R data set. The size of this file is about 1,494 bytes.

Documentation

Data from a Simulated Motorcycle Accident

Description

The motor data frame has 94 rows and 4 columns. The rows are obtained by removing replicate values of time from the dataset mcycle. Two extra columns are added to allow for strata with a different residual variance in each stratum.

Usage

motor

Format

This data frame contains the following columns:

times

The time in milliseconds since impact.

accel

The recorded head acceleration (in g).

strata

A numeric column indicating to which of the three strata (numbered 1, 2 and 3) the observations belong.

v

An estimate of the residual variance for the observation. v is constant within the strata but a different estimate is used for each of the three strata.

Source

The data were obtained from

Silverman, B.W. (1985) Some aspects of the spline smoothing approach to non-parametric curve fitting. Journal of the Royal Statistical Society, B, 47, 1–52.

References

Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.

Venables, W.N. and Ripley, B.D. (1994) Modern Applied Statistics with S-Plus. Springer-Verlag.

See Also

mcycle

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

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