R Dataset / Package boot / tuna

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

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Visual Summaries

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Embed
<iframe src="https://embed.picostat.com/r-dataset-package-boot-tuna.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-20702.csv 323 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 tuna data set which pertains to Tuna Sighting Data. The tuna data set is found in the boot R package. You can load the tuna data set in R by issuing the following command at the console data("tuna"). This will load the data into a variable called tuna. If R says the tuna 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 tuna R data set. The size of this file is about 323 bytes.

Documentation

Tuna Sighting Data

Description

The tuna data frame has 64 rows and 1 columns.

The data come from an aerial line transect survey of Southern Bluefin Tuna in the Great Australian Bight. An aircraft with two spotters on board flies randomly allocated line transects. Each school of tuna sighted is counted and its perpendicular distance from the transect measured. The survey was conducted in summer when tuna tend to stay on the surface.

Usage

tuna

Format

This data frame contains the following column:

y

The perpendicular distance, in miles, from the transect for 64 independent sightings of tuna schools.

Source

The data were obtained from

Chen, S.X. (1996) Empirical likelihood confidence intervals for nonparametric density estimation. Biometrika, 83, 329–341.

References

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

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

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