# R Dataset / Package boot / gravity

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## Visual Summaries

Embed
<iframe src="https://embed.picostat.com/r-dataset-package-boot-gravity.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
585 bytes
GNU General Public License v2.0
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the gravity data set which pertains to Acceleration Due to Gravity. The gravity data set is found in the boot R package. You can load the gravity data set in R by issuing the following command at the console data("gravity"). This will load the data into a variable called gravity. If R says the gravity 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 gravity R data set. The size of this file is about 585 bytes.

Documentation

## Acceleration Due to Gravity

### Description

The gravity data frame has 81 rows and 2 columns.

The grav data set has 26 rows and 2 columns.

Between May 1934 and July 1935, the National Bureau of Standards in Washington D.C. conducted a series of experiments to estimate the acceleration due to gravity, g, at Washington. Each experiment produced a number of replicate estimates of g using the same methodology. Although the basic method remained the same for all experiments, that of the reversible pendulum, there were changes in configuration.

The gravity data frame contains the data from all eight experiments. The grav data frame contains the data from the experiments 7 and 8. The data are expressed as deviations from 980.000 in centimetres per second squared.

### Usage

gravity


### Format

This data frame contains the following columns:

g

The deviation of the estimate from 980.000 centimetres per second squared.

series

A factor describing from which experiment the estimate was derived.

### Source

The data were obtained from

Cressie, N. (1982) Playing safe with misweighted means. Journal of the American Statistical Association, 77, 754–759.

### 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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