skulls: Correlations between length and breadth of skulls, by sex
In jrnold/datums: Miscellaneous Data Sets
Description
Usage
Format
Details
Source
References
Description
The data consist of the correlations between the length and
breadth of skulls in the Paris Catacombs gathered by
C. D. Fawcett, though neither a citation nor the raw data is provided in the article.
Usage
1
Format
A data frame with 3 rows and 4 columns.
name type description
sex "character" Either "male", "female", or "total"
n "integer" Number of observations
correlation "numeric" Pearson's correlation coefficient
probable_error "numeric" The probable error of the correlation coefficient, which is half the interquartile range.
Details
This is a dataset mentioned by Pearson as an example of the phenomena now known
as Simpson's Paradox and what he calls "spurious correlation":
“We are thus forced to the conclusion that a mixture of heterogeneous groups,
each of which exhibits in itself no organic correlation, will exhibit a greater or less
amount of correlation. This correlation may properly be called spurious, yet as it
is almost impossible to guarantee the absolute homogeneity of any community, our
results for correlation are always liable to an error, the amount of which cannot be
foretold. To those who persist in looking upon all correlation as cause and effect,
the fact that correlation can be produced between two quite uncorrelated characters
A and B by taking an artificial mixture of two closely allied races, must come rather
as a shock.” (Pearson )
The probable error of the correlation coefficient is
PE = 0.6745 (1 - r^2 / sqrt(n))
.
Source
Pearson, K. and Lee, A. and Bramley-Moore, L. (1899)
“Mathematical Contributions to the Theory of Evolution. VI.
Genetic (Reproductive) Selection: Inheritance of Fertility in Man, and of Fecundity in Thoroughbred Racehorses.”
Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, http://www.jstor.org/stable/90782.
References
Stigler, S. M. (2016) The Seven Pillars of Statistical Wisdom, p. 142
jrnold/datums documentation built on May 20, 2019, 1 a.m.
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Description Usage Format Details Source References
Description
The data consist of the correlations between the length and breadth of skulls in the Paris Catacombs gathered by C. D. Fawcett, though neither a citation nor the raw data is provided in the article.
Usage
1 |
Format
A data frame with 3 rows and 4 columns.
| name | type | description |
sex | "character" | Either "male", "female", or "total" |
n | "integer" | Number of observations |
correlation | "numeric" | Pearson's correlation coefficient |
probable_error | "numeric" | The probable error of the correlation coefficient, which is half the interquartile range. |
Details
This is a dataset mentioned by Pearson as an example of the phenomena now known as Simpson's Paradox and what he calls "spurious correlation":
“We are thus forced to the conclusion that a mixture of heterogeneous groups, each of which exhibits in itself no organic correlation, will exhibit a greater or less amount of correlation. This correlation may properly be called spurious, yet as it is almost impossible to guarantee the absolute homogeneity of any community, our results for correlation are always liable to an error, the amount of which cannot be foretold. To those who persist in looking upon all correlation as cause and effect, the fact that correlation can be produced between two quite uncorrelated characters A and B by taking an artificial mixture of two closely allied races, must come rather as a shock.” (Pearson )
The probable error of the correlation coefficient is
PE = 0.6745 (1 - r^2 / sqrt(n))
.
Source
Pearson, K. and Lee, A. and Bramley-Moore, L. (1899) “Mathematical Contributions to the Theory of Evolution. VI. Genetic (Reproductive) Selection: Inheritance of Fertility in Man, and of Fecundity in Thoroughbred Racehorses.” Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, http://www.jstor.org/stable/90782.
References
Stigler, S. M. (2016) The Seven Pillars of Statistical Wisdom, p. 142
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