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unknown (from Weka repository)

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arff slurped Weka

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Floating Point

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# Instances: 13 / # Attributes: 14
HDF5 (12.2 KB) XML CSV ARFF LibSVM Matlab Octave

Original Data Format

arff

Name

detroit

Version mldata

0

Comment

Data from StatLib (ftp stat.cmu.edu/datasets)

This is the data set called DETROIT' in the bookSubset selection in regression' by Alan J. Miller published in the Chapman & Hall series of monographs on Statistics & Applied Probability, no. 40. The data are unusual in that a subset of three predictors can be found which gives a very much better fit to the data than the subsets found from the Efroymson stepwise algorithm, or from forward selection or backward elimination.

The original data were given in appendix A of `Regression analysis and its application: A data-oriented approach' by Gunst & Mason, Statistics textbooks and monographs no. 24, Marcel Dekker. It has caused problems because some copies of the Gunst & Mason book do not contain all of the data, and because Miller does not say which variables he used as predictors and which is the dependent variable. (HOM was the dependent variable, and the predictors were FTP ... WE)

The data were collected by J.C. Fisher and used in his paper: "Homicide in Detroit: The Role of Firearms", Criminology, vol.14, 387-400 (1976)

The data are on the homicide rate in Detroit for the years 1961-1973. FTP - Full-time police per 100,000 population UEMP - % unemployed in the population MAN - number of manufacturing workers in thousands LIC - Number of handgun licences per 100,000 population GR - Number of handgun registrations per 100,000 population CLEAR - % homicides cleared by arrests WM - Number of white males in the population NMAN - Number of non-manufacturing workers in thousands GOV - Number of government workers in thousands HE - Average hourly earnings WE - Average weekly earnings

HOM - Number of homicides per 100,000 of population ACC - Death rate in accidents per 100,000 population ASR - Number of assaults per 100,000 population

N.B. Each case takes two lines.

Names

FTP,UEMP,MAN,LIC,GR,CLEAR,WM,NMAN,GOV,HE,

Types

1. numeric
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric
7. numeric
8. numeric
9. numeric
10. numeric

Data (first 10 data points)

FTP	UEMP	MAN	LIC	GR	CLEAR	WM	NMAN	GOV	HE	...
260.35	11.0	455.5	178.15	215.98	93.4	5587...	538.1	133.9	2.98	...
269.8	7.0	480.2	156.41	180.48	88.5	5385...	547.6	137.6	3.09	...
272.04	5.2	506.1	198.02	209.57	94.4	5191...	562.8	143.6	3.23	...
272.96	4.3	535.8	222.1	231.67	92.0	5004...	591.0	150.3	3.33	...
272.51	3.5	576.0	301.92	297.65	91.0	4824...	626.1	164.3	3.46	...
261.34	3.2	601.7	391.22	367.62	87.4	4650...	659.8	179.5	3.6	...
268.89	4.1	577.3	665.56	616.54	88.3	4482...	686.2	187.5	3.73	...
295.99	3.9	596.9	1131.2	1029.7	86.1	4321...	699.6	195.4	2.91	...
319.87	3.6	613.5	837.6	786.23	79.0	4165...	729.9	210.3	4.25	...
341.43	7.1	569.3	794.9	713.77	73.9	4015...	757.8	223.8	4.47	...
...	...	...	...	...	...	...	...	...	...	...

Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

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Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/

Measurement Details

Usage Scenario

revision 1

by mldata on 2010-11-06 09:59

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