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chscase_health

chscase_health

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: File README ----------- chscase A collection of the data sets used in the book "A Casebook for a First Course in Statistics and Data Analysis," by Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff, John Wiley and Sons, New York, 1995. Submitted by Samprit Chatterjee (schatterjee@stern.nyu.edu), Mark Handcock (mhandcock@stern.nyu.edu) and Jeff Simonoff (jsimonoff@stern.nyu.edu) This submission consists of 38 files, plus this README file. Each file represents a data set analyzed in the book. The names of the files correspond to the names used in the book. The data files are written in plain ASCII (character) text. Missing values are represented by "M" in all data files. More information about the data sets and the book can be obtained via gopher at the address swis.stern.nyu.edu The information is filed under ---> Academic Departments & Research Centers ---> Statistics and Operations Research ---> Publications ---> A Casebook for a First Course in Statistics and Data Analysis ---> Welcome! It can also be accessed from the World Wide Web (WWW) using a WWW browser (e.g., netscape) starting from the URL address http://www.stern.nyu.edu/SOR/Casebook NOTICE: These datasets may be used freely for scientific, educational and/or non-commercial purposes, provided suitable acknowledgment is given (by citing the Chatterjee, Handcock and Simonoff reference above). File: health.dat Note: attribute names were generated automatically since there was no information in the data itself. Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

3 features

col_1 (ignore)nominal50 unique values
0 missing
col_2nominal9 unique values
0 missing
col_3numeric9 unique values
0 missing
col_4numeric49 unique values
0 missing
col_5 (ignore)numeric30 unique values
0 missing

19 properties

50
Number of instances (rows) of the dataset.
3
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
2
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
Average class difference between consecutive instances.
66.67
Percentage of numeric attributes.
0.06
Number of attributes divided by the number of instances.
33.33
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

11 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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