Data
PopularKids

PopularKids

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Datasets of Data And Story Library, project illustrating use of basic statistic methods, converted to arff format by Hakan Kjellerstrand. Source: TunedIT: http://tunedit.org/repo/DASL DASL file http://lib.stat.cmu.edu/DASL/Datafiles/PopularKids.html Students' Goals , What Makes Kids Popular Reference: Chase, M. A., and Dummer, G. M. (1992), "The Role of Sports as a Social Determinant for Children," Research Quarterly for Exercise and Sport, 63, 418-424 Authorization: Contact authors Description: Subjects were students in grades 4-6 from three school districts in Ingham and Clinton Counties, Michigan. Chase and Dummer stratified their sample, selecting students from urban, suburban, and rural school districts with approximately 1/3 of their sample coming from each district. Students indicated whether good grades, athletic ability, or popularity was most important to them. They also ranked four factors: grades, sports, looks, and money, in order of their importance for popularity. The questionnaire also asked for gender, grade level, and other demographic information. Number of cases: 478 Variable Names: Gender: Boy or girl Grade: 4, 5 or 6 Age: Age in years Race: White, Other Urban/Rural: Rural, Suburban, or Urban school district School: Brentwood Elementary, Brentwood Middle, Ridge, Sand, Eureka, Brown, Main, Portage, Westdale Middle Goals: Student's choice in the personal goals question where options were 1 = Make Good Grades, 2 = Be Popular, 3 = Be Good in Sports Grades: Rank of "make good grades" (1=most important for popularity, 4=least important) Sports: Rank of "being good at sports" (1=most important for popularity, 4=least important) Looks: Rank of "being handsome or pretty" (1=most important for popularity, 4=least important) Money: Rank of "having lots of money" (1=most important for popularity, 4=least important)

11 features

Goals (target)nominal3 unique values
0 missing
Gendernominal2 unique values
0 missing
Gradenumeric3 unique values
0 missing
Agenumeric6 unique values
0 missing
Racenominal2 unique values
0 missing
Urban/Ruralnominal3 unique values
0 missing
Schoolnominal9 unique values
0 missing
Gradesnumeric4 unique values
0 missing
Sportsnumeric4 unique values
0 missing
Looksnumeric4 unique values
0 missing
Moneynumeric4 unique values
0 missing

19 properties

478
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
3
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.
6
Number of numeric attributes.
5
Number of nominal attributes.
18.18
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.43
Average class difference between consecutive instances.
0
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
54.55
Percentage of numeric attributes.
51.67
Percentage of instances belonging to the most frequent class.
45.45
Percentage of nominal attributes.
247
Number of instances belonging to the most frequent class.
18.83
Percentage of instances belonging to the least frequent class.
90
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

14 tasks

405 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Goals
188 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Goals
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Goals
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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