Data
Brainsize

Brainsize

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/Brainsize.html Brain Size and Intelligence Reference: Willerman, L., Schultz, R., Rutledge, J. N., and Bigler, E. (1991), "In Vivo Brain Size and Intelligence," Intelligence, 15, 223-228. Authorization: Contact authors Description: Willerman et al. (1991) collected a sample of 40 right-handed Anglo introductory psychology students at a large southwestern university. Subjects took four subtests (Vocabulary, Similarities, Block Design, and Picture Completion) of the Wechsler (1981) Adult Intelligence Scale-Revised. The researchers used Magnetic Resonance Imaging (MRI) to determine the brain size of the subjects. Information about gender and body size (height and weight) are also included. The researchers withheld the weights of two subjects and the height of one subject for reasons of confidentiality. Number of cases: 40 Variable Names: Gender: Male or Female FSIQ: Full Scale IQ scores based on the four Wechsler (1981) subtests VIQ: Verbal IQ scores based on the four Wechsler (1981) subtests PIQ: Performance IQ scores based on the four Wechsler (1981) subtests Weight: body weight in pounds Height: height in inches MRI_Count: total pixel Count from the 18 MRI scans

7 features

MRI_Count (target)numeric40 unique values
0 missing
Gendernominal2 unique values
0 missing
FSIQnumeric26 unique values
0 missing
VIQnumeric20 unique values
0 missing
PIQnumeric21 unique values
0 missing
Weightnumeric32 unique values
2 missing
Heightnumeric23 unique values
1 missing

19 properties

40
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
3
Number of missing values in the dataset.
2
Number of instances with at least one value missing.
6
Number of numeric attributes.
1
Number of nominal attributes.
14.29
Percentage of binary attributes.
5
Percentage of instances having missing values.
-86499.85
Average class difference between consecutive instances.
1.07
Percentage of missing values.
0.18
Number of attributes divided by the number of instances.
85.71
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
14.29
Percentage of nominal attributes.
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.
1
Number of binary attributes.

13 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: MRI_Count
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: MRI_Count
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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