Data
iq_brain_size

iq_brain_size

active ARFF Publicly available Visibility: public Uploaded 29-09-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Relationship between IQ and Brain Size Summary: Monozygotic twins share numerous physical, psychological, and pathological traits. Recent advances in in vivo brain image acquisition and analysis have made it possible to determine quantitatively whether: 1) twins share neuroanatomical traits; and 2) neuroanatomical measures correlate with brain size. Using magnetic resonance imaging and computer-based image analysis techniques, measurements of the volume of the forebrain, the surface area of the cerebral cortex and the mid-sagittal area of the corpus callosum were obtained in 10 pairs of monozygotic twins. Head circumference, body weight, and Full-Scale IQ were also measured. Analyses of variance were carried out using genotype, birth order, and sex, as between-subject factors. Pearson correlation coefficients were computed to assess the interrelationships between brain measures, head circumference, and IQ. Effects of genotype (but not of birth order) were found for total forebrain volume, total cortical surface area, and callosal area. Consistent with previous twin studies, highly significant effects of genotype but not birth order were also found for head circumference, body weight, and Full-Scale IQ. The significant effect of genotype on all measures was not attributable to sex differences across unrelated twin pairs. Significant correlations were observed between forebrain volume, cortical surface area, and callosal area as well as between each brain measure and head circumference. No correlation between IQ and any other measure was found. Monozygotic twins share similarities in forebrain volume, cortical surface area, and callosal area. Brain measures are highly correlated with one another and with head circumference, but none is correlated with IQ. Authorization: Contact Authors Reference: Tramo MJ, Loftus WC, Green RL, Stukel TA, Weaver JB, Gazzaniga MS. Brain Size, Head Size, and IQ in Monozygotic Twins. Neurology 1998; 50:1246-1252. Description: This datafile contains 20 observations (10 pairs of twins) on 9 variables. This data set can be used to demonstrate simple linear regression and correlation. Variable Names in order from left to right: CCMIDSA: Corpus Collasum Surface Area (cm2) FIQ: Full-Scale IQ HC: Head Circumference (cm) ORDER: Birth Order PAIR: Pair ID (Genotype) SEX: Sex (1=Male 2=Female) TOTSA: Total Surface Area (cm2) TOTVOL: Total Brain Volume (cm3) WEIGHT: Body Weight (kg) Therese Stukel Dartmouth Hitchcock Medical Center One Medical Center Dr. Lebanon, NH 03756 e-mail: stukel@dartmouth.edu Information about the dataset CLASSTYPE: numeric CLASSINDEX: 2

9 features

FIQ (target)numeric15 unique values
0 missing
CCMIDSAnumeric18 unique values
0 missing
HCnumeric15 unique values
0 missing
ORDERnominal2 unique values
0 missing
PAIRnominal10 unique values
0 missing
SEXnominal2 unique values
0 missing
TOTSAnumeric20 unique values
0 missing
TOTVOLnumeric19 unique values
0 missing
WEIGHTnumeric18 unique values
0 missing

107 properties

20
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
0
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.
3
Number of nominal attributes.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
55.96
Mean standard deviation of attributes of the numeric type.
-0.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
89.4
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.1
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.6
Maximum kurtosis among attributes of the numeric type.
6.99
Minimum of means among attributes of the numeric type.
0.79
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1906.28
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
22.22
Percentage of binary attributes.
16.63
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.45
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
10
The maximum number of distinct values among attributes of the nominal type.
-0.47
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
0.94
Third quartile of kurtosis among attributes of the numeric type.
-7.32
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.26
Maximum skewness among attributes of the numeric type.
0.91
Minimum standard deviation of attributes of the numeric type.
66.67
Percentage of numeric attributes.
1321.03
Third quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
174.83
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
33.33
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
1.18
Third quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.02
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.76
First quartile of kurtosis among attributes of the numeric type.
137.41
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
545.69
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
43.84
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0.15
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
4.62
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.67
Average number of distinct values among the attributes of the nominal type.
1.6
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.65
Mean skewness among attributes of the numeric type.

5 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: FIQ
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: FIQ
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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