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hutsof99_child_witness

hutsof99_child_witness

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Author: Source: Unknown - Date unknown Please cite: Graeme D. Hutcheson and Nick Sofroniou 1999 The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models. SAGE Publications. Copyright: Graeme D. Hutcheson & Nick Sofroniou, 1999 This software can be freely used for non-commercial purposes and can be freely distributed. Readme file =========== The data sets in this directory are taken from the above book. The data are presented in two formats, *.dat (ascii) and *.por (SPSS portable). The GLIM code and macros are provided in files *.glm and *.mac. Please read the errata file which indicates some minor differences between these data sets and those reported in the book. DATA FILE SOURCE IN BOOK DESCRIPTION Chapter 1 tab1_01.* Table 1.1 Video Games and Hostility Chapter 2 tab2_01.* Table 2.1 Normal Errors tab2_02.* Table 2.2 Skewed Errors tab2_03.* Table 2.3 Curvilinearity Chapter 3 tab3_01.* Table 3.1 Two Simple Models tab3_05.* Table 3.5 Cost and Sound Quality tab3_07.* Table 3.7 Exam marks and College Offers tab3_11.* Table 3.11 Quality of Children's Testimonies Age: 5-6 = 0; 8-9 = 1 Gender: female = 0; male = 1 Location: 1 = home; 2 = school; 3 = police interview 4 = special interview tab3_11d.* Table 3.11 Data in Table 3.11 with indicator dummy codes added Chapter 4 tab4_01.* Table 4.1 Infection Severity and Treatment Outcome Treatment Outcome: 0 = survived 1 = died tab4_14.* Table 4.14 Infection severity, Treatment outcome and Hospital Attended Hospital: 1 = hospital A 2 = hospital B 3 = hospital C tab4.14d.* Table 4.14 Infection severity, Treatment outcome and Hospital Attended including dummy codes logis.* Child witness data: copy of tab3_11, but includes prosecution logis_d.* Child witness data: copy of tab3_11d, but includes prosecution logis.por and logis_d.por provide the data to obtain the parameters calculated in the book (pages 147 to 152). It should be noted that these differ slightly to the parameters obtained using the data sets logis.dat and logis_d.dat, as the *.dat files only record the variable 'coherence' to 2 decimal places. Chapter 5 tab5_01.* Table 5.1 Job Satisfaction for doctors and dentists tab5_04.* Table 5.4 Race, Housing and Illness tab5_07.* Table 5.7 Dopamine and psychosis: integer scoring tab5_08.* Table 5.8 Dopamine and psychosis: mid-ranks scoring tab5_10.* Table 5.10 Treatment and Depression: integer scoring tab5_11.* Table 5.11 Treatment and depression: mid-ranks scoring tab5_13.* Table 5.13 Alcohol consumption and Libido: integer scores tab5_16.* Table 5.16 Alcohol consumption and libido: low vs medium or high tab5_17.* Table 5.17 Alcohol consumption and libido: medium vs high Chapter 6 tab6_11.* Table 6.11 Child witness example data set File: ../data/hutsof99/tab6_11.dat Note: changes from Errata.txt where not included! Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

16 features

acc (target)numeric39 unique values
0 missing
Q8numeric15 unique values
0 missing
compnumeric39 unique values
0 missing
Q14numeric21 unique values
0 missing
Q13numeric21 unique values
0 missing
Q12numeric21 unique values
0 missing
Q11numeric22 unique values
0 missing
Q10numeric17 unique values
0 missing
Q9numeric18 unique values
0 missing
subj (row identifier)numeric42 unique values
0 missing
Q7numeric18 unique values
0 missing
Q6numeric18 unique values
0 missing
Q5numeric18 unique values
0 missing
Q4numeric21 unique values
0 missing
Q3numeric23 unique values
0 missing
Q2numeric22 unique values
0 missing
Q1numeric17 unique values
0 missing
Show all 16 features

107 properties

NumberOfInstances
42
Number of instances (rows) of the dataset.
NumberOfFeatures
16
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
16
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
AutoCorrelation
-10.58
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.38
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.09
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
69.91
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.64
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
20.66
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.57
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
9.53
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0.01
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
2.71
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.29
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
1.98
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.42
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.63
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.86
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
2.61
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.26
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.69
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.5
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
3.11
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.08
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.32
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
3.42
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.29
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.89
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Regression on hutsof99_child_witness
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: acc
Supervised Regression on hutsof99_child_witness
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: acc
Clustering on hutsof99_child_witness
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Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
Clustering on hutsof99_child_witness
0 runs - estimation_procedure: 50 times Clustering
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