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Teaching Assistan Evaluation (E vs NE) data
The data set (and description) can be downloaded here:
http://archive.ics.uci.edu/ml/machine-learning-databases/tae/tae.data
Description:
1. Title: Teaching Assistant Evaluation
2. Sources:
(a) Collector: Wei-Yin Loh (Department of Statistics, UW-Madison)
(b) Donor: Tjen-Sien Lim (limt@stat.wisc.edu)
(b) Date: June 7, 1997
3. Past Usage:
1. Loh, W.-Y. & Shih, Y.-S. (1997). Split Selection Methods for
Classification Trees, Statistica Sinica 7: 815-840.
2. Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of
Prediction Accuracy, Complexity, and Training Time of
Thirty-three Old and New Classification Algorithms. Machine
Learning. Forthcoming.
(ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or
(http://www.stat.wisc.edu/~limt/mach1317.pdf)
4. Relevant Information:
The data consist of evaluations of teaching performance over three
regular semesters and two summer semesters of 151 teaching assistant
(TA) assignments at the Statistics Department of the University of
Wisconsin-Madison. The scores were divided into 3 roughly equal-sized
categories ("low", "medium", and "high") to form the class variable.
5. Number of Instances: 151
6. Number of Attributes: 6 (including the class attribute)
7. Attribute Information:
1. Whether of not the TA is a native English speaker (binary)
1=English speaker, 2=non-English speaker
2. Course instructor (categorical, 25 categories)
3. Course (categorical, 26 categories)
4. Summer or regular semester (binary) 1=Summer, 2=Regular
5. Class size (numerical)
6. Class attribute (categorical) 1=Low, 2=Medium, 3=High
8. Missing Attribute Values: None
Citation Request:
Please refer to the repository http://archive.ics.uci.edu/ml (see citation policy).
See also Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
Descriptive statistics:
Dataset= tae : n= 151 , d= 5
Class1: n= 29
Covariance matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 39.1946 -6.9532 -0.7771 14.1626 0.4002
[2,] -6.9532 34.1478 1.0197 -4.4039 -1.1872
[3,] -0.7771 1.0197 0.2217 2.9557 0.0012
[4,] 14.1626 -4.4039 2.9557 192.9089 2.0998
[5,] 0.4002 -1.1872 0.0012 2.0998 0.6133
Correlation matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000 -0.1901 -0.2636 0.1629 0.0816
[2,] -0.1901 1.0000 0.3706 -0.0543 -0.2594
[3,] -0.2636 0.3706 1.0000 0.4520 0.0033
[4,] 0.1629 -0.0543 0.4520 1.0000 0.1930
[5,] 0.0816 -0.2594 0.0033 0.1930 1.0000
Median: 17.1619 6.4604 1.687 29.9024 2.4378
Mean: 17.1379 6.1724 1.6897 31.8621 2.4483
MCD-estimated:
MDC-0.975-Mean: 19.5556 3 1.5 30.3333 2.5556
MDC-0.750-Mean: 19.5556 3 1.5 30.3333 2.5556
MDC-0.500-Mean: 19.5556 3 1.5 30.3333 2.5556
Class2: n= 122
Covariance matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 45.0633 -10.4132 -0.1706 -11.3214 -0.0156
[2,] -10.4132 52.1486 0.3795 -0.4491 1.5426
[3,] -0.1706 0.3795 0.1024 1.0566 -0.0839
[4,] -11.3214 -0.4491 1.0566 156.7205 -1.4613
[5,] -0.0156 1.5426 -0.0839 -1.4613 0.6379
Correlation matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000 -0.2148 -0.0794 -0.1347 -0.0029
[2,] -0.2148 1.0000 0.1642 -0.0050 0.2675
[3,] -0.0794 0.1642 1.0000 0.2637 -0.3281
[4,] -0.1347 -0.0050 0.2637 1.0000 -0.1462
[5,] -0.0029 0.2675 -0.3281 -0.1462 1.0000
Median: 12.4517 7.6282 1.882 25.6576 1.9322
Mean: 12.8115 8.5656 1.8852 26.918 1.918
MCD-estimated:
MDC-0.975-Mean: 12.6204 8.9907 2 28.1019 1.8241
MDC-0.750-Mean: 12.6204 8.9907 2 28.1019 1.8241
MDC-0.500-Mean: 12.6204 8.9907 2 28.1019 1.8241
Measures:
Mah.Dist: 1.1803
Mah.Dist-MCD-0.975: 2.0298
Mah.Dist-MCD-0.750: 2.0298
Mah.Dist-MCD-0.500: 2.0298
Zuletzt geändert am 17.02.2013
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