View statlib-20050214 cloud (public)
























- Summary
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- License
- unknown (from Weka repository)
- Dependencies
- Tags
- arff slurped Weka
- Attribute Types
- Integer,Floating Point,String
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# Instances: 108 / # Attributes: 8
HDF5 (23.7 KB) XML CSV ARFF LibSVM Matlab Octave
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- Original Data Format
- arff
- Name
- cloud
- Version mldata
- 0
- Comment
These data are those collected in a cloud-seeding experiment in Tasmania between mid-1964 and January 1971. Their analysis, using regression techniques and permutation tests, is discussed in:
Miller, A.J., Shaw, D.E., Veitch, L.G. & Smith, E.J. (1979). `Analyzing the results of a cloud-seeding experiment in Tasmania', Communications in Statistics - Theory & Methods, vol.A8(10), 1017-1047.
The rainfalls are period rainfalls in inches. TE and TW are the east and west target areas respectively, while NC, SC and NWC are the corresponding rainfalls in the north, south and north-west control areas respectively. S = seeded, U = unseeded.
Information about the dataset CLASSTYPE: nominal CLASSINDEX: 3
- Names
- PERIOD,SEEDED,SEASON,TE,TW,NC,SC,NWC,
- Types
- numeric
- nominal:seeded,unseeded
- nominal:AUTUMN,SPRING,SUMMER,WINTER
- numeric
- numeric
- numeric
- numeric
- numeric
- Data (first 10 data points)
PERIOD SEEDED SEASON TE TW NC SC NWC 1 seeded AUTUMN 1.69 3.73 1.65 1.8 3.33 2 unse... AUTUMN 0.74 0.78 1.09 0.79 1.59 3 seeded WINTER 0.81 0.86 2.39 0.36 2.06 4 unse... WINTER 1.44 2.01 2.96 1.27 4.05 5 seeded WINTER 2.48 4.61 4.16 2.16 6.0 6 unse... WINTER 0.84 2.39 2.76 0.87 4.17 7 unse... WINTER 0.37 1.37 1.08 0.85 3.45 8 seeded WINTER 0.37 0.84 0.26 0.47 0.9 9 unse... SPRING 1.33 2.31 2.53 1.08 3.65 10 seeded SPRING 3.38 5.56 2.76 3.1 5.06 ... ... ... ... ... ... ... ...
- Description
A gzip'ed tar containing StatLib datasets (statlib-20050214.tar.gz, 12,785,582 Bytes)
- URLs
- (No information yet)
- Publications
- Data Source
- http://lib.stat.cmu.edu/datasets/
- Measurement Details
- Usage Scenario
- revision 1
- by mldata on 2011-09-14 14:45
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Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.