View uci-20070111 sensory (public)
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- Integer,Floating Point
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- Original Data Format
- arff
- Name
- sensory
- Version mldata
- 0
- Comment
Data from StatLib (ftp stat.cmu.edu/datasets)
Data for the sensory evaluation experiment in Brien, C.J. and Payne, R.W. (1996) Tiers, structure formulae and the analysis of complicated experiments. submitted for publication.
The experiment involved two phases. In the field phase a viticultural experiment was conducted to investigate the differences between 4 types of trellising and 2 methods of pruning. The design was a split-plot design in which the trellis types were assigned to the main plots using two adjacent Youden squares of 3 rows and 4 columns. Each main plot was split into two subplots (or halfplots) and the methods of pruning assigned at random independently to the two halfplots in each main plot. The produce of each halfplot was made into a wine so that there were 24 wines altogether.
The second phase was an evaluation phase in which the produce from the halplots was evaluated by 6 judges all of whom took part in 24 sittings. In the first 12 sittings the judges evaluated the wines made from the halfplots of one square; the final 12 sittings were to evaluate the wines from the other square. At each sitting, each judge assessed two glasses of wine from each of the halplots of one of the main plots. The main plots allocated to the judges at each sitting were determined as follows. For the allocation of rows, each occasion was subdivided into 3 intervals of 4 consecutive sittings. During each interval, each judge examined plots from one particular row, these being determined using two 3x3 Latin squares for each occasion, one for judges 1-3 and the other for judges 4-6. At each sitting judges 1-3 examined wines from one particular column and judges 4-6 examined wines from another column. The columns were randomized to the 2 sets of judges x 3 intervals x 4 sittings using duplicates of a balanced incomplete block design for v=4 and k=2 that were latinized. This balanced incomplete block design consists of three sets of 2 blocks, each set containing the 4 "treatments". For each interval, a different set of 2 blocks was taken and each block assigned to two sittings, but with the columns within the block placed in reverse order in one sitting compared to the other sitting. Thus, in each interval, a judge would evaluate a wine from each of the 4 columns.
The scores assigned in evaluating the wines, and the factors indexing them, are given below. The factors are as follows: Occasion Judges Interval Sittings Position Squares Rows Columns Halfplot Trellis Method followed by the response variable Score
The scores are ordered so that the factors Occasion, Judges, Interval, Sittings and Position are in standard order; the remaining factors are in randomized order.
- Names
- Occasion,Judges,Interval,Sittings,Position,Squares,Rows,Columns,Halfplot,Trellis,
- Types
- nominal:1,2
- nominal:1,2,3,4,5,6
- nominal:1,2,3
- nominal:1,2,3,4
- nominal:1,2,3,4
- nominal:1,2
- nominal:1,2,3
- nominal:1,2,3,4
- nominal:1,2
- nominal:1,2,3,4
- Data (first 10 data points)
Occa... Judges Inte... Sitt... Posi... Squa... Rows Colu... Half... Trel... ... 1 1 1 1 1 1 3 3 1 2 ... 1 1 1 1 2 1 3 3 2 2 ... 1 1 1 1 3 1 3 3 1 2 ... 1 1 1 1 4 1 3 3 2 2 ... 1 1 1 2 1 1 3 1 1 3 ... 1 1 1 2 2 1 3 1 2 3 ... 1 1 1 2 3 1 3 1 2 3 ... 1 1 1 2 4 1 3 1 1 3 ... 1 1 1 3 1 1 3 2 2 1 ... 1 1 1 3 2 1 3 2 1 1 ... ... ... ... ... ... ... ... ... ... ... ...
- Description
A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)
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- Data Source
- http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/
- Measurement Details
- Usage Scenario
- revision 1
- by mldata on 2011-09-14 15:25
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Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.