Data

bolts

active
ARFF
Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn

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Data from StatLib (ftp stat.cmu.edu/datasets)
SUMMARY:
Data from an experiment on the affects of machine adjustments on
the time to count bolts. Data appear as the STATS (Issue 10) Challenge.
DATA:
Submitted by W. Robert Stephenson, Iowa State University
email: wrstephe@iastate.edu
A manufacturer of automotive accessories provides hardware, e.g. nuts,
bolts, washers and screws, to fasten the accessory to the car or truck.
Hardware is counted and packaged automatically. Specifically, bolts
are dumped into a large metal dish. A plate that forms the bottom of
the dish rotates counterclockwise. This rotation forces bolts to the
outside of the dish and up along a narrow ledge. Due to the vibration
of the dish caused by the spinning bottom plate, some bolts fall off
the ledge and back into the dish. The ledge spirals up to a point
where the bolts are allowed to drop into a pan on a conveyor belt.
As a bolt drops, it passes by an electronic eye that counts it. When
the electronic counter reaches the preset number of bolts, the
rotation is stopped and the conveyor belt is moved forward.
There are several adjustments on the machine that affect its operation.
These include; a speed setting that controls the speed of rotation
(SPEED1) of the plate at the bottom of the dish, a total number of
bolts (TOTAL) to be counted, a second speed setting (SPEED2) that is
used to change the speed of rotation (usually slowing it down) for the
last few bolts, the number of bolts to be counted at this second speed
(NUMBER2), and the sensitivity of the electronic eye (SENS). The
sensitivity setting is to insure that the correct number of bolts are
counted. Too few bolts packaged causes customer complaints. Too many
bolts packaged increases costs. For each run conducted in this
experiment the correct number of bolts was counted. From an
engineering standpoint if the correct number of bolts is counted, the
sensitivity should not affect the time to count bolts. The measured
response is the time (TIME), in seconds, it takes to count the desired
number of bolts. In order to put times on a equal footing the
response to be analyzed is the time to count 20 bolts (T20BOLT).
Below are the data for 40 combinations of settings. RUN is the order
in which the data were collected.
Analyze the data. What adjustments have the greatest effect on the
time to count 20 bolts? How would you adjust the machine to get
the shortest time to count 20 bolts? Are there any unusual features
to the data?
The data description and data may be freely used for non-commercial
purposes and can be freely distributed. Copyright remains with the
author and STATS Magazine.

T20BOLT (target) | numeric | 40 unique values 0 missing | |

RUN (row identifier) | numeric | 40 unique values 0 missing | |

SPEED1 | numeric | 3 unique values 0 missing | |

TOTAL | numeric | 3 unique values 0 missing | |

SPEED2 | numeric | 3 unique values 0 missing | |

NUMBER2 | numeric | 3 unique values 0 missing | |

SENS | numeric | 4 unique values 0 missing | |

TIME | numeric | 40 unique values 0 missing |

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

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

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

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

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

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

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

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

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

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

Maximum mutual information between the nominal attributes and the target attribute.

The maximum number of distinct values among attributes of the nominal type.

Average mutual information between the nominal attributes and the target attribute.

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

Average number of distinct values among the attributes of the nominal type.

Minimal mutual information between the nominal attributes and the target attribute.

The minimal number of distinct values among attributes of the nominal type.

-1.82

First quartile of kurtosis among attributes of the numeric type.

First quartile of mutual information between the nominal attributes and the target attribute.

0.91

First quartile of standard deviation of attributes of the numeric type.

-1.82

Second quartile (Median) of kurtosis among attributes of the numeric type.

7

Second quartile (Median) of means among attributes of the numeric type.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0

Second quartile (Median) of skewness among attributes of the numeric type.

2.9

Second quartile (Median) of standard deviation of attributes of the numeric type.

0.52

Third quartile of kurtosis among attributes of the numeric type.

Third quartile of mutual information between the nominal attributes and the target attribute.

0.83

Third quartile of skewness among attributes of the numeric type.

27.41

Third quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

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.RandomTree -depth 1

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Standard deviation of the number of distinct values among attributes of the nominal type.