fishcatch
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Dataset:6033030
OpenML dataset with id 232
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/3669/fishcatch.arff
Upload date: 23 April 2014
Dataset Characteristics
Number of classes: 0
Number of features: 8 (numeric: 6, symbolic: 2 and in total binary: 1 )
Number of instances: 158
Number of instances with missing values: 87
Number of missing values: 87
Author: Source: Unknown - Please cite:
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Weight treated as the class attribute. Identifier deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems. Singapore: Springer-Verlag.
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NAME: fishcatch
TYPE: Sample
SIZE: 159 observations, 8 variables
DESCRIPTIVE ABSTRACT:
159 fishes of 7 species are caught and measured. Altogether there are
8 variables. All the fishes are caught from the same lake
(Laengelmavesi) near Tampere in Finland.
SOURCES:
Brofeldt, Pekka: Bidrag till kaennedom on fiskbestondet i vaera
sjoear. Laengelmaevesi. T.H.Jaervi: Finlands Fiskeriet Band 4,
Meddelanden utgivna av fiskerifoereningen i Finland.
Helsingfors 1917
VARIABLE DESCRIPTIONS:
1 Obs Observation number ranges from 1 to 159
2 Species (Numeric)
Code Finnish Swedish English Latin
1 Lahna Braxen Bream Abramis brama
2 Siika Iiden Whitewish Leusiscus idus
3 Saerki Moerten Roach Leuciscus rutilus
4 Parkki Bjoerknan ? Abramis bjrkna
5 Norssi Norssen Smelt Osmerus eperlanus
6 Hauki Jaedda Pike Esox lucius
7 Ahven Abborre Perch Perca fluviatilis
3 Weight Weight of the fish (in grams)
4 Length1 Length from the nose to the beginning of the tail (in cm)
5 Length2 Length from the nose to the notch of the tail (in cm)
6 Length3 Length from the nose to the end of the tail (in cm)
7 Height% Maximal height as % of Length3
8 Width% Maximal width as % of Length3
9 Sex 1 = male 0 = female
___/////___ _
/ ___ |
/ _ / / H
< ) __) |
/__________/ __ _
|------- L1 -------|
|------- L2 ----------|
|------- L3 ------------|
Values are aligned and delimited by blanks.
Missing values are denoted with NA.
There is one data line for each case.
SPECIAL NOTES:
I have usually calculated
Height = Height%*Length3/100
Widht = Widht%*Length3/100
PEDAGOGICAL NOTES:
I have mainly used only Species=7 (Perch) and here is some of the
models and test, we have used
Weight=a+b*(Length3*Height*Width)+epsilon
Ho: a=0;
Heteroscedastic case. Question: What is proper weighting,
if you use Length3 as a weighting variable.
Log(Weight)=a+b1*Length3+epsilon
Weight^(1/3)=a+b1*Length3+epsilon
(Given by Box-Cox-transformation)
Ho: a=0;
Log(Weight)=a+b1*Length3+b2*Height+b3*Width+epsilon
Ho: b1+b2+b3=3;
i.e. dimension of the fish = 3
Weight^(1/3)=a+b1*Length3+b2*Height+b3*Width+epsilon
(Given by Box-Cox-transformation)
Ho: a=0;
Weight=a*Length3^b1*Height^b2*Width^b3+epsilon
Nonlinear, heteroscedastic case.
What is proper weighting?
Is obs 143
143 7 840.0 32.5 35.0 37.3 30.8 20.9 0
an outlier? It had in its stomach 6 roach.
REFERENCES:
Brofeldt, Pekka: Bidrag till kaennedom on fiskbestondet i vaara
sjoear. Laengelmaevesi. T.H.Jaervi: Finlands Fiskeriet Band 4,
Meddelanden utgivna av fiskerifoereningen i Finland.
Helsingfors 1917
SUBMITTED BY:
Juha Puranen
Departement of statistics
PL33 (Aleksanterinkatu 7)
000014 University of Helsinki
Finland
e-mail: jpuranen@noppa.helsinki.fi
This page was built for dataset: fishcatch
