‰HDF  ÿÿÿÿÿÿÿÿVÿÿÿÿÿÿÿÿ`ˆ¨ xTREEÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿXHEAPX Èdatadata_descr8ˆ¨ 0name 'fishcatch' (mldata0 ðcommentÊ !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 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. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 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. 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