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ITS » Master Theses » Statistika - S2
Posted by budi at 19/01/2007 09:04:58  •  20010 Views


PENGGUNAAN KRITERIA INFORMATION COMPLEXITY ICOMP UNTUK MODEL REGRESI

INFORMATIONAL COMPLEXITY CRITERIA FOR REGRESSION MODELS

Author :
CHAMID, NOER  




ABSTRAK

Salah satu metode dalam pemilihan model regresi terbaik adalah All Possible Regression. Kriteria seleksi yang digumakan dalam metode All Possible Regression ada tiga yaitu Koefisien Determinasi R2 Kuadrat Tengah Galat s2 dan Cp-mallow. Disampmg itu ada beberapa kriteria seleksi yang lain dalam pemilihan model regresi terbaik yaitu Akaikes Information Criteria AIC Bayesian Information Criteria BIC dan Information Complexity ICOMP. Kriteria seleksi AIC BIC dan ICOMP itu akan digunakan pada data hasil panen padi di Kabupaten Gresik tahun 2004. Ketiga kriteria seleksi ini memberikan hasil yang relatif sama.


ABSTRACT

Alt Possible Regression is a procedure to choose the best regression model. Selection criteria which used in All Possible Regression method are coefficient of determination R2 mean square error s2 and Cp statistic. Besides that there are several other procedures selection criteria to choose the best regression model that are Akaikes Information Criteria AIC Bayesian Information Criteria BRZ and Information Complexity RZOMP. Those procedures will be used at the product agriculture data in Kabupaten Gresik in 2004 and given the same result.



KeywordsAll Possible Regression; AIC; BIC; ICOMP
 
Subject:  analisis regresi
Contributor
  1. Drs. I Nyoman Latra, MS
    Dra. Kartika Fitriasari, M.Si
Date Create: 19/01/2007
Type: Text
Format: pdf; 41 pages
Language: Indonesian
Identifier: ITS-Master-3100006026458
Collection ID: 3100006026458
Call Number: 519.536 Cha p


Source
Theses Statistic RTSt 519.536 Cha p,2005

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