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• W1585616299 abstract "OZET: Ridge ve temel bilesenler regresyon analiz yontemleri, cok degiskenli regresyon verilerini analiz etmek icin kullanilan istatistik analiz yontemleridir. Coklu baglanti ortaya ciktiginda en kucuk kareler tahminleri sapmasiz olmasina karsin tahminlerin varyanslari buyuk oldugundan gercek degerlerinden oldukca uzakta olabilmektedirler. Bir derece yanli regresyon tahminlerine izin vermek suretiyle ridge ve temel bilesenler regresyon standart hatalari indirgenir. Dolayisiyla coklu baglanti durumu mevcut oldugunda en kucuk kareler metoduna alternatif olarak ridge ve temel bilesenler regresyon metotlari kullanilabilir. Bu arastirmada farkli yaslara sahip 91 adet sazan baligindan elde edilen cesitli vucut olculeri kullanilarak karkas agirligini tahminleyen bir modelin gelistirilmesi amaclanmistir. Vucut olculeri arasinda coklu baglanti durumu ortaya cikmasindan dolayi en kucuk kareler regresyonuna alternatif olan ridge ve temel bilesenler regresyon analiz yontemleri uygulanmis ve ayni veri seti icin bu uc metot karsilastirilmistir. Karsilastirma kriteri olarak belirleme katsayisi (R 2 ), hata kareler ortalamasinin karekoku (S), hata kareler ortalamasi ve modellerin varyasyon katsayisi kullanilmistir. Bu kriterlere gore, en iyi uyumu sirasiyla en kucuk kareler (R 2 =0.905, S=19.587), ridge (R 2 =0.898, S=20.2563) ve temel bilesenler regresyon (R 2 =0,878 S=22.127) metotlarinin verdigi gozlenmistir.  Sonuc olarak, coklu dogrusal baglanti durumunda en kucuk kareler metodu kullanmak yerine Ridge ve temel bilesenler regresyon yontemlerinin kullanilmasinin daha dogru olabilecegi kanaatine varilmistir. Anahtar kelimeler: Coklu dogrusal baglanti, Ridge regresyon, Temel bilesenler regresyonu, en kucuk kareler metodu ABSTRACT: Ridge and principal component regression analysis methods are statistical analysis techniques that are used to analyze multiple regression data. In the case of Multicollinearity, although Least Squares estimates are unbiased, variances of these estimates are larger and these variances can be farther than real values. With adding a degree of bias to regression estimates,  standard errors of Ridge and principal component regression are reduced. Therefore, in the event of Multicollinearity, Ridge and principal component regression methods can be used as an alternative to Least Squares method. This investigation aimed to fit a model in order to estimate carcass weight from various body measurements of 91 cyprinus fish with different ages. As Multicollinearity problems among body measurements were determined, Ridge and principal component regression methods as an alternative to Least Squares method were applied for available data, performances of these three methods for the data were compared with each other. In order to compare effectiveness of these methods, Coefficient of Determination (R 2 ), Root of Mean Square Error (RMSE), Mean Square Error (MSE), and Coefficient of Variation as comparison criteria were used. According to these criteria,  the best fit orders were observed in least squares  (R 2 =0.905, S=19.587), Ridge (R 2 =0.898, S=20.2563) and principal component regression (R 2 =0,878 S=22.127), respectively. As a result, it was concluded that, use of Ridge and principal component regression analysis methods could be truer instead of least squares method under Multicollinerity problem    Key Words: Multicollinearity, Ridge regression, principal component regression, least squares method" @default.
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• W1585616299 date "2010-01-01" @default.
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• W1585616299 title "Çoklu Doğrusal Bağlantı Durumunda Ridge ve Temel Bileşenler Regresyon Analiz Yöntemlerinin Kullanımı / Use of Ridge and Principal Component Regression Analysis Methods in Multicollinearity" @default.
• W1585616299 doi "https://doi.org/10.17097/zfd.18038" @default.
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