Multivariate multiple regression analysis based on principal component scores to study relationships between some pre-and post-slaughter traits of broilers;Etlik piliçlerde kesim öncesi ve kesim sonrası bazı özellikler arasındaki đliskilerin temel bilesenler skorları üzerinden uygulanan c ̧ok deǧiskenli çoklu regresyon analizi ile arastırılması

Tarim Bilimleri Dergisi

Volumn 17, Issue 1, 2011, Pages 77-83

  Mendeş, Mehmet ^a  

^a ÇANAKKALE ONSEKIZ MART UNIVERSITY   (Turkey)

Author keywords

Canonical correlation; Multivariate regression analysis; Prediction; Principal component analysis

Indexed keywords

ANIMALIA; AVES; GALLUS GALLUS;

EID: 79959597994     PISSN: 13007580     EISSN: None     Source Type: Journal    
DOI: 10.1501/tarimbil_0000001158     Document Type: Article

References (30)

1. Akbas Y & Takma Ç (2005). Canonical correlation analysis for studying the relationship between egg production traits and body weight, egg weight and age at sexual maturity in layers. Czech Journal of Animal Science 50(4): 163-168
2. Akkartal E, Mendes M & Kursun I (2009). Multivariate analysis of the relations between some blood biochemistry parameters, morphological characters and tonic immobility in broiler. Asian Journal of Chemistry 21(4):2869-2874
3. Baldava S (2004). Seismic performance prediction of steel structures using multiple intensity measures. The University of Texas at Austin. Master thesis
4. Breiman L & Friedman J H (1997). Predicting multivariate responses in multiple linear regression. Journal of the Royal Statistical Society 59(1): 3-54
5. Çamdeviren H, Demir N, Kanik A & Keskin S (2005). Use of principal component scores in multiple linear regression models for prediction of Cholophyll-a in reservoirs. Ecological Modelling 181:581-589
6. Fievez V, Vlaeminck B, Dhanoa M S & Dewhurst R J (2003). Use of principal component analysis to investigate the origin of heptadecenoic and conjugated linoleic acids in milk. Journal of Dairy Science 86: 4047-4053
7. Goldwasser M A & Fitzmaurice G M (2006). Multivariate linear regression analysis of childhood psychopathology using multiple informant data. Internatioanl Journal of Methods in Psychiatric Research 10(1):1-10
8. Hintze J (2001). NCSS and PASS Number cruncher statistical systems. Kaysville, Utah. www.NCSS.com
9. Hoe J S & Kim D S (2004). A new method of ozone forecasting using fuzzy expert and neural network systems. Science of the Total Environment 325:221-237
10. Joliffe I (2002). Principal Component Analysis, 2nd ed., Springer
11. Johnson J D (1991). Applied Multivariate Data Analysis. Springer-Verlag New York, USA
12. Johnson R A & Wichern D W (1982). Applied Multivariate Statistical Analysis (5th edition). Upper Saddle River, NJ: Prentice Hall
13. Kaiser H F (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement 20:141-151
14. Keskin S, Boysan M & Göktas I (2008). Mükemmeliyetçilik ve obsesif kompülsif semptomlar arasi{dotless} iliski için çok deǧiskenli analiz yaklasi{dotless}mi{dotless}. Türkiye Klinikleri Ti{dotless}p Bilimleri Dergisi 28:319-326
15. Khattree R & Naik D N (1999). Applied Multivariate Statistics with SAS Software. 2nd ed.Cary NC: SAS Institute Inc
16. Liu Y, Lyon B G, Windham W R, Lyon C E & Savage E M (2004). Principal component analysis of physical, color and sensory characteristic of chicken breast deboned at two, four, six and twenty-four hours postmorten. Poultry Science 83: 101-108
17. Lutz J G & Eckert T L (1994). The relationship between canonical correlation analysis and multivariate multiple regression. Educational and Psychological Measurement 54(3): 666-675
18. Mendes M, Karabayir A, Ersoy I E & Savas T (2005). The relationship among pre-and post slaughter traits of American Bronze turkey. Archives of Animal Breeding 48(3): 283-289
19. Mendes M & Akkartal E (2007). Canonical correlation analysis for studying the relationships between preand post slaughter traits of Ross 308 broiler chickens. Archive Gefligelkünde 71(6): 267-271
20. Mendes M (2009). Multiple linear regression models based on principal component scores to predict slaughter weight of broilers. Archive Gefligelkünde 73(2):139-144
21. Monge P R (1977). Multivariate multiple regression in communication research. 80p
22. Özkan M M & Mendes M (2004). Empirical table values of Eigen values for different variable numbers and sample size combinations. Pakistan Journal of Biological Sciences 7(6): 870-878
23. Posta J, Komlosi I & Mihok S (2007). Principal component analysis of performance test traits in Hungarian Sporthorse mares. Archives of Animal Breeding 50: 125-135
24. Raick C, Beckers J M, Soetaert K & Gregoire M (2006). Can principal component analysis be used to predict the dynamics of a strongly non-linear marine biogeochemical model? Ecological Modelling 196: 345-364
25. SAS (1999). SAS OnlineDoc, Version 8, Cary, NC., USA
26. Sharma S (1996). Applied multivariate techniques. John Wiley & Sons, Inc., Canada
27. Spark R S, Zucchini W & Coutsourides D (1985). On variable selection in multivariate regression. Communications in Statistics - Theory and Methods 14(7):1569-1587
28. Sousa S IV, Martins F G, Alvim-Ferraz M C M & Pereira M C (2007). Multiple linear regression and artificial neural Networks based on principal components to predict ozone concentrations. Environmental Modelling & Software 22: 97-103
29. Tabachnick B G & Fidell L S (2001). Using Multivariate Statististics. Allyn and Bacon A Pearson Education Company Boston, U.S.A
30. Thompson M L, Reynolds J, Cox L H, Guttorp P & Sampson P D (2001). A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment 35: 617-630