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Search Result For 'Regression Analysis' , Result Number : 3
Staff Name
Research Area
Mazen Mohamed Abd ulrahman
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Regression Analysis
Ahmed Mohamed Mohamed Sedki Khalifa
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
Regression Analysis
Fatma Desouky Mohamed Abdallah
Faculty Research Area On Zu Site
Faculty Research Area On Staff Site
International Journal of Statistics and Applications 2017, 7(3): 192-195 DOI: 10.5923/j.statistics.20170703.05 Statistical Assessment of Some Factors Affecting Calving Interval by Using Ordinal Logistic Regression in Holstein Cows Fatma D. M. Abdallah1, Eman A. Abo Elfadl2,* 1Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt 2Department of Animal Husbandry and Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Egypt Abstract Background & objectives: Calving interval considered an important trait throughout Holstein dairy cow's life. There are many risk factors which have a great impact on it. In the line with this consideration, the purpose of this study is to apply ordinal logistic regression model to estimate the effect of these risk factors on calving interval. Methods: Ordinal logistic regression analysis was used to estimate the odds ratio (OR) and probability of Holstein dairy cows conception for 3400 lactation records from Dina farms company, Egypt. The data was collected over a period extended from 1998 to 2010. The logit link function was used to predict the probability of occurrence of pregnancy using SPSS version 20.0, USA. Results: The odds ratio showed that the likelihood of pregnancy in cows with different parities was 0.931, 0.787, 0.634 and 1.000 for lactation order 2, 3, 4 and 5 respectively. Odds ratio of pregnancy of cows calving in winter were higher than those calving in summer, it was 1.234 and 1.000 respectively. Odds of different periods of days open were 0.586, 0.771, 0.638 and 1.000 respectively. Odds of different periods of dry period were 0.378, 0.525, 0.545 and 1.000 respectively. Conclusions: Findings showed fitting of this model to the data, it also showed the ability of ordinal logistic regression to provide measure which facilitates understanding of the important risk factors affecting calving interval.
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