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Search Result For 'Probability' , Result Number : 6

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Osama Elsayed Eraky Mohamed Abokhasem
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
Probability
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.
Fatma Desouky Mohamed Abdallah
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
Zagazig Veterinary Journal, ©Faculty of Veterinay Medicine, Zagazig University, 44511, Egypt. Volume 47, Number 2, p. XXX, June 2019 DOI: 10.21608/zvjz.2019.11121.1034 RSEARCH ARTICLE Application of Different Biostatistical Methods in Biological Data Analysis Khairy M. El-Bayomi, Fatma D. Mohamed, Mahmoud S. El-Tarabany and Hagar F. Gouda* Animal Wealth Development Department, Faculty of Veterinary Medicine, Zagazig University, 44511, Egypt Abstract Logistic regression is one of the popular methods used in genetic data analysis. That is applied to predict a categorical binary dependent variable on basis of predictor variables, and to test the probability of getting a particular value of the dependent variable that is related to the explanatory variable. The objective of this study is to highlight the crucial role of biostatistical methods in increasing the accuracy of the results in veterinary and biological practices. Statistical analysis of previously published data in the National Research Center, Dokki, Giza, Cairo, Egypt was done using SPSS version, 24 to predict hepatocellular carcinoma metastasis by knowing the genotypes, age, and gender of the patients. The genotypes and gender displayed a significant effect on metastasis (P < 0.05) while age had no significant effect on metastasis (P > 0.05). There are other types of data (animal breeding and production) which were analyzed by repeated measures ANOVA and principal component analysis (PCA). The repeated measures ANOVA is equivalent to normalized ANOVA, but for related, not independent groups. Data of this test was obtained from a study aimed to measure body weight of three breeds of rabbits at 4 time points 4th, 6th, 8th and 10th weeks of the experiment. The main effect of breed types of rabbits was significant (P < 0.05), the time (weeks) was highly significant (P < 0.001) and their interaction was also highly significant (P < 0.001). Principal component analysis (PCA) is used to reduce a large set of variables to a small set that still contains most of the information in the large set. A reduced set is easier to analyze and interpret. Data with 6 variables reduced to only 2 variables where initial eigenvalues were > 1 for two variables and their values were (2.768 and 1.147).
Nahla Said Abdelrahman Abdrabou Elsayed
- Faculty Research Area On Zu Site
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Probability Measure
Nahla Said Abdelrahman Abdrabou Elsayed
- Faculty Research Area On Zu Site
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Probability Theory
Mohamed Salah Abdel-Moneim Gharib
- Faculty Research Area On Zu Site
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Utility of Atherogenic lndex of Plasma in Predicting Plaque Burden ln Patients with Ghest Pain and lntermediate Pretest Probability of Goronary Artery Disease