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Search Result For 'Statistics' , Result Number : 45

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Mazen Mohamed Abd ulrahman
- Faculty Research Area On Zu Site
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Computational Statistics
Metwally AlAwadi Elsayed AlAwadi
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Order Statistics
Metwally AlAwadi Elsayed AlAwadi
- Faculty Research Area On Zu Site
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Dual Generalized Order Statistics
Metwally AlAwadi Elsayed AlAwadi
- Faculty Research Area On Zu Site
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Generalized Order Statistics
Metwally AlAwadi Elsayed AlAwadi
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Order Statistics
Sameh mohamed abd monem ahmed
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Geostatistics
Halaa Ismail Mahmoud Hessien
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Biostatistics
Hoda Abdelhamied Abdulwahab
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Applied Statistics in Sports
Mohammed Afifi Yousef Afifi
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Biostatistics
Osama Elsayed Eraky Mohamed Abokhasem
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Order Statistics
Hatem Elsayed Abdelwahied Semary
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Applied Statistics
Hatem Elsayed Abdelwahied Semary
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Biostatistics
Mohamed Elsayed Ahmed Mead
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Applied Statistics
Mohamed Elsayed Ahmed Mead
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Mathematical Statistics
Mohamed Salah Ahmed Hasanien
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Mathematical Statistics
Mahasen Mahmoud Helmy Khater
- Faculty Research Area On Zu Site
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STATISTICS
Mohsen Mohamed Mohamed Fuad
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Analytics & Statistics for Enterprise Engineering
Mohamed Mostafa Abdelhamed Mohamed Elewa
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Educational Statistics
Mostafa Abdelsalam Musstafa
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statistics
Ibrahim Mohamed Alie
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Calf health, Epidemiology, Biostatistics and Animal Infectious diseases
Asmaa Taha Yassin Ali Feshawy
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statistics
Shebl Ebrahim Shebl Ali Salem
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Biostatistics
Fatma Desouky Mohamed Abdallah
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Applied Mathematics 2019, 9(1): 1-5 DOI: 10.5923/j.am.20190901.01 Role of Time Series Analysis in Forecasting Egg Production Depending on ARIMA Model Fatma D. M. Abdallah Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt Abstract The goal of this study is to show the role of time series models in predicting process and to demonstrate the suitable type of it according to the data under study. Autoregressive integrated moving averages (ARIMA) model is used as a common and a more applicable model. Univariate ARIMA model is used here to forecast egg production in some layers depending on daily data from the period of May to October 2018. Different criteria of the ARIMA model can be used to choose the suitable one such as the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute relative percentage error (MARPE). Depending on these measures the autoregressive integrated moving average model with ordering (2,2,1) is considered the best model for forecasting process. The model fit statistics such as RMSE (331.520) which was low and the lowest BIC value (11.745) indicating that the model fit the data well. The high value of R2 (0.95) and MAPE (4.542) indicated a perfect forecasting model. Also, ARIMA model with ordering (1,2,2) is good in prediction process.
Fatma Desouky Mohamed Abdallah
- Faculty Research Area On Zu Site
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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
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International Journal of Statistics and Applications 2018, 8(3): 129-132 DOI: 10.5923/j.statistics.20180803.03 Parametric Survival Models for Predicting of Pregnancy in Friesian Cattle 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: This work was performed to apply different parametric survival models instead of nonparametric ones for predicting pregnancy in Friesian dairy cattle by using days open as a time variable. The models used were exponential, normal, log-normal, Weibull, logistic, log logistic and smallest extreme value. Data for present study were obtained from animal records belongs to different Dakhalia farms (n = 1842) Covering the period between 2009 and 2011. Variables included in this study were days open as a dependent variable and the independent variables were: age at calving, dry period, calving interval, season, and lactation order. The survival time data were modeled by using life data regression procedure of Statgraphics statistical package program. Model parameters were estimated and comparisons among models were done based on Akaike Information Criterion (AIC). The Weibull model was the best option for evaluating time till occurrence of pregnancy where it had the smallest (AIC) value. It also showed a good fit with the studied data. This Weibull survival model can be used to predict the length of time from calving to conception in Friesian. But the Logistic was not appropriate to describe the dataset
Fatma Desouky Mohamed Abdallah
- Faculty Research Area On Zu Site
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Statistical Modelling of Categorical Outcome with More than Two Nominal Categories Fatma D.M. Abdallah* Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt *Corresponding author: Nour_stat2013@yahoo.com Received August 13, 2018; Revised October 04, 2018; Accepted December 04, 2018 Abstract This paper aims to explain and apply an important statistical method used for modelling categorical outcome variable with at least two unordered categories. Logistic regression model especially multinomial logistic type (MNL) model is the best choice to model unordered qualitative data. A simulation study was done to examine the efficiency of the model in representing categorical response variable. Three explanatory variables (age, species, and sex) are used for discrimination. While the outcome variable was Rose Bengal Plate Test (RBPT) results which has four outcome categories (negative, positive, false positive, and false negative). Therefore, logit model will be utilized to model this data. MNL models were fitted using SPSS packages and parameters estimated depending on maximum likelihood (MLE) by the Newton-Raphson algorithm. This model depends mainly on two estimates to interpret the results, they are the regression coefficient and the exponentiated coefficients which known as the odds ratio. This model was a good fitted for description the data of 500 values of Rose Bengal Plate Test results of Brucella in sheep and goat species. The results showed fitting of the model to the data with highly significant likelihood ratio statistic for the overall model (P value = 0.000**). Wald test was significant for all variables in positive category and this indicated that age, species and sex are good predictors for test results. The odds ratio in case of positive category for age, species and sex was 1.589, 0.214 and 0.133 respectively. Keywords: multinomial logistic regression, odds ratio, Rose Bengal Plate Test (RBPT), maximum likelihood and pseudo R2 Cite This Article: Fatma D.M. Abdallah, “Statistical Modelling of Categorical Outcome with More than Two Nominal Categories.” American Journal of Applied Mathematics and Statistics, vol. 6, no. 6 (2018): 262-265. doi: 10.12691/ajams-6-6-7.
Fatma Desouky Mohamed Abdallah
- Faculty Research Area On Zu Site
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Using Discriminant Analysis and Artificial Neural Network Models for Classification and Prediction of Fertility Status of Friesian Cattle Eman A. Abo Elfadl1,*, Fatma D. M. Abdallah2 1Department of Animal Husbandry and Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Egypt 2Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt *Corresponding author: emmy_f1984@yahoo.com Abstract Background & objectives: This study was undertaken to compare the accuracies of Discriminant analysis model (DA) and Artificial neural networks model (ANN) for classification and prediction of Friesian cattle fertility status by using its reproductive traits. Methods: Data was collected through field survey of 2843 animal records of Friesian breed belongs to El Dakhalia province farms, Egypt. Data was covering the period extended from 2010 to 2013. The samples of dairy production sectors were selected randomly. Data was collected from valid farm records or the structured questionnaires established by the researcher. Results: The results of classification accuracy indicated that the artificial neural network (ANN) model is more efficient than the discriminant analysis (DA) model in expressing overall classification accuracy and accuracies of correctly classified cases of fertility status for Friesian cattle. The results showed that The ANN models had shown the highest classification accuracy (93.6%) for year (2010) while, it was (79.9%) for DA. The comparison of overall classification accuracies clearly favored the supremacy of ANN over DA. The results also were confirmed by the areas under Receiver Operating Characteristic Curves (ROC) captured by ANN and DA. ROC curves are used mainly for comparing different discriminating rates. Areas under ROC curves were higher in case of ANN models across the different years compared to DA models. The differences in accuracies were also significant at 5% level of significance with p-value 0.005 by using Paired Sample t-test. From all of the above we can conclude that artificial neural network model was more accurate in prediction and classification of fertility status than a traditional statistical model (Discriminant analysis). Keywords: artificial neural networks, discriminant analysis, prediction, classification, ROC curve and fertility status Cite This Article: Eman A. Abo Elfadl, and Fatma D. M. Abdallah, “Using Discriminant Analysis and Artificial Neural Network Models for Classification and Prediction of Fertility Status of Friesian Cattle.” American Journal of Applied Mathematics and Statistics, vol. 5, no. 3 (2017): 90-94. doi: 10.12691/ajams-5-3-1.
Khaled Youssef Kamal Mostafa
- Faculty Research Area On Zu Site
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Biostatistics
Ola Alsayed Sayed Ahmed Nafea
- Faculty Research Area On Zu Site
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Biostatistics
Elsayed Mahsoub Ahmed Nigm
- Faculty Research Area On Zu Site
- Faculty Research Area On Staff Site
On the continuation of the limit distribution of intermediate order statistics under power normalization
Rasha AbdelWahab Atwa
- Faculty Research Area On Zu Site
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Statistics
Nahla Said Abdelrahman Abdrabou Elsayed
- Faculty Research Area On Zu Site
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Non-Parametric Statistics
Nahla Said Abdelrahman Abdrabou Elsayed
- Faculty Research Area On Zu Site
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Statistics
Noha Mohamed Saeed
- Faculty Research Area On Zu Site
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biostatistics
Haroun Mohammed Abdel-Fattah Barakat
- Faculty Research Area On Zu Site
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generalized order statistics
Haroun Mohammed Abdel-Fattah Barakat
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order statistics
Halle Mahmoud Ahmed Khaleel
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Mathematical Statistics
Islam Abdallah Heusseiny Metwally
- Faculty Research Area On Zu Site
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concomitants of order statistics
Islam Abdallah Heusseiny Metwally
- Faculty Research Area On Zu Site
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Generalized order statistics
Islam Abdallah Heusseiny Metwally
- Faculty Research Area On Zu Site
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Order statistics
- Faculty Research Area On Zu Site
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Biostatistics
yousef Ahmed Elayman
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Medical statistics
- Faculty Research Area On Zu Site
Concomitants of order statistics
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Generalized Order Statistics
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Order Statistics