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A comparison between classification statistical models and neural networks with application on palestine data / Abdallah Salman Mohammed Aldirawi ; Supervised Amani Moussa Mohamed , Mahmoud A. Abdelfattah

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Abdallah Salman Mohammed Aldirawi , 2021Description: 125 Leaves : charts ; 30cmOther title:
  • مقارنة بين نماذج التصنيف الإحصائية والشبكات العصبية مع التطبيق على بيانات من فلسطين [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics Summary: This study aims at choosing the best statistical model for Labor Force data in Palestine in 2019, comparing between Multinomial Logistic Regression, Discriminant Analysis and Artificial Neural Networks. The Palestinian Labor Force data has 12 variables with manpower as the dependent variable containing three categories of Employment, Unemployment, and Outside of labor force.The other 11 are all nominal independent variables with the exception of age which is a scale variable. The results of these comparisons have shown that Multinomial Logistic Regression gave the best accuracy in prediction with (82.2%), (79.2%) for Discriminant Analysis and (81.6%) for Artificial Neural Networks. Labor Force data from a survey on Labor Force data with 9 variables have further been used, the dependent variable being nominal with two categories (Employed and Unemployed) while the other 8 independent variables are all nominal except, age variable.The results of these comparisons have shown that Artificial Neural Networks gave the best accuracy in prediction with (82.7%), (81.6%) for Logistic Regression and (79.5%) Discriminant Analysis
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.Ph.D.2021.Ab.C (Browse shelf(Opens below)) Not for loan 01010110084479000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.Ph.D.2021.Ab.C (Browse shelf(Opens below)) 84479.CD Not for loan 01020110084479000

Thesis (Ph.D.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics

This study aims at choosing the best statistical model for Labor Force data in Palestine in 2019, comparing between Multinomial Logistic Regression, Discriminant Analysis and Artificial Neural Networks. The Palestinian Labor Force data has 12 variables with manpower as the dependent variable containing three categories of Employment, Unemployment, and Outside of labor force.The other 11 are all nominal independent variables with the exception of age which is a scale variable. The results of these comparisons have shown that Multinomial Logistic Regression gave the best accuracy in prediction with (82.2%), (79.2%) for Discriminant Analysis and (81.6%) for Artificial Neural Networks. Labor Force data from a survey on Labor Force data with 9 variables have further been used, the dependent variable being nominal with two categories (Employed and Unemployed) while the other 8 independent variables are all nominal except, age variable.The results of these comparisons have shown that Artificial Neural Networks gave the best accuracy in prediction with (82.7%), (81.6%) for Logistic Regression and (79.5%) Discriminant Analysis

Issued also as CD

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