ANALYSIS FOR COUNT AND CATEGORICAL DATA USING REGRESSION MODELS

Authors

  • Taghreed Abdul-Razek A lecturer of Statistics at AL AZHAR University, Faculty of Commerce, Department of Statistics, Cairo, Egypt
  • Abdul-Motaleb Al-Said, Ph.D Assistant Professor of Statistics at King Abdul-Aziz University, Faculty of Science, Department of Statistics

DOI:

https://doi.org/10.53555/eijms.v5i1.40

Abstract

Counts are non-negative integers. It represents the number of occurrences of an event within a fixed period. Measurements scales of categorical variable consist of two main types of measurement scales, ordered scales that named ordinal variables and unordered scales that named nominal variables. Regression models are the most frequently used statistical models for analyzing count data such as Poisson and negative binomial regression models; logistic regression models are used with binary and categorical variables. The main goals of this research are considering these models along estimating the parameters of them, discuss the proper model of each type of data, and make a comparison between models using suitable statistical programs for analyzing the two data sets.

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Published

2016-06-27

Issue

Vol. 2 No. 1 (2016): EPH - International Journal of Mathematics and Statistics (ISSN: 2208-2212)

Section

Articles

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Copyright (c) 2016 International Journal of Mathematics and Statistics

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