Title: Assitant Professor in Applied Statistics Early life and Education : Born and Raised in Baghdad, Professor Ebtisam Abdullah exhibited a passion for mathmatics from a young age. Her academic journey began at the prestigious University of Baghdad, where she pursued a Bachelor's degree in Administration and Economics with a specialization in statistics. Academic Career: After completing her undergaduate studies, professor Ebtisam went on to earn her master's and ph.D. degrees in statistics from Univeristy of Arkansas , Little Rock. Where she emerged as promising young scholar.
Academic Credentials:
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ph.D. in statistics from a reputable institution (Univeristy of Arkansas, Little Rock) with a demonstarted experties in advanced statistical methadologies, mathmatic modeling, and data analysis.
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Strong publication record in Scholarly journals, showcasing original research contributions and intellectual leadership in the field of statistics.
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Teaching Excellence: proven ability to deliver high-quality instruction at the undergraduate and graduate levels, with a focus on critical thinking , quantitative reasoning and data literacy skills.
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Research Expertise: Expertise in specialized area of statistics , such as Bayesian inference, machine learning , time series analysis, with a strong foundation in theoratical principles and practical applications.
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Ability to mentor graduate students and postdoctoral reserachers providing guidance , support and mentorship to cultivate their research skills and scholarly potential.
Computer Science for second stage students .
Supervise in Mathmatical Statistic, linear and non linear regration, Bayesian Methods, etc.
(1)
Multiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of
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A new distribution, the Epsilon Skew Gamma (ESΓ ) distribution, which was first introduced by Abdulah [1], is used on a near Gamma data. We first redefine the ESΓ distribution, its properties, and characteristics, and then we estimate its parameters using the maximum likelihood and moment estimators. We finally use these estimators to fit the data with the ESΓ distribution
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Examination of skewness makes academics more aware of the importance of accurate statistical analysis. Undoubtedly, most phenomena contain a certain percentage of skewness which resulted to the appearance of what is -called "asymmetry" and, consequently, the importance of the skew normal family . The epsilon skew normal distribution ESN (μ, σ, ε) is one of the probability distributions which provide a more flexible model because the skewness parameter provides the possibility to fluctuate from normal to skewed distribution. Theoretically, the estimation of linear regression model parameters, with an average error value that is not zero, is considered a major challenge due to having difficulties, as no explicit formula to calcula
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