Master in Statistics

Faculty of Science - Department of Statistics

b

Description

The Program Description 

This program grants a high degree (Master's) degree in statistics through the study of a number of general, specialized and supportive courses, and after passing them, the research project is presented in one of the topics of statistics supported by statistics and results.

Objectives

The Programs Targets

1- Preparing researchers with theoretical and applied scientific and technical knowledge. 

2- Preparing researchers with the necessary methodologies to analyze data for the public and private sectors.

3- Preparing statistical cadres to help in university education.

4- Carrying out research and studies that contribute to solving the various problems of society and pampering difficulties in all fields.

5- Establishing a scientific and methodological base for research and development in the field of statistics and related fields.

Outcomes

The Program's outputs

(a) Knowledge and understanding

 

Understand and apply basic concepts and scientific terminology in statistics.

a1

Building cadres capable of collecting and analyzing various data and measuring them in terms of quality..

a2

The ability to build research cadres for university teaching in the field of statistics.

a3

Understand information systems and documentation.

a4

Ability to establish a scientific base for research and development in the field of statistics.

a5

 

 

(b) Mental skills

To help student of having appropriate statistical skills:

 

Ability to solve problems related to data analysis.

b1

The ability to implement technical consultations for most sectors.

b2

Ability to choose the appropriate statistical test to find solutions to issues and questions.

b3

Understand information and documentation systems.

b4

The ability to develop oneself theoretically and practically in the field of statistics.

b5

 

 

(c) Practical and professional skills

After the completion of the course a student supposed to be able to:

 

Use appropriate methods and data appropriateness in an accurate manner.

c1

Analyzing, classifying and extracting information from data

c2

Design, build and analyze statistical models in terms of their advantages and disadvantages.

c3

The ability to compare between the statistical methods used.

c4

Ability to deal with theoretical and practical systems in the field of statistics.

c5 

 

 

(d) General and transferred skills.

Work within a team.

d1

Acquire the skill of written and oral communication through the preparation and delivery of research papers.

d2

Finding data and information from various sources and tabulating them.

d3

Use appropriate methods for statistical analysis methods.

D4

Finding theoretical and practical solutions and their applications in the field of statistics.

d5

 

Certificate Rewarded

Masters degree

Entry Reuirements

The Program Conditions for Applicants

The applicant must study in the following program:

1- The applicant must have obtained a bachelor's degree from one of the recognized educational institutions.

2- The applicant must successfully pass the entrance exam prepared by the department.

3- His admission should not conflict with the terms of the admission regulations for postgraduate studies at the university.

4- The applicant must have studied and successfully passed the introductory courses (Perroquets ) or equivalent.

Study Plan

The Master in Statistics prepares students to qualify for Master in Statistics. The student studies several subjects which have been carefully chosen in this major to cover its different aspects.

It comprises 8 Semesters of study, in which the student will study a total of 36 units, which include 0 units of general subjects, and 0 major units

Study plan for this program is shown below:

1st Semester

Code Title Credits Course Type Prerequisite
MA601 03 Compulsory +

The overall aim of the course is to give a good understanding of · Complex Numbers, variables and functions (revision). · Cauchy-Riemann equations, complex integrals, power series and its convergence.· Cauchy’s theorem and integrals formula, Taylor’s theorem and Laurent expansion, Residues and Jordan’s theorems, evaluating (improper) real integrals.· Integrals transforms of Laplace, Fourier, Mellin, Complex Fourier and their inversion integrals.· Generating and characteristic Functions, complex approach to obtain the distribution of sum, differenceand product of continuous independent random variables.

ST601 03 Compulsory +

: The main purposesof this course are: · Making an individual understanding to the basic theoretical knowledge aboutfundamental principles of the estimation theory and its techniques. · To identify a good estimator. · To learn the concept of the Uniformly Minimum Variance Unbiased Estimators (UMVUE). · To establish various properties of likelihood estimators. · Understanding the concept of statistical estimation under Bayesian framework.

ST606 03 Elective +

· The concept of Bayesian decision making , Expected loss and decision rules (nonrandomized and randomized). · The Decision principles (conditional Bayes, frequents), inference as decision problem and optimal decision rules. · The Bayes and minimax decision rule and admissibility of minimax. Conjugate prior families and hierarchical priors. · The Parametric Empirical Bayes, Posterior distribution, Loss function and squared error loss. The precautionary loss and LINEX loss and Bayes HPD confidence intervals

2nd Semester

Code Title Credits Course Type Prerequisite
ST602 03 Compulsory +

· Theconstruction of Most Powerful (MP) tests and UniformlyMost Powerful (UMP) tests. · IdentifyingtheUniformly Most Powerful Unbiased (UMPU) tests and Locally best tests. · The obtaining of the power and power function of the test. · The concept of Generalized Likelihood Ratio Test (GLRT) and Sequential Probability Ratio Test (SPRT) . · Uniformly most accurate one-sided confidence Interval and its relation to UMP tests for one-sided null against one-sided alternative hypotheses. The construction of shortest expected length confidence interval.

ST608 03 Compulsory +

· To introduce the concept of a time series, and discuss a range of descriptive methods for identifying features of interest; · To present a range of approaches for representing trends and seasonality in a time series, and to assess their relative merits; · To describe the theoretical properties of commonly used time series models; · To describe a range of approaches for predicting future values of a time series;To show how to apply the techniques from the course to real time series data sets in the statistical package R.

ST609 03 Elective +

1- To introduce branch which is an integration of mathematics, statistics, and economics used to deal with econometric models. 2- To provide the students with some useful tools for his/her future research. 3- . To help the student to develop a way of thinking in quantitative terms. 4- To help the students understanding simultaneous Equation Models

3rd Semester

Code Title Credits Course Type Prerequisite
ST603 03 Compulsory +

· To provide an understanding of the basic concepts in probability theory · Characteristic functions and their properties · Types of convergence and the limit of probability distributions.

ST607 03 Compulsory ST603 +

4th Semester

Code Title Credits Course Type Prerequisite
ST612 03 Elective +

5th Semester

Code Title Credits Course Type Prerequisite
ST610 03 Elective +

ST699 06 Compulsory +