Non Category

1.1.            ORIGIN AND DEVELOPMENT OF O.R.

To first view, Operations research (or the operational research) is a recent technique, dating at most of World War II. And, in fact, it is well to its application to the military operations that it owes its name. 

But really, it is a lot older. Since the XVIIs Century, Blaise Pascal and Pierre Fermat, inventors of the notion of expectation of a random variable (1654), looked for, followed from Jacques Bernoulli, to solve problems of decision in the uncertain situation. 

This module introduces to the students about the role and significance of accounting information and the fundamentals and  basic accounting procedures, accounting principles, accounting concepts, assumptions and conventions, accounting cycle, which are involved in the production of a business entity’s financial statements and enabling the students to understand the basic language of the business, accounting as a science in addition to gain knowledge of recording of business transactions, measurement of business performance and the assessment of financial position of the organizations. 

This module covers the application of many kinds of mathematical methods to improve the efficiency of processes and the management of complex systems. A range of techniques associated with mathematical models constructed to describe and solve problems is considered. It will cover two broadly types of processes, namely, discrete and continuous stochastic processes.

At the end of Statistics for Economists, students should be able to

• distinguish between a population and a sample

• understand the concept of a sampling distribution

• apply the sampling distribution of the single sample mean and a single proportion

• understand where the t distribution should be used

• apply the sampling distribution of the difference between two sample means

1. Statistics for Economists based on Inferential Statistics: generalizing from samples to populations using probabilities performing hypothesis testing, determining relationships between variables, and making predictions. 

2. Inferential statistics is a process of describing the population based on the sample results (i.e: it consists of techniques for reaching conclusions about a population based upon information contained in a sample). 

3. We use inferential statistics to try to infer from the sample data what the population might think (i.e. to make inferences from our data to more general conditions).

4. Inferential statistics uses patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation), modeling relationships within the data (regression), extrapolation, interpolation, or other modeling techniques like ANOVA, time series, and data mining.

The Module Description

The course of study and Research Methods introduces the student to academic studies by teaching him/her the organization of the material to learn, the time management, the note taking a and the use of the public library, the choice of a topic and definition of the research problem, the hypothesis variables, the phases of a research process, the sampling techniques, data collection techniques, results, presentation and discussion techniques and styles for presenting the references.

This module first introduces simultaneous equations models and other components of the module are qualitative response regression models, nonlinear regression models and introduction to time series econometrics. The computer is a fundamental tool in this module and students will be required to be familiar with econometric package (E-Views, State,...)

-enables students to understand, describe and analyze sexual and reproductive health issues and related health promotion programs in low and middle income countries

-providesasoundfoundationinthetheoryabout local and global challenges relating to the social, political and economic context influencing sexual and reproductive health

The module aims to provide students with important skills which are of both academic and vocational value, being an essential part of the training of an economic statistician scientist and also useful for a career. By the end of the module students should have competencies in the following: An awareness of the empirical approach to economic and social science; review and extend fundamental statistical concepts; methods of data collection and analysis for labour statistics, industrial, and agricultural and rural statistics.

Having successfully completed the module, students should be able to:

Understand fundamental statistical concepts; methods of data collection and analysis for labour statistics, industrial, agricultural and rural statistics.

Conduct basic data collection techniques including surveys.

The Module of Inferential Statistics is intended to impart the core knowledge on statistical inference–random sampling distributions, estimation and hypothesis testing. In addition to that the students are also expected to learn the applications of this theoretical knowledge to practical situations of statistical inference.

It introduces simple linear regression and Bayesian inference with the intension of students to learn different techniques that are used in statistical estimation and prediction. It is also designed to give knowledge to students on the topics focusing on using SPSS with the SPSS beginner in mind.

Brief description of aims and content

The primary aims are to advance students’ understanding of economics and to train students to analyze problems using neoclassical microeconomic theory. Over the course of the semester, students will study the functioning of markets, and learn how to apply economic principles to a variety of business and public policy issues.

In this module students should gain sufficient understanding of macroeconomic theory to understand empirical research and so give a valuable insight into topical issues. Particular reference is made to the relative importance of the economics in the long run and macroeconomic policies in the open economy.

1. Micro and macroeconomic models and problems expressed in standard diagrammatic and basic mathematical terms, and be able to examine problems based on such models.
2. The implications of different macroeconomic policies.
3. Demonstrate knowledge and understanding of the main subject of the modules.
4. Understand micro and macroeconomic content in applied economic journals.

The module introduces students to the basic techniques of formal (statistical) demographic analysis. All demographic sub-fields will be covered at a basic level: marriage, fertility, mortality, population growth, migration, as well as techniques and purpose of standardization and evaluation of data.

The module also aims at familiarizing students to theories of the interrelationship between population, resources and development as well as to the complexities of interdisciplinary nature of population and economic development, and to population policies.

IT aids plenty of resources to enhance the teaching skills and learning ability. With the help of IT now it is easy to provide audio visual education. The learning resources are being widens and widen. Now with this vivid and vast technique as part of the IT curriculum, learners are encouraged to regard computers as tools to be used in all aspects of their studies. In particular, they need to make use of the new multimedia technologies to communicate ideas, describe projects, and order information in their work!!

This lecture aims to show the direct link between data  systems and their use in economic modeling aimed to analyze specific policy issues. The lecture first illustrates the use of a SAM in the calculation of accounting and price multipliers and their use in the analysis of economy-wide effects of an injection. The second part of the lecture describes how to calibrate a static applied general equilibrium (AGE) model using a SAM and an IO table. It further explores how to apply this calibrated model in the analysis of alternative policy scenarios.

This Module introduces to the students Statistics which is essentially a decision making tool. This module imparts knowledge on fundamental statistical concepts and how to convert data into information, which enables policy makers, managers, and researchers to make informed decisions.

This module covers the application of many kinds of mathematical methods to improve the efficiency of processes and the management of complex systems. A range of techniques associated with mathematical models constructed to describe and solve problems is considered. Operations Research concerns the application of many kinds of mathematical methods to improve the efficiency of processes and the management of complex systems. The module is discussing a variety of useful models, techniques and applications. Some of the models which are discussed in this module include transportation models, network models, project scheduling, inventory control models and non linear programming models. The applications include industrial processes, management systems, road and transport networks, telecommunication systems and construction projects.