Available courses

AEB317 RURAL SOCIOLOGY (2021 SEMESTER 1 FT)

The course AEB317 (Introduction to Rural Sociology) is a two credit core to most undergraduate and Higher Diploma programmes.
It is offered in semester 1 (August to December).

Rural Sociology isa branch of sociology that has its concern with rural social structures, rural livelihoods, rural cultural settings including rural political leadership and other issues that concern rural communities.

With these aspects of the course, the students who take this course are prepared to understand rural development issues.

AEB418 INTRODUCTION TO ECONOMETRICS (2022 SEMESTER 1 FT)

The study of Economics is based on theoretical assumptions about the behaviour of economic agents. Econometrics can be employed to estimate economic relationships and quantify the effects of changes in economic fundamentals on the behaviour of economic agents, as well as their welfare. It applies mathematical and statistical tools on economic data to test and verify theoretical models, and to assess the behaviour of economic agents as well their response to changes in economic variables and public policy, and to conduct hypotheses testing. This course will therefore equip students with econometrics tools for estimating equations for explaining economic relationships. Topics covered include simple linear regression models, multiple linear regression models, functional forms, hypothesis testing, multicollinearity, heteroskedasticity, autocorrelation, regression on dummy independent variables and regressions on dummy dependent variable.

Learning Outcomes

At the end of the course, students will be able to:
• Define econometrics and state its purpose
• Estimate coefficients and related statistical measures for simple regression equations.
• Specify and estimate multiple linear regression models.
• Estimate regression models with alternative functional forms.
• Diagnose classical regression model violations and identify remedial measures.
• Estimate regression models with dummy independent variables.
• Estimate regression models with dummy dependent variables.