| Unit name | Quantitative Methods 1 |
|---|---|
| Unit code | ECON20012 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Proud |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
ECON10009 Essential Mathematics for Economics |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Economics |
| Faculty | Faculty of Arts, Law and Social Sciences |
Why is this unit important?
Being able to understand observed data and make inferences about the relationships between variables is an important and useful tool to acquire, especially in the modern economy as data is becoming more readily available.
In economics, empirical data is used alongside theoretical models to validate hypotheses and make sense of real-world patterns. Combining data and theory is particularly important to make predictions about economic outcomes in the future, and to inform policy-making decisions.
This unit allows you to develop the foundational tools in probability and statistics to analyse data.
How does this unit fit into your programme?
This unit builds upon the mathematical and statistical concepts covered in the first-year courses. The statistical and technical tools developed in this unit provide the foundations needed for second- and third-year units in econometrics and applied topics.
Overview of content
This unit will introduce you to the key statistical tools that underpin econometrics. In the first part of the unit, you will learn basic concepts in statistics, such as evaluating the bias, consistency, and efficiency of simple estimators, and applying formal statistical tests. In the second part of the unit, you will learn how to estimate relationships between economic variables using Ordinary Least Squares and the key principles underlying the concepts of causality and identification strategies.
You will learn topics such as:
How will students, personally, be different as a result of the unit
You will develop an understanding of how to use observational data to make inferences about causal relationships between economic variables. You will gain a rigorous understanding of the linear regression model and how to use it – this is a foundational tool for data analysis in any field (not just economics).
You will develop hands-on experience in using statistical software to analyse economic data, which will be important as you enter the job market. By developing a deeper understanding of the concepts in the course, such as causality and correlation, you will gain a better perspective and tools to recognize important relationships between real-world variables.
Learning outcomes
By the end of this unit, you will be able to:
The learning activities and approaches are designed to combine theory with applications -- you will learn a new theoretical concept and how to apply it. This approach of learning each statistical concept and its application side-by-side is especially well-suited for this unit because the two learning activities reinforce each other. In addition, this approach demonstrates the relevance of the material to real-world problems.
Tasks which help you learn and prepare for summative tasks
You will be given weekly assignments to complete across a range of elements of the unit. You will gain feedback through solutions, and the opportunity to discuss your answers during office hours.
Tasks which count towards your unit mark (summative)
When assessment does not go to plan
If students fail the unit such that credit points cannot be awarded at the first attempt, they will normally be provided reassessment in the failed element(s).
The reassessment tasks will be:
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ECON20012).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
