| Assessment |
| Title |
Exam Length |
Weight |
Mode |
No. Words |
| Assignment 1 |
|
10%
|
On/Off Campus
|
|
| Assessment Notes |
| Problem solving and computation |
|
| Relates to Learning Outcomes (LO) and
Graduate Attributes (GA) |
| LO: 1, 2, 6
GA: 1, 2, 4, 6 |
| Assignment 2 |
|
10%
|
On/Off Campus
|
|
| Assessment Notes |
| Problem solving and computation |
|
| Relates to Learning Outcomes (LO) and
Graduate Attributes (GA) |
| LO: 3, 6
GA: 1, 2, 4, 6 |
| Assignment 3 |
|
10%
|
On/Off Campus
|
|
| Assessment Notes |
| Problem solving and computation |
|
| Relates to Learning Outcomes (LO) and
Graduate Attributes (GA) |
| LO: 4-6
GA: 1, 2, 4, 6 |
| Online Assessment |
|
10%
|
Off Campus
|
|
| Assessment Notes |
| 4 quizzes |
|
| Relates to Learning Outcomes (LO) and
Graduate Attributes (GA) |
| LO: 1, 2, 3, 5
GA: 1, 2, 4, 6 |
| Tutorial Assessment |
|
10%
|
On Campus
|
|
| Assessment Notes |
| Four quizzes will form part of the tutorial assessment |
|
| Relates to Learning Outcomes (LO) and
Graduate Attributes (GA) |
| LO: 1, 2, 3, 5
GA: 1, 2, 4, 6 |
|
| Final Examination |
2 hrs
|
60%
|
On/Off Campus
|
|
| Relates to Learning Outcomes (LO) and
Graduate Attributes (GA) |
| LO: 1-6
GA: 1, 2, 4, 6 |
|
| Learning Outcomes (LO) |
Upon completion of this unit, students will be able to:
-
test hypotheses concerning differences between two or more population parameters (e.g., means, proportions);
-
understand and use chi-square and other non-parametric test procedures;
-
understand and estimate multiple regression models; interpret, and test hypotheses concerning the parameters of a multiple regression model and its use for prediction; estimate and intepret models with dummy variables; specify, estimate and interpret simple non-linear models;
-
understand and use different time-series forecasting models (moving averages, exponential smoothing, linear trend and quadratic trend model) to forecast a time series;
-
compute and interpret index numbers for measuring changes in prices over time or space; and
-
use excel to conduct data analysis.
|
| Graduate Attributes (GA) |
|
Attribute |
Taught |
Assessed |
Practised |
| 1 |
Knowledge of a Discipline
This graduate attribute is central to the unit and embedded in the notes and readings, practised by the students, and assessed.
|
|
|
|
| 2 |
Communication Skills
The lectures include instructions on how to examine and statistically analyse economic and business data in order to solve real world problems. These skills are practised through tutorial exercises and assessed through assignments and the exam.
|
|
|
|
| 3 |
Global Perspectives
The unit includes consideration of real-world business and economic data. This knowledge is embedded in examples.
|
|
|
|
| 4 |
Information Literacy
Students are taught the ability to examine data and interpret analytical results. These are practised during tutorial exercises and assessed in assignments and exams.
|
|
|
|
| 5 |
Life-Long Learning
Students are taught foundation principles which will be the basis for analytical work in their future employment.
|
|
|
|
| 6 |
Problem Solving
The lectures and tutorials are based on understanding the principles in analysing business and economic problems. The assessment questions are designed to test the students? ability to solve such problems.
|
|
|
|
| 7 |
Social Responsibility
This unit teaches and requires the practice of ethical applications of statistical methods.
|
|
|
|
| 8 |
Team Work
Students are encouraged to practise teamwork during tutorial sessions and online discussions.
|
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|
|
|
