Statistics for Prediction

Unit Outline (Higher Education)

Level of Unit in Course

AQF Level(s) of Course

Level of Unit in Course

Introductory

Intermediate

Effective Term:

2024/05

Institute / School :

Institute of Innovation, Science & Sustainability

Unit Title:

Statistics for Prediction

Unit ID:

STATS2101

Credit Points:

15.00

Prerequisite(s):

(MS501 or STATS1000)

Co-requisite(s):

Nil

Exclusion(s):

(MS602)

ASCED:

010103

Other Change:

Brief description of the Unit

This unit introduces the two main themes of predictive statistical analysis - regression and time series methods. Data from various disciplinary contexts is utilised, and there is a strong emphasis on computing skills, interpretation of computer output and communication of statistical results and conclusions.

Grade Scheme:

Graded (HD, D, C, P, MF, F, XF)

Work Experience Indicator:

Placement Component: No

Supplementary Assessment:

Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment

Course Level:

Learning Outcomes:

Knowledge:

K1.	Describe relationship between dependent and independent variables using appropriate linear regression models.

K2.	Describe relationships using time series regression models.

K3.	List regression assumptions, and evaluate model appropriateness from these assumptions.

K4.	Recognise importance of regression models for predictions.

Skills:

S1.	Apply available software such as SPSS and MINITAB to develop regression models.

S2.	Build regression models using iterative model selection procedure such as stepwise regression and backward elimination.

S3.	Perform appropriate diagnostics for detecting outlying and influential observations prior to model development.

S4.	Perform appropriate hypothesis tests to determine the significance of independent variables in a regression model.

S5.	Build appropriate time series regression models.

S6.	Use linear regression and time series models for predictions.

S7.	Present clear, orderly and informative statistical summaries and technical reports.

Application of knowledge and skills:

A1.	Build regression models for real life applications.

A2.	Apply regression models to predict future events and conditions.

Unit Content:

•Simple and multiple regression: model selection and evaluation, transformations, residuals and influence.
•Time series analysis and forecasting: classical decomposition, exponential smoothing, regression methods, sinusoidal models.

Graduate Attributes:

	Learning Outcomes Assessed	Assessment Tasks	Assessment Type	Weighting
1.	K1-K4, S1-S7, A1-A2	Read research and apply various aspects of regression and time series.	Assignments	50 - 60%
2.	K1-K4, S2-S6, A1-A2	Summarise theoretical aspects of the unit	Examination	40-50%

Adopted Reference Style:

APA

Professional Standards / Competencies:

Standard / Competency

Threshold Learning Outcomes - Mathematics: Initial

Attribute			Assessed	Level
1 Understanding
	1.1 Demonstrate a coherent understanding of the mathematical sciences.
		1.1.1 Ability to construct logical, clearly presented and justified arguments incorporating deductive reasoning.	Yes	Intermediate
		1.1.2 Understanding of the breadth of the discipline, its role in other fields, and the way other fields contribute to development of the mathematical sciences.	Yes	Intermediate
2 Knowledge
	2.1 Exhibit depth and breadth of knowledge in the mathematical sciences.
		2.1.2 Well-developed knowledge in at least one sub-discipline of the mathematical sciences.	Yes	Intermediate
3 Inquiry and Problem Solving
	3.1 Investigating and solving problems using mathematical and statistical methods.
		3.1.1 Ability to formulate and model practical and abstract problems in mathematical and / or statistical terms using a variety of methods.	Yes	Intermediate
		3.1.2 Ability to apply mathematical and / or statistical principles, concepts, techniques and technology to solve practical and abstract problems and interpret results critically.	Yes	Intermediate
4 Communication
	4.1 Communicate mathematical and statistical information, arguments, or results for a range of purposes using a variety of means.
		4.1.1 Appropriate interpretation of information communicated in mathematical and statistical form.	Yes	Intermediate
		4.1.2 Appropriate presentation of information, reasoning and conclusions in a variety of modes, to diverse audiences (expert and non-expert).	Yes	Intermediate
5 Responsibility
	5.1 Demonstrate personal, professional and social responsibility.
		5.1.1 Ability to self direct learning to extend their existing knowledge and that of others.	No	Intermediate
		5.1.2 Ability to work effectively and responsibly in an individual or team context.	Yes	Intermediate
		5.1.3 Ethical application of mathematical and statistical approaches to solving problems.	Yes	Intermediate