Financial Statistics

FIN10002 12.5 Credit Points Hawthorn Available to incoming Study Abroad and Exchange students

Duration

  • One Semester or equivalent

Contact hours

  • 24 hours face to face + blended

On-campus unit delivery combines face-to-face and digital learning.

2024 teaching periods

Hawthorn

Higher Ed. Semester 1		 Hawthorn

Higher Ed. Semester 2

Dates:
26 Feb 24 - 26 May 24

Results:
2 Jul 24

Last self enrolment:
10 Mar 24

Census:
31 Mar 24

Last withdraw without fail:
12 Apr 24

Dates:
29 Jul 24 - 27 Oct 24

Results:
3 Dec 24

Last self enrolment:
11 Aug 24

Census:
31 Aug 24

Last withdraw without fail:
13 Sep 24
More teaching periods
Swinburne Online

Teaching Period 1		 Swinburne Online

Teaching Period 2

Dates:
11 Mar 24 - 9 Jun 24

Results:
2 Jul 24

Last self enrolment:
24 Mar 24

Census:
5 Apr 24

Last withdraw without fail:
26 Apr 24

Dates:
8 Jul 24 - 6 Oct 24

Results:
29 Oct 24

Last self enrolment:
21 Jul 24

Census:
2 Aug 24

Last withdraw without fail:
23 Aug 24

Prerequisites

Anti-requisite (similar in content)
Students that have successfully completed STA10003 Foundations of Statistics should not study this unit as it is similar in content.
 

Aims and objectives

This unit provides students with an introduction to statistics within a financial context. Students will gain an appreciation of what statistical methods can achieve, as well as skills in preparing, analysing and interpreting business data and statistical analysis. Students will also learn how to apply analytical tools to visualise and analyse data. The focus of the unit is on data science as an analytical and decision-making tool, in a variety of business contexts, with a major emphasis on interpretation and application.

Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
 
1. Identify and apply commonly used techniques for data collection and analysis. 
2. Apply fundamental concepts of probability and probability distributions to problems in business decision-making. 
3. Apply statistical inference methods to conduct and explain the results of hypothesis testing. 
4. Apply simple regression analysis to explain the relationship between variables to draw inferences about relationships. 
5. Apply technological tools to analyse data for decision-making purposes