ADVANCED TOPICS IN STATISTICS D

STA5ATD

2020

Credit points: 15

Subject outline

This subject enables 5th year statistics students to enrol in subjects offered through the Key Centre of Statistical Science (KCSS) or through the Department of Mathematics and Statistics Access Grid Room. Subject syllabus is dependent on the subject chosen and students are advised to refer to the departmental website or the Department of Mathematics and Statistics Master of Statistical Science Coordinator prior to enrolment for more details. Enrolment into this subject must be approved by the Department of Mathematics and Statistics Master of Statistical Science Coordinator.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Andriy Olenko

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 5 - Masters

Exchange Students: No

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: only by approval of the Master of Statistical Science coordinator

Co-requisites: N/A

Incompatible subjects: STA4ATD

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Introduction to Linear Regression Analysis

Resource Type: Book

Resource Requirement: Recommended

Author: Montgomery, D. C, Peck, E. A. and Vining, G.

Year: 2006

Edition/Volume: 4TH EDITION

Publisher: WILEY

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Demonstrate specialised theoretical and technical skills in a specified statistics topic.

02. Use specialised cognitive and technical skills to critically analyse, reflect on and synthesise complex information, problems, concepts and theories relevant to the topic.

03. Apply established theories relevant to the topic.

04. Use advanced communication skills to transmit knowledge and ideas of statistics to others.

05. Demonstrate autonomy, expert judgement, adaptability and responsibility as a statistician.

Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Andriy Olenko

Class requirements

LectureWeek: 10 - 22
Two 1.00 hour lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
This is dependent on the subject.	N/A	N/A	N/A	No	100	SILO1, SILO2, SILO3, SILO4, SILO5

Melbourne (Bundoora), 2020, Semester 2, Day