School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

Your unit may be subject to government or third party COVID-19 vaccination requirements. Please consider this before enrolling in this unit, and speak with the unit coordinator if this raises any concerns.

  • Unit Title

    Applied Multivariate Statistics
  • Unit Code

    MAT3110
  • Year

    2022
  • Enrolment Period

    1
  • Version

    3
  • Credit Points

    15
  • Full Year Unit

    N
  • Mode of Delivery

    On Campus
  • Unit Coordinator

    Dr Ebenezer AFRIFA-YAMOAH

Description

In this unit students will develop understanding of the theory and techniques of multivariate statistical analyses and their applications in areas such as health, biological, environmental and data science. This unit will prepare students for real world data analysis in industry and research.

Prerequisite Rule

Students must pass MAT2110.

Learning Outcomes

On completion of this unit students should be able to:

  1. Apply multifactor and multivariate techniques to solve problems related to multivariate data sets.
  2. Use critical thinking skills with regard to the modelling of multi-factor and multivariate data in an individual and collaborative setting.
  3. Implement statistical analysis using appropriate software.
  4. Communicate statistical theories, principles and techniques to specialist and non-specialist audiences.
  5. Incorporate ethical and cultural considerations in designing a research project, gathering and storing data.

Unit Content

  1. Methods of ordination including multidimensional scaling.
  2. Role of statistical methods in scientific investigation; structure of a data set; statistical models and relevant statistical distributions: normal, t, chi-squared, F, and tests for normality.
  3. Methods of classification including discriminant analysis and cluster analysis.
  4. Factor Analysis: principal components and their properties, principal component analysis, the factor analysis model.
  5. Comparison of multivariate means: hypothesis tests; confidence regions.
  6. Regression Analysis: the multiple linear regression model, least squares estimation, analysis of residuals, model building, coefficient of determination, selection of a subset of regressors.

Learning Experience

Students will attend on campus classes as well as engage in learning activities through ECU's LMS

JoondalupMount LawleySouth West (Bunbury)
Semester 213 x 2 hour lectureNot OfferedNot Offered
Semester 213 x 2 hour tutorialNot OfferedNot Offered

For more information see the Semester Timetable

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.

ON CAMPUS
TypeDescriptionValue
AssignmentMultipart assignment30%
TestMid-semester test30%
ExaminationEnd of semester examination.40%

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Academic Integrity

Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.

Breaches of academic integrity can include:

Plagiarism

Copying the words, ideas or creative works of other people, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).

Unauthorised collaboration (collusion)

Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).

Contract cheating

Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.

Cheating in an exam

Using or having access to unauthorised materials in an exam or test.

Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.

ECU's policies and more information about academic integrity can be found on the student academic integrity website.

All commencing ECU students are required to complete the Academic Integrity Module.

Assessment Extension

In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000001386.

Special Consideration

Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000003318.

MAT3110|3|1

School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

Your unit may be subject to government or third party COVID-19 vaccination requirements. Please consider this before enrolling in this unit, and speak with the unit coordinator if this raises any concerns.

  • Unit Title

    Applied Multivariate Statistics
  • Unit Code

    MAT3110
  • Year

    2022
  • Enrolment Period

    2
  • Version

    3
  • Credit Points

    15
  • Full Year Unit

    N
  • Mode of Delivery

    On Campus
  • Unit Coordinator

    Dr Ebenezer AFRIFA-YAMOAH

Description

In this unit students will develop understanding of the theory and techniques of multivariate statistical analyses and their applications in areas such as health, biological, environmental and data science. This unit will prepare students for real world data analysis in industry and research.

Prerequisite Rule

Students must pass MAT2110.

Learning Outcomes

On completion of this unit students should be able to:

  1. Apply multifactor and multivariate techniques to solve problems related to multivariate data sets.
  2. Use critical thinking skills with regard to the modelling of multi-factor and multivariate data in an individual and collaborative setting.
  3. Implement statistical analysis using appropriate software.
  4. Communicate statistical theories, principles and techniques to specialist and non-specialist audiences.
  5. Incorporate ethical and cultural considerations in designing a research project, gathering and storing data.

Unit Content

  1. Methods of ordination including multidimensional scaling.
  2. Role of statistical methods in scientific investigation; structure of a data set; statistical models and relevant statistical distributions: normal, t, chi-squared, F, and tests for normality.
  3. Methods of classification including discriminant analysis and cluster analysis.
  4. Factor Analysis: principal components and their properties, principal component analysis, the factor analysis model.
  5. Comparison of multivariate means: hypothesis tests; confidence regions.
  6. Regression Analysis: the multiple linear regression model, least squares estimation, analysis of residuals, model building, coefficient of determination, selection of a subset of regressors.

Learning Experience

Students will attend on campus classes as well as engage in learning activities through ECU's LMS

JoondalupMount LawleySouth West (Bunbury)
Semester 213 x 2 hour lectureNot OfferedNot Offered
Semester 213 x 2 hour tutorialNot OfferedNot Offered

For more information see the Semester Timetable

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.

ON CAMPUS
TypeDescriptionValue
AssignmentMultipart assignment30%
TestMid-semester test30%
ExaminationEnd of semester examination.40%

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Academic Integrity

Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.

Breaches of academic integrity can include:

Plagiarism

Copying the words, ideas or creative works of other people, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).

Unauthorised collaboration (collusion)

Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).

Contract cheating

Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.

Cheating in an exam

Using or having access to unauthorised materials in an exam or test.

Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.

ECU's policies and more information about academic integrity can be found on the student academic integrity website.

All commencing ECU students are required to complete the Academic Integrity Module.

Assessment Extension

In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000001386.

Special Consideration

Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000003318.

MAT3110|3|2