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  2. Applied Multivariate Statistics [MAT3110]

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.

  • Unit Title

    Applied Multivariate Statistics

  • Unit Code

    MAT3110

  • Year

    2019

  • Enrolment Period

    1

  • Version

    2

  • Credit Points

    15

  • Full Year Unit

     N

  • Mode of Delivery

    On Campus

  • Unit Coordinator

    Dr Johnny Su Hau LO

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 1 unit from MAT2110

Learning Outcomes

On completion of this unit students should be able to:

  1. Review, analyse and solve problems related to multi-factor and multivariate data sets.
  2. Identify and apply the appropriate multi-factor and multivariate statistical technique for a wide variety of data sets.
  3. Produce estimates and predictions based on multivariate and multi-factor generalised linear regression models.
  4. Understand the theories and principles of multivariate statistical analysis.
  5. Demonstrate critical thinking skills with regard to the modelling of multi-factor and multivariate data in an individual and collaborative setting.
  6. Apply and implement statistical analysis with appropriate software.
  7. Communicate an understanding of multi-factor and multivariate statistical techniques in both oral and written form.

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.

Additional Learning Experience Information

Lectures, tutorials and workshops

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 Board of Examiners.

ON CAMPUS
TypeDescriptionValue
AssignmentMultipart assignment25%
TestMid-semester test25%
PresentationOral presentation10%
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 Misconduct

Edith Cowan University has firm rules governing academic misconduct and there are substantial penalties that can be applied to students who are found in breach of these rules. Academic misconduct includes, but is not limited to:

  • plagiarism;
  • unauthorised collaboration;
  • cheating in examinations;
  • theft of other students' work;

Additionally, any material submitted for assessment purposes must be work that has not been submitted previously, by any person, for any other unit at ECU or elsewhere.

The ECU rules and policies governing all academic activities, including misconduct, can be accessed through the ECU website.

MAT3110|2|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.

  • Unit Title

    Applied Multivariate Statistics

  • Unit Code

    MAT3110

  • Year

    2019

  • Enrolment Period

    2

  • Version

    2

  • Credit Points

    15

  • Full Year Unit

     N

  • Mode of Delivery

    On Campus

  • Unit Coordinator

    Dr Johnny Su Hau LO

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 1 unit from MAT2110

Learning Outcomes

On completion of this unit students should be able to:

  1. Review, analyse and solve problems related to multi-factor and multivariate data sets.
  2. Identify and apply the appropriate multi-factor and multivariate statistical technique for a wide variety of data sets.
  3. Produce estimates and predictions based on multivariate and multi-factor generalised linear regression models.
  4. Understand the theories and principles of multivariate statistical analysis.
  5. Demonstrate critical thinking skills with regard to the modelling of multi-factor and multivariate data in an individual and collaborative setting.
  6. Apply and implement statistical analysis with appropriate software.
  7. Communicate an understanding of multi-factor and multivariate statistical techniques in both oral and written form.

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.

Additional Learning Experience Information

Lectures, tutorials and workshops

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 Board of Examiners.

ON CAMPUS
TypeDescriptionValue
AssignmentMultipart assignment25%
TestMid-semester test25%
PresentationOral presentation10%
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 Misconduct

Edith Cowan University has firm rules governing academic misconduct and there are substantial penalties that can be applied to students who are found in breach of these rules. Academic misconduct includes, but is not limited to:

  • plagiarism;
  • unauthorised collaboration;
  • cheating in examinations;
  • theft of other students' work;

Additionally, any material submitted for assessment purposes must be work that has not been submitted previously, by any person, for any other unit at ECU or elsewhere.

The ECU rules and policies governing all academic activities, including misconduct, can be accessed through the ECU website.

MAT3110|2|2