Overview

To view overview information, please select an offering from the drop-down menu above.

Academic contacts

To view unit coordinator information, please select an offering from the drop-down menu above.

Offerings

KAPLAN-SGP-TJA-INT-2024-2024
KAPLAN-SGP-TJA-MIXEDMODE-2026-2026
KAPLAN-SGP-TMA-MIXEDMODE-2025-2025
KAPLAN-SGP-TSA-INT-2024-2024
KAPLAN-SGP-TSA-MIXEDMODE-2026-2026
MURDOCH-S1-EXT-2024-2024
MURDOCH-S1-FACE2FACE-2025-ONGOING
MURDOCH-S1-INT-2024-2024
MURDOCH-S1-ONLINEFLEX-2025-ONGOING
MURDOCH-S2-EXT-2023-2024
MURDOCH-S2-FACE2FACE-2025-ONGOING
MURDOCH-S2-INT-2023-2024
MURDOCH-S2-ONLINEFLEX-2025-ONGOING

Enrolment rules

Prior studies equivalent to MAS183 Statistical Data Analysis OR ICT583, OR MBS659 Quantitative Research for Business. Data Science Applications and enrolment in a Graduate IT course or permission of the Academic Chair.
Students who have completed MAS223 Applied Statistics require permission of the Academic Chair to enrol.

Other learning activities

To view other learning activity information, please select an offering from the drop-down menu above.

Learning activities

To view learning activity information, please select an offering from the drop-down menu above.

Learning outcomes

1.

Perform a variety of statistical analyses with statistical software applying a variety of specific experimental design or modelling approaches.

2.

Explain conceptually the various statistical methods covered in the unit, the correct application of these methods, and interpret statistical software output.

3.

Solve a real life research question by statistical analyses.

4.

Illustrate the statistical analyses output using a comprehensive technical report and excecutive summary for non-technical audience.

Assessments

To view assessment information, please select an offering from the drop-down menu above.

Additional information

Unit content:Topics covered in this unit include: · Descriptive statistics, graphical displays, and data cleaning · Sampling distributions, the boostrap, and the jackknife · Linear regression and modelling for inferential purposes · Model prediction · Principal component analysis, · Discriminant analysis · Cluster analysis Select additional topics.