| Semester 1, 2020 Online | |
| Short Description: | Multvrt Anlysis High-Dim Data |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Sciences |
| Student contribution band : | Band 2 |
| ASCED code : | 010103 - Statistics |
| Grading basis : | Graded |
Staffing
Examiner:
Requisites
Pre-requisite or Co-requisite: STA8170 or STA2300
Other requisites
STA8005 is not available to students who have already undertaken or intend to undertake STA3200.
Rationale
Statistics is concerned with the process of making sense out of data. It is the study of uncertainty and is concerned with the process of decision making in the face of uncertainty. As our ability to collect, accumulate and access data increases so does the Volume (amount), Variety (of types, sources and resolutions of data), Velocity (speed of data generation and handling) and Veracity (amount of noise and processing errors) of the data sets we wish to analyse and extract valuable information from. Variety creates wide or high-dimensional data sets that may require specific analytic approaches in order to distinguish useful patterns or develop predictive models for decision making.
Synopsis
This course covers some of the statistical concepts and methodologies appropriate for the analysis of large and/or high dimensional data sets. Students will learn the mathematical foundation of a number of statistical methods, the benefit and limitations of each method, how to correctly apply these methods using statistical software and how to assess the effectiveness of given analyses for given data sets. Students will also learn how to perform statistical analyses in the statistical software R. This will require students to master the writing of R code.
Objectives
On successful completion of this course students should be able to:
- Demonstrate advanced and integrated understanding of high-dimensional data sets.
- Apply the knowledge of high-dimensional data sets in the evaluation and choice of appropriate statistical methods.
- Apply the knowledge of a range of computational methods and diagnostic techniques to test hypotheses and evaluate and interpret the output correctly and in context.
- Analyse critically the capabilities of and implement R software as a statistical package different statistical methods.
- Independently develop an appropriate strategy for the analysis of a complex high-dimensional data set and effectively communicate the results with justification of statistical decisions made throughout the process.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Review matrix algebra, linear regression and confidence intervals. Introduction to the features of high-dimension data, graphical summaries and R programming. | 20.00 |
| 2. | Multivariate Normality and Hypothesis Testing | 20.00 |
| 3. | Multidimensional Scaling and Cluster Analysis | 20.00 |
| 4. | Discriminant Function Analysis and Canonical Correlation Analysis | 20.00 |
| 5. | Principle Components Analysis and Factor Analysis | 20.00 |
Text and materials required to be purchased or accessed
ALL textbooks and materials available to be purchased can be sourced from (unless otherwise stated). (https://omnia.usq.edu.au/textbooks/?year=2020&sem=01&subject1=STA8005)
Please for alternative purchase options from USQ Bookshop. (https://omnia.usq.edu.au/info/contact/)
Reference materials
Student workload expectations
| Activity | Hours |
|---|---|
| Assessments | 50.00 |
| Online Lectures | 26.00 |
| Online Tutorials | 26.00 |
| Private 精东传媒app | 70.00 |
Assessment details
| Description | Marks out of | Wtg (%) | Due Date | Notes |
|---|---|---|---|---|
| Assignment 1 | 100 | 20 | 14 Apr 2020 | |
| Assignment 2 | 100 | 30 | 26 May 2020 | |
| Project | 100 | 50 | 11 Jun 2020 |
Important assessment information
-
Attendance requirements:
It is the students' responsibility to participate appropriately in all activities and to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration. -
Requirements for students to complete each assessment item satisfactorily:
To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item. -
Penalties for late submission of required work:
Students should refer to the Assessment Procedure (point 4.2.4) -
Requirements for student to be awarded a passing grade in the course:
To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course. -
Method used to combine assessment results to attain final grade:
The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative items for the course. -
Examination information:
NO EXAM: There is no examination in this course. -
Examination period when Deferred/Supplementary examinations will be held:
NO EXAM: There is no examination in this course, there will be no deferred or supplementary examinations. -
精东传媒app Student Policies:
Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene 精东传媒app policies and practices. These policies can be found at .
Other requirements
-
Computer, e-mail and Internet access:
Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at . -
Students can expect that questions in assessment items in this course may draw upon knowledge and skills that they can reasonably be expected to have acquired before enrolling in this course. This includes knowledge contained in pre-requisite courses and appropriate communication, information literacy, analytical, critical thinking, problem solving or numeracy skills. Students who do not possess such knowledge and skills should not expect the same grades as those students who do possess them.