|Semester 1, 2020 Online|
|Short Description:||Multvrt Stat Mthds|
|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|
Examiner: Rachel King
STA3200 is not available to students who have already undertaken or intend to undertake STA8005.
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.
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.
On successful completion of this course students should be able to:
- Demonstrate 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.
- Effectively implement R software to perform analysis using different statistical methods.
- Synthesise and interpret analyses and communicate the results of analyses to a diverse audience to aid decision making.
|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.||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 USQ's Online Bookshop (unless otherwise stated). (https://omnia.usq.edu.au/textbooks/?year=2020&sem=01&subject1=STA3200)
Please contact us for alternative purchase options from USQ Bookshop. (https://omnia.usq.edu.au/info/contact/)
Student workload expectations
|Description||Marks out of||Wtg (%)||Due Date||Notes|
|Assignment 1||100||20||14 Apr 2020|
|Assignment 2||100||30||21 May 2020|
|Assignment 3||100||50||12 Jun 2020||(see note 1)|
- The assignment date will be available via UConnect when the Alternative Assessment Schedule has been released. Students will be provided further instruction regarding the assignment by their course examiner via StudyDesk.
Important assessment information
There are no attendance requirements for this course. However, it is the students' responsibility 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:
Due to COVID-19 the requirements for S1 2020 are: To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.
Requirements after S1 2020:
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 http://policy.usq.edu.au/documents.php?id=14749PL (point 4.2.4)
Requirements for student to be awarded a passing grade in the course:
Due to COVID-19 the requirements for S1 2020 are: To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.
Requirements after S1 2020:
To be assured of receiving a passing grade a student must obtain at least 50% of the total weighted marks available for the course (i.e. the Primary Hurdle), and have satisfied the Secondary Hurdle (Supervised), i.e. the end of semester examination by achieving at least 40% of the weighted marks available for that assessment item.
Supplementary assessment may be offered where a student has undertaken all of the required summative assessment items and has passed the Primary Hurdle but failed to satisfy the Secondary Hurdle (Supervised), or has satisfied the Secondary Hurdle (Supervised) but failed to achieve a passing Final Grade by 5% or less of the total weighted Marks.
To be awarded a passing grade for a supplementary assessment item (if applicable), a student must achieve at least 50% of the available marks for the supplementary assessment item as per the Assessment Procedure http://policy.usq.edu.au/documents/14749PL (point 4.4.2).
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.
Due to COVID-19 the requirements for S1 2020 are: There is no examination in this course.
Requirements after S1 2020:
In a Restricted Examination, candidates are allowed access to specific materials during the examination. The only materials that candidates may use in the restricted examination for this course are:
- writing materials (non-electronic and free from material which could give the student an unfair advantage in the examination);
- calculators which cannot hold textual information (students must indicate on their examination paper the make and model of any calculator(s) they use during the examination)
- Students whose first language is not English, may, take an appropriate unmarked non-electronic translation dictionary (but not technical dictionary) into the examination. Dictionaries with any handwritten notes will not be permitted. Translation dictionaries will be subject to perusal and may be removed from the candidate's possession until appropriate disciplinary action is completed if found to contain material that could give the candidate an unfair advantage.
Examination period when Deferred/Supplementary examinations will be held:
Due to COVID-19 the requirements for S1 2020 are: There is no examination in this course, there will be no deferred or supplementary examinations.
Requirements after S1 2020:
Any Deferred or Supplementary examinations for this course will be held during the next examination period.
University Student Policies:
Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at http://policy.usq.edu.au.
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 http://www.usq.edu.au/current-students/support/computing/hardware
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.