The purpose of this paper is to develop skills in the use of statistical packages for data analysis and modelling. The emphasis is on observational rather than experimental data. The topics covered are regression modelling and its generalisations; cluster analysis; principal components analysis and discriminant analysis.
Hamish Rodger (firstname.lastname@example.org)
Official Timetable Information
Students who successfully complete this paper should be able to:
* identify the appropriate technique to use when analysing data within the scope of the techniques covered
* communicate the results from the analysis
* have developed skills in analysing data within the scope of the techniques covered when using R and Weka.
STAT221 Statistical Data Analysis
The paper is divided into two main topics: Regression, taught by Steven Miller in in weeks 1 to 6, and Multivariate techniques, taught by Bob Durrant in weeks 7 to 12 [jointly with the students on COMP321].
Class attendance is expected. The lecture material, assigned readings and tutorial exercises are all integral parts of the paper. Failure to attend any of these means a student may miss material not presented elsewhere. Students are responsible for all material covered in the paper.
Students should expect to spend a minimum of about 10 hours per week on this paper. This includes the four contact hours mentioned above.
Students are required to submit internal assessment items by the required deadlines.
The assignments must be submitted by the required deadline unless prior arrangements have been made for an extension. Assignments that are handed in late without prior arrangement will be penalised. Late assignments will not be marked in detail. We may decide to award dome credit, but there is the possibility of zero marks being awarded for that item of assessment.
Tutorial exercises that are handed in after the require deadline will not be marked unless prior arrangements have been made for an extension.
Extensions will normally only be given in the case of illness, family bereavement or serious personal circumstances.
This is comprised of two components, your internal work and your final exam. The internal work : final exam ratio will be 1 : 1. However you must achieve at least 40% in both internal work and final exam to achieve a clear pass.
Assignments will be submitted online to the link provided on Moodle. All assignments must be accompanied by a completed copy of the Statistics Department cover sheet (also available from Moodle).
Marked assignments and tests will be handed back in your lectures. Marked work that is not picked up in class can be collected from the FCMS reception, ground floor, FG link town, between 1-5pm on weekdays. Please note that assessment items not collected within four weeks will be put into storage, and one week's notice must be given to have them recovered (email email@example.com). The expected turnaround time for marking the test and assignments is one week.
Internal assessment/final examination ratio 1:1
Each half of the paper contributes to 50% of the internal assessment. The internal assessment will consist of:
Weeks 1-6 assessment:
2 assignments (30% and one test (20%).
Test: Wednesday 20th August (week 6) 12noon in G.3.33
Weeks 7-12 assessment
5 tutorial exercises (30%) and one assignment (20%).
The material in the lectures may be extended by readings from various sources. These readings are part of the paper and may be assessed.
Steven will use the R statistical package. Download your own copy of R from http://cran.stat.auckland.ac.nz/. Bob will be using Weka and R. Weka may be downloaded from http://www.cs.waikato.ac.nz/~ml/weka/
The Overall Satisfaction with the Quality of this Paper item in the 2013 student appraisal for STAT321 was 2.0 on a scale from 1 to 5. This was based on 4 responses from the 7 students enrolled, a response rate of 57%.
Very few comments were received regarding the paper, either positive or negative. One student asked that more background to topics not covered in this course but presumed known, could be provided for students coming from different academic pathways. We will try to take this into consideration in 2014.
We take student appraisal information seriously and hope you will consider giving us constructive feedback on what you like and dislike at the end of the course.
The class noticeboard for this paper is located in the corridor outside the Statistics office (G.2.18). Any notices for this paper will be posted there or on Moodle. The internal assessment marks listed by student ID number, will be posted on the noticeboard.
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