STATS 301 : Statistical Programming and Modelling using SAS
Science
2020 Summer School (1200) (15 POINTS)
Course Prescription
Course Overview
This is a course in statistical data analysis which has an emphasis on learning how to use the SAS statistical package. In addition to learning how to manipulate data using SAS, the course covers a variety of linear models, including generalised linear models. Students learn how to interpret and communicate statistical findings. Topics covered include: SAS specific data acquisition and manipulation: Creating and saving code, creating and permanently saving data, importing data from other statistical packages, creating and coding variables, use of SAS functions, sub-setting data, concatenating data, merging data, working with arrays, collapsing data and reshaping data. Linear Models: Introduction to regression. Trend and scatter. Simple linear regression. Regression model assumptions, diagnostics, and prediction. Transformations. One sample t-test. Paired and non-paired data. Non-linear trends. Two sample linear models. Dummy variables. Strengths and weaknesses of transformations. Models including interactions. Multiple regression including exploratory tools, factors and diagnostics. One-way ANOVA and two-way ANOVA, including understanding interactions. Generalised linear models for binary data (logistic regression).
Course Requirements
Capabilities Developed in this Course
| Capability 1: | Disciplinary Knowledge and Practice |
| Capability 2: | Critical Thinking |
| Capability 3: | Solution Seeking |
| Capability 4: | Communication and Engagement |
Learning Outcomes
- Analyse: Learn how to read data into SAS, how to check that it has been done it correctly and how to fix it if it hasn’t. (Capability 3)
- Evaluate and apply: Understanding how you can change data for use in subsequent analyses. (Capability 1 and 3)
- Identify and analyse: Modelling multiple variables (continuous and factors) in a regression setting. Transforming data in regression. Interpretation of regression models. Model building. (Capability 1, 2, 3 and 4)
- Analyse: Understanding one-sample and two sample analyses. Data descriptions and Assumption checks. Two way tables of counts and odds-ratios. Appropriate presentation of results. (Capability 1 and 3)
- Identify and analyse: Transforming data in order to satisfy model assumptions. Why, when and how data are transformed before performing analyses, back-transformations and the consequences for inferences. (Capability 1 and 3)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Assignments | 30% | Individual Coursework |
| Final Exam | 50% | Individual Coursework |
| Test | 20% | Individual Coursework |
| 3 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||||||
| Assignments | ||||||||||
| Final Exam | ||||||||||
| Test | ||||||||||
You must get at least 50% in the course work and at least 50% in the final exam.
Key Topics
• How to appreciate the usefulness and ubiquity of SAS in various industries – e.g. Medicine, Finance, Marketing.
• The ‘architecture’ of SAS and solving problems as and when they arise.
• How to configure data from disparate data sources into a format that SAS can ‘read in’ and hence summarise data.
• How to use the SAS statistical package to appropriately model any underlying relationships.
• How to communicate findings in a format that is easy to understand and accessible across different working environments – e.g. MS Word & Excel, HTML.
• How to communicate the main findings from an analysis in written form, using non-technical language.
Learning Resources
Special Requirements
Workload Expectations
This course is a standard 15 point course and students are expected to spend 20 hours per week involved in each 15 point course that they are enrolled in.
For this course, you can expect 6 hours of lectures, a 1 hour computer laboratory help session, 6 hours of reading and thinking about the content and 7 hours of work on assignments and/or test preparation per week.
Digital Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
Copyright
The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.
You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Student Feedback
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.
Your feedback helps to improve the course and its delivery for all students.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.