Course Title: Analyse data and report results
Part B: Course Detail
Teaching Period: Term2 2015
Course Code: MATH5334C
Course Title: Analyse data and report results
School: 155T Vocational Health and Sciences
Campus: City Campus
Program: C5283 - Diploma of Laboratory Technology (Pathology Testing)
Course Contact: Amberlee Mitton
Course Contact Phone: +61 3 9925 8053
Course Contact Email: amberlee.mitton@rmit.edu.au@rmit.edu.au
Name and Contact Details of All Other Relevant Staff
Namrita Kaul
99254309
Nominal Hours: 80
Regardless of the mode of delivery, represent a guide to the relative teaching time and student effort required to successfully achieve a particular competency/module. This may include not only scheduled classes or workplace visits but also the amount of effort required to undertake, evaluate and complete all assessment requirements, including any non-classroom activities.
Pre-requisites and Co-requisites
Pre-Requisites MSL924001A - Process and interpret data
Course Description
This unit of competency covers the ability to perform scientific calculations, analyse trends and uncertainty in data and report results within the required timeframe.
National Codes, Titles, Elements and Performance Criteria
National Element Code & Title: |
MSL925001A Analyse data and report results |
Element: |
1. Perform scientific calculations |
Performance Criteria: |
1.1. Ensure raw data are consistent with expectations and reasonable ranges |
Element: |
2. Analyse trends and relationships in data |
Performance Criteria: |
2.1. Determine linear and non-linear relationships between sets of data |
Element: |
3. Determine variation and/or uncertainty in data distributions |
Performance Criteria: |
3.1. Organise raw data into appropriate frequency distributions |
Element: |
4. Check for aberrant results |
Performance Criteria: |
4.1. Identify results that cannot be reconciled with sample, sample documentation, testing procedures and/or expected outcomes |
Element: |
5. Report results |
Performance Criteria: |
5.1. Use charts, tables and graphs to present results in the required format |
Learning Outcomes
Details of Learning Activities
1. Lectures
2. Online learning activites
3. Computer Based tutorials
Teaching Schedule
6th July |
• Introduction to Statistics • Basic Statistics • Descriptive Statistics |
|
13th July
|
• Continue with previous week • Equations involving pH, [H], [OH], buffer calculations, Ka, pKa, Kb, pKb, Kw • Henderson – Hasselbach equation |
|
20th July
|
• Correlation • Linear and Non-Linear relationship between data |
|
27th July
|
• Normal Distribution • Control Charts |
|
3rd August
|
• Continue with Control Charts • Types of Control charts |
|
10th August |
1. Normal Distribution 2. z scores calculations |
|
17th August |
• Calculating confidence limits for the means • More on Normal Distribution • Revision for mid sem |
|
24th August |
Mid semester exams | |
31st August |
Student Vacation |
|
7th Sept |
Conducting statistical tests z-test |
|
14th Sept |
•t-test and using Minitab | |
21st Sept |
• Conducting Chi-square and using Minitab | |
28th Sept |
• Conducting ANOVA using Minitab | |
5th Oct |
• Case studies based on all statistical tests • Valid measurement and data acceptability tests |
|
12th Oct |
• Continue with previous week. | |
19th Oct |
Revision/ Catch up | |
26th Oct | Exam Week | |
Learning Resources
Prescribed Texts
Handouts and Online Learning materials will be provided during the course. |
References
Practical statistics for the health sciences / Peter Martin, Robyn Pierce |
|
Statistics for starters / Peter Martin & Robyn Pierce |
Other Resources
Handouts on MyStudies RMIT
Overview of Assessment
Assessments for this course typically consist of:
• Class activities
• Assignments
• Presentations
• Exams
Assessment Tasks
To demonstrate competency in this course you need to successfully complete each one of the following pieces of Assessment:
Two assignments to be conducted online (weightage of 10% each)
Mid semester exam (weightage of 30%)
Minitab exam to be conducted online (weightage of 10%)
End of semester exam (weightage of 40%)
Assessment Matrix
Other Information
This course is graded in accordance with competency-based assessment, but which also utilise graded assessment
CHD Competent with High Distinction (80 – 100%)
CDI Competent with Distinction (70 – 79%)
CC Competent with Credit (60 – 69%)
CAG Competency Achieved – Graded (50 – 59%)
NYC Not Yet Competent
DNS Did Not Submit for assessment
• To pass the course you need to pass, on average, each type of assessment (exams and assignments etc.) For example, if there are two exams you need to have an average of 50% to pass and you also need to have passed the other assessment types. You can’t make up marks from one type of assessment to another (e.g. pass the exams but fail the prac component).
• Late work that is submitted without an application for an extension (see below) will not be corrected.
• APPLICATION FOR EXTENSION OF TIME FOR SUBMISSION OF ASSESSABLE WORK - A student may apply for an extension of up to 7 days from the original due date. They must lodge the application form (available online http://www1.rmit.edu.au/students/assessment/extension) at least 24 hours before the due date. The application is lodged with the School Admin Office on Level 6, Bdg 51. Students requiring longer extensions must apply for SPECIAL CONSIDERATION.
• For missed assessments such as exams- you (& your doctor if you are sick) must fill out a special consideration form. This form must be lodged online with supporting evidence prior to, or within, 48 hours of the scheduled time of examination http://www1.rmit.edu.au/students/specialconsideration
Course Overview: Access Course Overview