Course Title: Analyse data and report results

Part B: Course Detail

Teaching Period: Term2 2016

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:

Name and Contact Details of All Other Relevant Staff

Namrita Kaul


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


1. Perform scientific calculations

Performance Criteria:


Ensure raw data are consistent with expectations and reasonable ranges


Calculate scientific quantities involving algebraic, power, exponential and/or logarithmic functions


Ensure calculated quantities are consistent with estimations


Present results using the appropriate units, uncertainties and number of significant figures



2. Analyse trends and relationships in data

Performance Criteria:


Determine linear and non-linear relationships between sets of data


Prepare and analyse control charts to determine if a process is in control


Identify possible causes for out-of-control condition


Follow workplace procedures to return process to in-control operation


3. Determine variation and/or uncertainty in data distributions

Performance Criteria:


Organise raw data into appropriate frequency distributions


Calculate means, medians, modes, ranges and standard deviations for ungrouped and grouped data


Interpret frequency distributions to determine the characteristics of the sample or population


Calculate standard deviations and confidence limits for means and replicates


Estimate the uncertainty in measurements using statistical analysis


Determine data acceptability using statistical tests and workplace procedures


4. Check for aberrant results

Performance Criteria:


Identify results that cannot be reconciled with sample, sample documentation, testing procedures and/or expected outcomes


Determine appropriate actions in consultation with supervisor as required


5. Report results

Performance Criteria:


 5.1 Use charts, tables and graphs to present results in the
required format
5.2 Verify that entry of data and results are correct
5.3 Prepare reports and records in a format and style
consistent with their intended use and workplace
5.4 Communicate results within the specified time and in
accordance with workplace confidentiality and security

Learning Outcomes

Details of Learning Activities

1. Lectures

2. Online learning activities

3. Computer Based tutorials

Teaching Schedule

Teaching Schedule(Based on Week starting)
4th July
• Introduction to Statistics
• Basic Statistics
• Descriptive Statistics       
11th July


• Continue with previous week
• Equations involving pH, [H], [OH], buffer calculations, Ka, pKa, Kb, pKb, Kw
• Henderson – Hasselbach equation
18th July


• Correlation
• Linear and Non-Linear relationship between data
25th July


• Normal Distribution
• Control Charts
1st  August


• Continue with Control Charts
• Types of Control charts
8th August
1. Normal Distribution
2. z scores calculations
15th  August
• Calculating confidence limits for the means
• More on Normal Distribution
• Revision for mid sem
22nd August
Mid semester exams 
29th August

Student Vacation

5th Sept

Conducting statistical tests


12th Sept
•t-test and using Minitab 
19th Sept
• Conducting Chi-square and using Minitab 
26th Sept
• Conducting ANOVA  using Minitab 
3rd Oct

• Case studies based on all statistical tests

• Valid measurement and data acceptability tests

10th Oct
• Continue with previous week. 
17th Oct
Revision/ Catch up 
24th OctExam Week 

Learning Resources

Prescribed Texts

Handouts and Online Learning materials will be provided during the course.


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:

  1. Four online assignments/quizzes to be conducted online (10% each) 
  2. Minitab exam- using and interpreting statistical analysis(20%)
  3. In Class Presentation - Data Collection and Analysis(10%)
  4. End of semester exam (30%) 

  (All assessment tasks/online and presentation will be conducted during class time in the class room)

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 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 

Course Overview: Access Course Overview