Course Title: Analyse data and report results

Part B: Course Detail

Teaching Period: Term2 2013

Course Code: MATH5334C

Course Title: Analyse data and report results

School: 155T Vocational Health and Sciences

Campus: City Campus

Program: C5282 - Diploma of Laboratory Technology (Biotechnology)

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:

1.1. Ensure raw data are consistent with expectations and reasonable ranges
1.2. Calculate scientific quantities involving algebraic, power, exponential and/or logarithmic functions
1.3. Ensure calculated quantities are consistent with estimations
1.4. Present results using the appropriate units, uncertainties and number of significant figures


2. Analyse trends and relationships in data

Performance Criteria:

2.1. Determine linear and non-linear relationships between sets of data
2.2. Prepare and analyse control charts to determine if a process is in control
2.3. Identify possible causes for out-of-control condition
2.4. Follow enterprise procedures to return process to in-control operation


3. Determine variation and/or uncertainty in data distributions

Performance Criteria:

3.1. Organise raw data into appropriate frequency distributions
3.2. Calculate means, medians, modes, ranges and standard deviations for ungrouped and grouped data
3.3. Interpret frequency distributions to determine the characteristics of the sample or population
3.4. Calculate standard deviations and confidence limits for means and replicates
3.5. Estimate the uncertainty in measurements using statistical analysis
3.6. Determine data acceptability using statistical tests and enterprise procedures


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
4.2. 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 in a format and style consistent with their intended use and enterprise guidelines
5.4. Communicate results within the specified time and in accordance with enterprise confidentiality and security guidelines

Learning Outcomes

Details of Learning Activities

1. Lectures

2. Online learning activites

3. Computer Based tutorials

Teaching Schedule

Teaching Schedule
8th July
• Introduction to Statistics
• Basic Statistics
• Descriptive Statistics       
15th July


• Continue with previous week
• Equations involving pH, [H], [OH], buffer calculations, Ka, pKa, Kb, pKb, Kw
• Henderson – Hasselbach equation
22nd July


• Correlation
• Linear and Non-Linear relationship between data
29th July


• Normal Distribution
• Control Charts
5th h August


• Continue with Control Charts
• Types of Control charts
12th August
1. Normal Distribution
2. z scores calculations
19th  August
• Calculating confidence limits for the means
• More on Normal Distribution
• Revision for mid sem
26th August
Mid semester exams  
2nd Sept
• Conducting Statistical Tests
• . z-test
9th Sept
• t-test and using Minitab  
16th Sept
• Conducting Chi-square and using Minitab  
23rd Sept
• Mid Semester Break  
30th Sept
• Conducting ANOVA  using Minitab  
7th Oct
• Case studies based on all statistical tests  
14th Oct
• Continue with previous week.  
21st Oct
Revision/ Catch up  
28th Oct Exam 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

Two assignments to be conducted online (weightage of 10% each)
Mid sem exam(weightage of 30%)
Minitab exam to be conducted online (weightage of 10%)
End of sem exam(weightage of 40%)

Assessment Matrix

Course Overview: Access Course Overview