# Course Title: Analyse data and report results

## Part B: Course Detail

Teaching Period: Term2 2012

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

9925 4309

namrita.kaul@rmit.edu.au

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 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 Element: 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 Element: 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 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 4.2 Determine appropriate actions in consultation with supervisor as required Element: 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

 Week Starting Week No Topic EoC 9th July 1 • Introduction to Statistics • Basic Statistics • Descriptive Statistics 16th July 2 • Continue with previous week • Equations involving pH, [H], [OH], buffer calculations, Ka, pKa, Kb, pKb, Kw • Henderson – Hasselbach equation 23rd July 3 • Correlation • Linear and Non-Linear relationship between data 30th July 4 • Normal Distribution • Control Charts 6th August 5 • Continue with Control Charts • Types of Control charts 13th August 6 1. Normal Distribution 2. z scores calculations 20th August 7 • Calculating confidence limits for the means • More on Normal Distribution • Revision for mid sem 27th August 8 Mid semester exams 3rd Sept 9 Conducting Statistical Tests . z-test 10h Sept 10 t-test and using Minitab 17th Sept 11 Conducting Chi-square and using Minitab 24th Sept 12 Mid Semester Break 1st Oct 13 Conducting ANOVA and using Minitab 8th Oct 14 Case studies based on all statistical tests 15th Oct 15 Continue with previous week. 22nd Oct 16 Revision 29th Oct 17 Exams

Learning Resources

Prescribed Texts

References

Other Resources

Handouts on MyStudies RMIT

Class room activities

Overview of Assessment

Assessments for this course typically consist of:
• Class activities
• Assignments
• Presentations
• Exams

The following tasks will be conducted during the semester:

• Two assignments (10% each)
• Mid sem exam(30%)
• Minitab exam(10%)
• End of sem exam(40%)

(More details will be provided during the classes)

Assessment Matrix

Course Overview: Access Course Overview