# 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: 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 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

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.

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

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 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