# Course Title: Analyse data and report results

## Part B: Course Detail

Teaching Period: Term1 2011

Course Code: ISYS7548L

Course Title: Analyse data and report results

School: 155T Life & Physical Sciences

Campus: City Campus

Program: C6068 - Advanced Diploma of Computer Science

Course Contact : Raymond Rozen

Course Contact Phone: +61 3 9925 4699

Course Contact Email:rar@rmit.edu.au

Name and Contact Details of All Other Relevant Staff

Michael Cobucci
Building 51, level 06, Room 04
+61 3 9925 4898
michael.cobucci@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

None

Course Description

Descriptive statistics, mean and standard deviation of both grouped and ungrouped data, mode, median, permutation and combinations, Probability, addition theorem mutually exclusive and independent events, conditional probability, Bayes Theorem Expectation, Binomial, Normal Distributions, Confidence Intervals ( large samples ),
The Central Limit Theorem, Confidence Intervals ( small samples ),  the student T distributions, Hypothesis Testing, decisions, null and alternative hypotheses, acceptance and rejection regions, one and two sided alternative significance levels. Two Sample Hypothesis Testing. Regression Analysis.

National Codes, Titles, Elements and Performance Criteria

 National Element Code & Title: PMLDATA500A Analyse Data,Report Results 02/2 Element: Analyse trends and relationships in data Performance Criteria: Element: Check for aberrant results Performance Criteria: Element: Determine variation and uncertainty in data distributions Performance Criteria: Element: Perform laboratory computations Performance Criteria: Element: Report results Performance Criteria:

Learning Outcomes

Details of Learning Activities

Learning Activities for this course may include:

• Teacher directed face-to face delivery of lessons
• Class discussions
• Pair/Group discussion
• Small group workshops
• Revision quizzes
• Worksheets
• Laboratory experiments
• Record keeping of experiments
• Presentations
• Research activities
• Mathematical problem solving
• Note taking / Data collection
• Graphing activities
• Use of calculator
• Use of computer, eg software programs and the Internet

Teaching Schedule

 WeekSemester 2 2010 Performance Criteria Section Title Topic Problem Set Week 1 (Class 1 - Semester 2) 1.1 Introduction to the Course ISYS7548L   Analyse trends and relationships in data Mean and Standard Deviation of grouped and ungrouped data Start Set 1A Set 1B Week 1 (Class 2 - Semester 2) 1.1 Analyse trends and relationships in data Mean and Standard Deviation of grouped and ungrouped data Set 1A Set 1B Week 2 (Class 1 - Semester 2) 1.2 Analyse trends and relationships in data Mode and Median Set 1C Set 1D Week 2 (Class 2 - Semester 2) 1.3 Analyse trends and relationships in data Permutation and combinations Set 1E Set 1F Week 3 (Class 1 - Semester 2) 1.4 Analyse trends and relationships in data Probability Set 1G Week 3 (Class 2 - Semester 2) 1.4 Analyse trends and relationships in data Probability Set 1G Week 4 (Class 1 - Semester 2) 1.5 Analyse trends and relationships in data Addition theorem mutually exclusive and independent events, conditional probability Set 1H Set 1I Set 1J Set 1K Week 4 (Class 2 - Semester 2) 1.5 Analyse trends and relationships in data addition theorem mutually exclusive and independent events, conditional probability Set 1H Set 1I Set 1J Set 1K Week 5 (Class 1 - Semester 2) 2.0 2.1 2.2 2.3 Check for aberrant results. Bayes Theorem Expectation Binomial, Normal Distributions Confidence Intervals (large samples ) Set 2A Set 2B Set 2C Set 2D Week 5 (Class 2 - Semester 2) 2.0 2.1 2.2 2.3 Check for aberrant results. Bayes Theorem Expectation Binomial, Normal Distributions Confidence Intervals (large samples) Set 2A Set 2B Set 2C Set 2D Week 6 (Class 1 - Semester 2) 2.0 2.1 2.2 2.3 Check for aberrant results. Bayes Theorem Expectation Binomial, Normal Distributions Confidence Intervals (large samples ) Set 2A Set 2B Set 2C Set 2D Week 6 (Class 2 - Semester 2) 2.0 2.1 2.2 2.3 Check for aberrant results. Bayes Theorem Expectation Binomial, Normal Distributions Confidence Intervals (large samples ) Set 2A Set 2B Set 2C Set 2D Week 7 (Class 1 - . Bayes 2.0 2.1 2.2 2.3 Semester 2) Check for aberrant results Theorem Expectation Binomial, Normal Distributions Confidence Intervals (large samples ) Set 2A Set 2B Set 2C Set 2D Week 7 (Class 2 - Bayes 2.0 2.1 2.2 2.3 Semester 2)  Check for aberrant results. Theorem Expectation Binomial, Normal Distributions Confidence Intervals (large samples ) Set 2A Set 2B Set 2C Set 2D Week 8 (Class 1 - Semester 2) 3.0 3.1 3.2 Determine variation and uncertainty in data distributions The Central Limit Theorem, Confidence Intervals ( small samples ), the student T distributions Set 3A Set 3B Set 3C Week 8 (Class 1 - Semester 2) 3.0 3.1 3.2 Determine variation and uncertainty in data distributions The Central Limit Theorem, Confidence Intervals ( small samples ), the student T distributions Set 3A Set 3B Set 3C Week 9 (Class 1 - Semester 2) The Central Limit Theorem, 3.0 3.1 3.2 Determine variation and uncertainty in data distributions Confidence Intervals ( small samples ), the student T distributions Set 3A Set 3B Set 3C Week 9 (Class 2 - Semester 2) The Central Limit Theorem, 3.0 3.1 3.2 Determine variation and uncertainty in data distributions Confidence Intervals ( small samples ), the student T distributions Set 3A Set 3B Set 3C Week 10 (Class 1 - Semester 2)    The Central Limit Theorem, 3.0 3.1 3.2 Determine variation and uncertainty in data distributions Confidence Intervals ( small samples ), the student T distributions Set 3A Set 3B Set 3C Week 10 (Class 2 - Semester 2)  The Central Limit Theorem, 3.0 3.1 3.2 Determine variation and uncertainty in data distributions Confidence Intervals ( small samples ), the student T distributions Set 3A Set 3B Set 3C Week 11 (Class 1 - Semester 2 4.0 4.1 4.2 Perform laboratory computations Hypothesis Testing Decisions Null and alternative hypothesis Set 4A Set 4B Set 4C Week 11 (Class 2 - Semester 2) 4.0 4.1 4.2 Perform laboratory computations Hypothesis Testing Decisions Null and alternative hypothesis Set 4A Set 4B Set 4C Week 12 (Class 1 - Semester 2) 4.0 4.1 4.2 Perform laboratory computations Hypothesis Testing Decisions Null and alternative hypothesis Set 4A Set 4B Set 4C Week 12 (Class 2 - Semester 2) 4.0 4.1 4.2 Perform laboratory computations Hypothesis Testing Decisions Null and alternative hypothesis Set 4A Set 4B Set 4C Week 13 (Class 1 - Semester 2) 4.0 4.1 4.2 Perform laboratory computations Hypothesis Testing Decisions Null and alternative hypothesis Set 4A Set 4B Set 4C Week 13 (Class 2 - Semester 2) 4.0 4.1 4.2 Perform laboratory computations Hypothesis Testing Decisions Null and alternative hypothesis Set 4A Set 4B Set 4C Week 14 (Class 1 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D Week 14 (Class 2 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D Week 15 (Class 1 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D Week 15 (Class 2 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D Week 16 (Class 1 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D Week 16 (Class 2 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D Week 17 (Class 1 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D Week 17 (Class 2 - Semester 2) 5.0 5.1 5.2 Report Results Acceptance and rejection regions One and two sided alternative significance levels Two Sample Hypothesis Testing. Regression Analysis Set 5A Set 5B Set 5C Set 5D REVIEW/ REVISION END OF SEMESTER EXAM END OF SEMESTER 2

Learning Resources

Prescribed Texts

 There is no prescribed textbook for this course. Class notes and sets of problem booklets will be handed out to students.

References

 Any first year text, or Mathematical Methods Year 11 or 12 textbook, or most first year texts on Probability and Statistics.

Other Resources

Overview of Assessment

The student must demonstrate an understanding of all elements of a particular competency to be deemed competent. Assessment methods have been designed to measure achievement of each competency in a flexible manner over a range of assessment tasks.
Assessment will incorporate a variety of methods including written tests, assignments and a final exam.

Assessment Tasks will consist of Tests and an end of semester Exam.

In class tests: 50%

Exam 1
End of Semester 2, November – Exam: 50%

TOTAL = 100%

Assessment Matrix

 Element 1 Element 2 Element 3 Element 4 Test √ √ √ √ Exam √ √ √ √

Other Information

Further Support

Additional RMIT study and support can be obtained from the Study and Learning Centre (SLC). Further information can be obtained via the following website:
www.rmit.edu.au/studyandlearningcentre

The SLC can also be contacted on 9925 3600.
The SLC can also be contacted via the E-mail learning query service.

University Plagiarism Statement

Students are reminded that cheating, whether by fabrication, falsification of data, or plagiarism, is an offence subject to University disciplinary procedures. Plagiarism in oral, written or visual presentations is the presentation of the work, idea or creation of another person, without appropriate referencing, as though it is one’s own. Plagiarism is not acceptable. The use of another person’s work or ideas must be acknowledged. Failure to do so may result in charges of academic misconduct, which carry a range of penalties including cancellation of results and exclusion from your course. Students are responsible for ensuring that their work is kept in a secure place. It is also a disciplinary offence for students to allow their work to be plagiarised by another student. Students should be aware of their rights and responsibilities regarding the use of copyright material.

Course Overview: Access Course Overview