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

Week
Semester 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

All students will need a scientific or graphic calculator. Access to a computer would be advantageous


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

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