33290 Statistics and Mathematics for Science
Warning: The information on this page is indicative. The subject outline for a
particular semester, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2015 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 33190 Mathematical Modelling for Science OR 33130 Mathematical Modelling 1
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 33230 Mathematical Modelling 2 AND 35101 Introduction to Linear Dynamical Systems AND 35102 Introduction to Analysis and Multivariable Calculus AND 35151 Introduction to Statistics AND 37131 Introduction to Linear Dynamical Systems AND 37132 Introduction to Mathematical Analysis and Modelling AND 37151 Introduction to Data Analysis
Description
This subject covers studies of simultaneous linear equations and their occurrence in scientific problems; methods for solving these equations using matrices and determinants; eigenvalues and eigenvectors; vectors in two and three dimensions; products of vectors; spatial geometry and coordinate systems; functions of several variables; partial derivatives; optimisation and method of least squares; probability with a focus on the determination of the reliability of a system of components in various engineering contexts; variance, skewness and kurtosis; and probability distributions, conditional probability and bi-variate probability. The computer algebra system Mathematica is used throughout the subject as an aid to computation, graph plotting and visualisation.
Subject objectives
Upon successful completion of this subject students should be able to:
| 1. | understand the way in which probability can supply useful tools and resources to model real world problems |
|---|---|
| 2. | use the terminology and concepts of probability |
| 3. | use formal and informal language to demonstrate understanding of these concepts |
| 4. | demonstrate knowledge of all assumptions underlying probability techniques |
| 5. | demonstrate a high level of skill in checking whether the assumptions underlying probability techniques are satisfactory in particular situations |
| 6. | use the computer system Minitab to perform calculations and explore statistical ideas relevant to the subject content. |
| 7. | understand the way in which mathematics can provide useful tools and resources to real world problems |
| 8. | use mathematical terminology and concepts |
| 9. | use formal and informal language to demonstrate understanding of these concepts |
| 10. | demonstrate a satisfactory level of skill in the computational techniques covered in the subject content |
| 11. | use the computer system Mathematica to perform calculations and explore mathematical ideas relevant to the subject content |
| 12. | be aware of the historical context of mathematical development. |
| 13. | communicate the above knowledge clearly, logically and critically |
| 14. | apply the subject matter covered in lectures, tutorials, laboratories and assignments to previously unseen problems. |
This subject also contributes specifically to the development of following course intended learning outcomes:
- An understanding of the nature, practice and application of the chosen science discipline. (1.0)
- Encompasses problem solving, critical thinking and analysis attributes and an understanding of the scientific method knowledge acquisition. (2.0)
- The ability to acquire, develop, employ and integrate a range of technical, practical and professional skills, in appropriate and ethical ways within a professional context, autonomously and collaboratively and across a range of disciplinary and professional areas, e.g. time management skills, personal organisation skills, teamwork skills, computing skills, laboratory skills, data handling, quantitative and graphical literacy skills. (3.0)
- The capacity to engage in reflection and learning beyond formal educational contexts that is based on the ability to make effective judgments about one?s own work. The capacity to learn in and from new disciplines to enhance the application of scientific knowledge and skills in professional contexts. (4.0)
- An awareness of the role of science within a global culture and willingness to contribute actively to the shaping of community views on complex issues where the methods and findings of science are relevant. (5.0)
- An understanding of the different forms of communication - writing, reading, speaking, listening -, including visual and graphical, within science and beyond and the ability to apply these appropriately and effectively for different audiences. (6.0)
- An ability to think and work creatively, including the capacity for self-starting, and the ability to apply science skills to unfamiliar applications. (7.0)
Contribution to the development of graduate attributes
This subject contributes to the development of the following graduate attributes:
1. Disciplinary knowledge and its appropriate application.
2. An Enquiry-oriented approach.
3. Professional skills and their appropriate application.
6. Communication skills.
7. Initiative and innovative ability.
his subject provides the disciplinary knowledge and skills for the analysis of data which can be gathered in experimental situations in a wide variety of sciences. These technical skills are evaluated through the problems in the tutorial and laboratory classes. It also emphasises the need to critically evaluate the nature of the data in order to ensure that appropriate statistical techniques are used and to report the results of the statistical analysis in appropriate ways. These aspects are examined in the assignments which present data sets for analysis but leave the students to determine the appropriate methods of analysis. These assignments can be completed by students in groups in order to develop communication skills and teamwork skills including time management and organisation skills.
Teaching and learning strategies
Spring Session:
Lectures: 3 hrs per week
Tutorials: 1hr per week
Computer laboratories: 1hr per week
Summer Session:
Lectures: Three 120 minute lectures per week in December and two 120 minute lectures per week in January
Tutorials: Three 60 minutes tutorials per week in December and two 60 minutes tutorials per week in January
Computer labs: Five drop-in sessions in the computer lab during the semester
Content
The major topics covered in this subject are:
- Data collection - methods and limitations, Data analysis, Statistical estimation and Critical about data-based claims
- Linear modelling and operations with matrices, eigenvalues and eigenvectors, functions of several variables and an introduction to their calculus, an introduction to unconstrained and constrained optimisation techniques.
Assessment
Assessment task 1: Statistics - Weekly Exercises through WileyPLUS
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge and its appropriate application. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.0, 2.0 and 3.0 |
| Type: | Exercises |
| Weight: | 10% |
| Criteria: | Accuracy of analysis, clarity of communication. |
Assessment task 2: Statistics ? Critiquing task
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge and its appropriate application. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 2, 3 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.0, 3.0, 4.0 and 6.0 |
| Type: | Exercises |
| Weight: | 5% |
| Criteria: |
|
Assessment task 3: Statistics ? Presentation task
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge and its appropriate application. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.0, 3.0 and 6.0 |
| Type: | Presentation |
| Weight: | 10% |
| Criteria: |
|
Assessment task 4: Mathematics ? Mastery Tests
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge and its appropriate application. |
|---|---|
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.0, 3.0 and 6.0 |
| Type: | Quiz/test |
| Weight: | 27% |
Assessment task 5: Mathematics ? Assignment
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge and its appropriate application. |
|---|---|
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.0, 2.0, 3.0, 5.0, 6.0 and 7.0 |
| Weight: | 5% |
| Criteria: | As provided in the marking rubric available online (These relate to the course intended learning outcomes). |
Assessment task 6: Statistics and Mathematics ? Examination
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge and its appropriate application. |
|---|---|
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.0, 2.0, 3.0, 6.0 and 7.0 |
| Type: | Examination |
| Weight: | 43% |
| Criteria: |
|
Minimum requirements
The final mark will be the addition of marks for all components of the assessment. Students must gain a combined mark of 50% or greater to pass the subject.
Student must obtain at least 40% of the marks available for the Statistics examination in order to pass this subject. If 40% is not reached, an X grade fail may be awarded for the subject, irrespective of an overall mark greater than 50.
Students must also obtain at least 80% of the available marks in at least one attempt of the available attempts of each mastery test to pass the subject. Students who have not achieved this by 5:00pm on the last day of the teaching period may have one further attempt. Those students doing so will not have marks achieved on the mathematics questions in the final exam counted towards their final result. (That is, students must demonstrate mastery of fundamental knowledge and skills.)
Recommended texts
Statistics component textbook
Robin H. Lock, Patti Frazer Lock, Kari Lock Morgan, Eric F. Lock, Dennis F. Lock.
Unlocking the Power of Data. Wiley, 2013.
You may want to buy a copy of the book yourself. If you do make sure that you are purchasing the book packaged with the WileyPLUS registration card so that you can access further resources at WileyPLUS. To do this either buy from the university bookshop who have ordered the correct stock as detailed below
Paperback
Statistics: Unlocking the Power of Data, 1st Edition Binder Ready Version + WileyPLUS Card
ISBN : 978-1-118-63197-3
Hardcover
Statistics: Unlocking the Power of Data, 1st Edition + WileyPLUS Card
ISBN : 978-1-118-56631-2
or purchase from WileyDirect.
http://www.wileydirect.com.au/buy/statistics-unlocking-power-data-1st-edition/
If you buy a copy of the book using the book ISBN only then you will not get a WileyPLUS access code and you will have to buy that separately (a more expensive option).
References
Devore, J.L. & Farnum, N.R. Applied Statistics for Engineers and Sciences, 2nd Ed. Cengage Learning, 2004.
Other resources
U:PASS
U:PASS (UTS Peer Assisted Study Success) is a voluntary “study session” where you will be studying the subject with other students in a group. It is led by a student who has previously achieved a distinction or high distinction in that subject, and who has a good WAM. The leader will typically prepare questions for you to work on, or if you have specific questions or things you’re not clear on, you can bring them along, and the leader will get the group to work on that. It’s really relaxed, friendly, and informal. Because the leader is a student just like you, they understand what it’s like to study the subject and how to do well, and they can pass those tips along to you. Students also say it’s a great way to meet new people and a “guaranteed study hour”.
You can sign up for U:PASS sessions in My Student Admin https://onestopadmin.uts.edu.au/. You’ll find it listed in the area where you sign up for lectures, tutorials, etc. Note that sign up is not open until week 1, as it’s voluntary and only students who want to go should sign up.
Note that you don’t have to be struggling in the subject to attend U:PASS – frequently students who are already doing well will do even better after attending U:PASS.
If you have any questions or concerns about U:PASS, please contact Georgina at upass@uts.edu.au, or check out the website: http://www.ssu.uts.edu.au/peerlearning/index.html
