-- Main.yauhee - 14 Feb 2025

XMUT321 Engineering Statistics (2025, T1)

Course Outline

Prescription

The course introduces the fundamentals of engineering statistics. Topics include probability mass and density functions, random variables and functions of random variables, confidence intervals and statistical tests, as applied to engineering problems.

Learning Objectives

Students who pass this course should be able to:

* Identify random variables and use them to model observations in engineering applications.

* Apply statistical tests and compute confidence intervals for observed data.

* Use the Matlab programming language to solve problems in statistics encountered by engineers.

* To select an appropriate standard family of probability mass or density functions, and estimate its parameters.

A schedule of lecture topics and assignment dates is on the website. Copies of the lecture slides will be available online.

The computer lab work for the course will take place during the scheduled Lab times, but we expect that you will need approximately 6 hours per week of additional work to complete the homework assignments and prepare for the tests and exam. We hope to provide some additional time in the computer lab, but much of the time this will need to be done on your own computer.

Teaching Schedule

Please see here https://ecs.wgtn.ac.nz/Courses/XMUT321_2025T1/LectureSchedule

Textbook and other Materials

To be able to follow the course the student will need the textbook cited below.

* W. Navidi, Statistics for Engineers and Scientists. McGraw-Hill.

Workload

The course is expected to have a total workload of about 150 hours, to be conducted in 8 weeks. If you are still struggling with English, you may need to spend more time than this. This means you should expect to work on this course for about 20 hours or more every week.

Staff

The staff for the course are

* Dr Yau Hee Kho (yauhee.kho_@_vuw.ac.nz)

* Dr Wei Heng Zheng (XMUT co-teacher)

Assignments and Laboratory (and Tutorials)

There will be frequent tutorial exercises as part of the course. These will consist of exercises to ensure you understand how to use the key concepts introduced in the lectures. There will be homework assignments through the course, as well as laboratory exercises to help you understand the concepts introduced in lectures. You will generally work on these individually.

Assignment Submission

Online submission can be found here.;

Assignment Marking and Late Penalties

The assignments (homework and labs) are very important for your learning, and will together contribute a total of 40% to your final grade.

We will mark the assignments as quickly as possible.

You are strongly encouraged to attempt and submit all the assignments on time. If you miss an assignment, contact the lecturer as soon as possible. Penalty of 10% deduction per working day will be applied for any late submission without excuses. After 1 week, no submission will be entertained.

Note that the marks from all assignments will be included in the event of RESIT exam.

Model solutions to the tutorials will be posted from time to time, so that you can review and assess your own work, and also build on the model solutions for the next assignment. Comparing your work to the provided solutions is an important part of the learning.

Working Together

We encourage you to discuss the exercises together. However, you must submit the work and reports on your own, i.e. do not copy from whoever you have discussed with.

Make sure you read the section on plagiarism below.

Tests and Exam

There will be one test worth 10%, held during the course.

You should contact the lecturer as early as possible if you are not going to be able to attend a test at the scheduled time, or if you missed a test.

There will be an exam at the end of the course, worth 40%.

All the assessments (assignments, tests, and exam) will address the learning objective of the course. The test and exam will assess all the material covered by the course up to the time of the test/exam.

Grade Computation

Your grade for the course will be based on a combined mark for the assignments, the tests, and the exam:

Item Weight
4 Homework 5% each = 20% in total
2 Labs 10% each = 20% in total
1 Test 10%
Examination 40%
Attendance 10%

Academic Integrity and Plagiarism

Academic integrity means that university staff and students, in their teaching and learning are expected to treat others honestly, fairly and with respect at all times. It is not acceptable to mistreat academic, intellectual or creative work that has been done by other people by representing it as your own original work.

Academic integrity is important because it is the core value on which the University's learning, teaching and research activities are based. The University's reputation for academic integrity adds value to your qualification.

Plagiarism is presenting someone else's work as if it were your own, whether you mean to or not. "Someone else's work" means anything that is not your own idea. Even if it is presented in your own style, you must acknowledge your sources fully and appropriately. This includes:

• Material from books, journals or any other printed source

• The work of other students or staff

• Information from the internet

• Software programs and other electronic material

• Designs and ideas

• The organisation or structuring of any such material

A video on Academic Success and Academic Honesty can be found here https://www.bilibili.com/video/BV1nb4y117QN/ You are strongly encouraged to watch and understand the contents of the video.

Notes (on plagiarism)

The following notes are intended to help students and staff understand what is and is not acceptable under this policy and what happens when plagiarism is suspected.

You should always properly cite any work of others that you are including in work that you submit. Some guidelines on how to do this will be found at the end of this document.

When you use someone else's work in an assignment you should be certain that you are making appropriate use of that work. While citing the work may avoid any question of plagiarism, failure to do the work yourself may mean that the submitted work fails to meet some or all of the requirements of a particular assignment. If in doubt ask your lecturer.

Do not lend your work to others. If someone submits work that is the same as or very similar to yours you should expect to be asked to explain and, if the explanation is not satisfactory, to be penalised.

If you are ever in doubt as to whether some action you have taken may be considered as plagiarism, you should consult your lecturer and/or clearly state on the submitted work the extent of the contribution from others.

Plagiarism and Code

If you are completing a programming project, you may be allowed to use code segments from a software library on the web, from model solutions in previous courses you have taken, or even from other students. If you do this, you must clearly indicate all of the code that has come from another source, and state the source.

Unless your course requirements state otherwise, you are not required to cite algorithms, data structures or source code provided with the assignment or from lecture notes.

If you are in doubt about the use of code that you have not written yourself you should check with your lecturer before submitting the program. If you have had help from someone else (other than a tutor), it is always safe to state the help that you got. For example, if you had help from someone else in writing a component of your code, it is not plagiarism as long as you state (eg, as a comment in the code) who helped you in writing the method.

Classroom Policies

* No eating, drinking, or smoking.

* Respect classmates’ ideas, opinions, and questions.

* No behaviour that prevents other students from learning.

* Take good care of the laboratory facilities.

System Management: Set up ECS account, Change password, Report a problem

For Staff:

Marking system; password reset;
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