Jeremiah
School of Information and Physical Sciences
OVERVIEW
Course Description Statistics provides us with a quantitative framework to utilise data
for describing, summarising, and modelling the world around us.
Engineering statistics combines engineering and statistics using
scientific methods for analysing data. This course introduces
students to the fundamental concepts of probability, random
variables and their distributions, and shows how these ideas
provide the theoretical foundation for data analysis through
statistical modelling, estimation and hypothesis testing with a
major emphasis on applications in electrical engineering and
computer systems. On completion of this course students will be
able to apply statistical theory to make informed decisions and
predictions relevant to engineering.
Academic Progress Nil
Requirements
Requisites This course has similarities to STAT1300 and STAT2010. If you
have successfully completed STAT1300 or STAT2010 you
cannot enrol in this course.
Assumed Knowledge MATH1110 Mathematics for Engineering, Science and
Technology 1
OR
MATH1120 Mathematics for Engineering, Science and
Technology 2
OR
MATH1210 Mathematical Discovery 1
OR
MATH1220 Mathematical Discovery 2
Knowledge of and experience in Python
Contact Hours Callaghan
Computer Lab
Face to Face On Campus
2 hour(s) per week(s) for 13 week(s) starting Week 2
Lecture
Face to Face On Campus
2 hour(s) per week(s) for 13 week(s) starting Week 1
Tutorial
Face to Face On Campus
2 hour(s) per week(s) for 13 week(s) starting Week 1
CRICOS Provider 00109J
Unit Weighting 10
Workload Students are required to spend on average 120-140 hours of
effort (contact and non-contact) including assessments per 10
unit course.
, STAT2110: Engineering Statistics
Callaghan Semester 1 - 2024
CONTACTS
Course Coordinator Callaghan
Dr Kirill Glavatskiy
Consultation: TBA on Canvas
Teaching Staff Other teaching staff will be advised on the course Canvas site.
School Office School of Information and Physical Sciences
SR233, Social Sciences Building
Callaghan
+61 2 4921 5513
9am-5pm (Mon-Fri)
SYLLABUS
Course Content The course will include the following topics:
• Sample space, events, axioms of probability and Bayes’ theorem
• Random variables and their distributions: Univariate
• Expected values and their properties
• Functions of random variables
• Vector and matrix calculations
• Random vectors and joint distributions: Multivariate
• Samples, sampling distributions and Central Limit Theorem
• Hypothesis testing
• Estimation
• Simple linear regression models
• Monte Carlo Simulation
Course Learning On successful completion of this course, students will be able to:
Outcomes 1. Explain the basic concepts underlying probability and hypothesis testing.
2. Explain the underlying assumptions and the applicability of each of the approaches studied.
3. Apply statistical models and statistical concepts including probability and hypothesis testing
to solve engineering problems.
4. Apply linear algebra concepts and methods to statistical models.
5. Demonstrate an enhanced analytical ability.
Course Materials With the exception of the following texts, all course materials will be provided to students via
Canvas.
Required Text:
R.E. Walpole, R.H. Myers, S.L. Myers and K.E. Ye, Probability & Statistics for Engineers &
Scientists, Pearson, 9 Global Ed., U.K., 2017
Recommended Text:
J.L. Devore, Probability and Statistics for Engineering and the Sciences, Cengage Learning, 9
Ed., Boston, 2016.
D.C. Montgomery, G.C. Runger and N.F. Hubele, Engineering Statistics, Wiley, 5 Ed., New
York, 2010.
Page 2 of 5
School of Information and Physical Sciences
OVERVIEW
Course Description Statistics provides us with a quantitative framework to utilise data
for describing, summarising, and modelling the world around us.
Engineering statistics combines engineering and statistics using
scientific methods for analysing data. This course introduces
students to the fundamental concepts of probability, random
variables and their distributions, and shows how these ideas
provide the theoretical foundation for data analysis through
statistical modelling, estimation and hypothesis testing with a
major emphasis on applications in electrical engineering and
computer systems. On completion of this course students will be
able to apply statistical theory to make informed decisions and
predictions relevant to engineering.
Academic Progress Nil
Requirements
Requisites This course has similarities to STAT1300 and STAT2010. If you
have successfully completed STAT1300 or STAT2010 you
cannot enrol in this course.
Assumed Knowledge MATH1110 Mathematics for Engineering, Science and
Technology 1
OR
MATH1120 Mathematics for Engineering, Science and
Technology 2
OR
MATH1210 Mathematical Discovery 1
OR
MATH1220 Mathematical Discovery 2
Knowledge of and experience in Python
Contact Hours Callaghan
Computer Lab
Face to Face On Campus
2 hour(s) per week(s) for 13 week(s) starting Week 2
Lecture
Face to Face On Campus
2 hour(s) per week(s) for 13 week(s) starting Week 1
Tutorial
Face to Face On Campus
2 hour(s) per week(s) for 13 week(s) starting Week 1
CRICOS Provider 00109J
Unit Weighting 10
Workload Students are required to spend on average 120-140 hours of
effort (contact and non-contact) including assessments per 10
unit course.
, STAT2110: Engineering Statistics
Callaghan Semester 1 - 2024
CONTACTS
Course Coordinator Callaghan
Dr Kirill Glavatskiy
Consultation: TBA on Canvas
Teaching Staff Other teaching staff will be advised on the course Canvas site.
School Office School of Information and Physical Sciences
SR233, Social Sciences Building
Callaghan
+61 2 4921 5513
9am-5pm (Mon-Fri)
SYLLABUS
Course Content The course will include the following topics:
• Sample space, events, axioms of probability and Bayes’ theorem
• Random variables and their distributions: Univariate
• Expected values and their properties
• Functions of random variables
• Vector and matrix calculations
• Random vectors and joint distributions: Multivariate
• Samples, sampling distributions and Central Limit Theorem
• Hypothesis testing
• Estimation
• Simple linear regression models
• Monte Carlo Simulation
Course Learning On successful completion of this course, students will be able to:
Outcomes 1. Explain the basic concepts underlying probability and hypothesis testing.
2. Explain the underlying assumptions and the applicability of each of the approaches studied.
3. Apply statistical models and statistical concepts including probability and hypothesis testing
to solve engineering problems.
4. Apply linear algebra concepts and methods to statistical models.
5. Demonstrate an enhanced analytical ability.
Course Materials With the exception of the following texts, all course materials will be provided to students via
Canvas.
Required Text:
R.E. Walpole, R.H. Myers, S.L. Myers and K.E. Ye, Probability & Statistics for Engineers &
Scientists, Pearson, 9 Global Ed., U.K., 2017
Recommended Text:
J.L. Devore, Probability and Statistics for Engineering and the Sciences, Cengage Learning, 9
Ed., Boston, 2016.
D.C. Montgomery, G.C. Runger and N.F. Hubele, Engineering Statistics, Wiley, 5 Ed., New
York, 2010.
Page 2 of 5
