The University of Newcastle, Australia
Available in 2019

Course handbook

Description

Introduces key areas of statistical theory directly relevant to Engineering.

This course develops 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.


Availability2019 Course Timetables

Callaghan

  • Semester 2 - 2019

Learning outcomes

On successful completion of the course students will be able to:

1. Understand the basic concepts underlying probability and hypothesis testing.

2. Explain the underlying assumptions and the applicability of each of the approaches studied.

3. Use 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.


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

Requisite

This course has similarities to STAT2010. If you have successfully completed STAT2010 you cannot enrol in this course.


Assumed knowledge

STAT2110Engineering StatisticsIntroduces key areas of statistical theory directly relevant to Engineering.

This course develops 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.FSCITFaculty of Science724School of Mathematical and Physical Sciences1020005980Semester 2 - 2019CALLAGHANCallaghan2019MATH1110 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 2The 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 YOn successful completion of this course, students will be able to:1Understand the basic concepts underlying probability and hypothesis testing.2Explain the underlying assumptions and the applicability of each of the approaches studied.3Use statistical models and statistical concepts including probability and hypothesis testing to solve engineering problems.4Apply linear algebra concepts and methods to statistical models.5Demonstrate an enhanced analytical ability. This course has similarities to STAT2010. If you have successfully completed STAT2010 you cannot enrol in this course.Quiz: In-class quizzesFormal Examination: ExaminationWritten Assignment: Written Assessment CallaghanComputer LabFace to Face On Campus2hour(s)per Week for0Full Term1LectureFace to Face On Campus2hour(s)per Week for0Full Term1


Assessment items

Quiz: In-class quizzes

Formal Examination: Examination

Written Assignment: Written Assessment


Contact hours

Callaghan

Computer Lab

Face to Face On Campus 2 hour(s) per Week for Full Term starting in week 1

Lecture

Face to Face On Campus 2 hour(s) per Week for Full Term starting in week 1