Course Title: Statistics and Measurement Analysis

Part A: Course Overview

Course Title: Statistics and Measurement Analysis

Credit Points: 12.00


Course Coordinator: TBD

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: @rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

This course has no pre-requisites, but the knowledge associated with a passing grade in first year mathematics (MATH2211 or MATH2163 or equivalent) is assumed.


Course Description

This course is a second-year core component of the BH116 Bachelor of Applied Science (Surveying) (Honours) and BH117 Bachelor of Science (Geospatial Science) (Honours) programs. It establishes knowledge of the basic concepts in statistics and the analysis of surveying & geospatial data for later courses in the BH116 & BH117 programs.

The Statistics section provides a broad introductory knowledge of statistical methods in data analysis, with an emphasis on techniques that are useful in Geospatial science.

The Measurement Analysis section covers the application of least squares adjustment to generate statistically optimal results from measurement data and to assess measurement quality.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BH116 Bachelor of Applied Science (Surveying)(Honours) and BH117 Bachelor of Applied Science (Geospatial Science)(Honours)

1.1    Describe the fundamental and applied scientific knowledge that underpins Surveying and Geospatial Science.

1.2    Demonstrate in-depth understanding of the spatial models and mathematical methods used in contemporary practice.

1.3    Understand specialist bodies of knowledge in Surveying and Geospatial Science

2.1    Apply standard and advanced techniques to solve a range of measurement and data management problems.

3.1    Design and implement solutions to complex problems in survey measurement and land development.

3.2    Interpret and critically analyse results and make informed judgements on the appropriateness of solutions.

3.3    Apply critical and analytical skills in a scientific and professional manner.

4.1    Communicate effectively by means of oral, written and graphical presentations to peers and a wider audience.

6.3    Professionally manage and use information.


After completing this course, you will be able to:

  1. Describe the nature of random variables and probability distributions through direct calculation and computer simulation
  2. Perform statistical inference tasks using software (descriptive statics, t-test, hypothesis tests, confidence intervals, linear regression) and be able to state the assumptions necessary for the validity of results.
  3. Set up and complete a least squares adjustment of measurement data.
  4. Analyse measurements and results using least squares and statistical tests.   


Overview of Learning Activities

Learning will occur through a series of lectures, lab discussions, assignments, online and in-class tests.

 

Total study hours

Teacher guided hours: 48

Learner directed hours: 48


Overview of Learning Resources

Learning resources are available in the Library on the topics covered. References will be provided during the course. You will be able to access lecture notes, course information and assorted learning materials through Canvas.


Overview of Assessment

This course has no hurdle requirements.

 

Assessment Task 1: Statistics lab assessments

Weighting 15%

This assessment task supports CLOs 1, 2

 

Assessment Task 2: Statistics class tests

Weighting 35%

This assessment task supports CLOs 1, 2

 

Assessment Task 3: Least squares assignments

Weighting 20%

This assessment task supports CLOs 3, 4

 

Assessment Task 4: Least squares class tests

Weighting 30%

This assessment task supports CLOs 3, 4