Course Outline

 Course : FC 21342 – Inferential Statistics and Numerical Methods Core/Specialization : Core Programme : Bachelor of Sciences Honours in Surveying Sciences Department : Surveying & Geodesy Faculty : Faculty of Geomatics Contact Hours : 150 Year : II Semester : I Prerequisites : FC11216, FC11221, FC12225 Lecturer : Dr T. D. A. Gomesz Room No. : SF-14 Telephone No. : 045-3453071 E-mail

Synopsis

This course is to:

• Enable the student to understand the theoretical and practical aspects of the foundation of statistical inference
• Enable the student to project the findings from a sample to the population and communicating the results to the general public
• Introduce numerical techniques implemented in MATLAB for the solution of in problems in Geomatics, topics covered include an introduction to MATLAB, error analysis, interpolation and curve fitting, and numerical solutions to systems of linear equations.

Contents

• Random Variables
• Properties of estimators
• Methods of point estimation
• Interval estimation
• Testing statistical hypotheses
• Applications
• Introduction and error analysis
• Solving system of linear equations
• Solutions of non-linear equations
• Curve and surface fitting by approximating functions
• Numerical differentiation and integration

Learning Outcomes

By the end of the course, students should be able to:

No.

Course Learning Outcome

Programme Outcome

Assessment Methods

1.

Estimate population parameters based on sample data and assess the reliability of the estimates.

P01, P03

FE/CA

2.

Formulate relevant statistical hypotheses to solve practical problems, test them, draw appropriate conclusions, and communicate the findings effectively

P01, P03

FE/CA/Lab Asignment

3.

Use statistical software to calculate the estimates of the population parameters, construct confidence intervals, and test statistical hypotheses

P01, P03,P05

FE/CA/Lab Asignment

4.

Understand and estimate errors due to round-off and truncation, error propagation and numerical instability

P01, P02

FE/CA

5.

Perform data analysis using interpolation, extrapolation, and curve-fitting, including quantification of the degree of fit using fundamental algorithms in numerical methods

P01, P02

FE/CA/Lab Asignment

6.

Solve linear systems of equations and find approximate roots of non-linear equations using techniques used for the analysis of numerical algorithm

P01, P02, P05

FE/CA/Lab Asignment

7.

Ability to implement numerical algorithms efficiently in MATLAB to visualize data and to solve problems

P02,  P05

FE/CA/Lab Asignment

8.

Demonstrate the communication skills to interpret numerical results

P02, P03, P07

CA/Lab Asignment

Student Learning Time (SLT)

 Teaching and Learning Activities Student Learning Time (hours) Directed Learning Lectures and  Student-Centered Learning (SCL) 30 Computer Lab Practical 30 Independent Learning Home work assignments(HW) 20 Lab Assignments 20 Preparation- SCL activities 10 Final Report(preparation ) 10 Revision 12 Assessment Assignments/ Lab Assignments 15 Final Report(presentation) 01 Final Examination 02 TOTAL (SLT) 150

Teaching Methodology

 Lecture/presentations based on case studies/journal papers/classroom discussions.Discussions are both individual and group assignments.

Reference

• Introduction to Probability Theory, Paul G. Hoel, Sydney C. Port, and Charls J. Stone, Haughton Mifflin, Boston 1971.
• A basic course in Statistics, Fourth Edition, G. M. Clark and D. Cooke, Arnold, London, 1998.
• Technical Analysis and Applications with MATLAB, William D Stanley, Cengage Learning, 2004.

Final Report

Students should demonstrate ability of the material learned in the course unit by completing a project, writing a formal report, and making a presentation