• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Gain the ability in evaluation of geodetic observations using statistical methods and analysis of the results produced.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
   2. Acquire scientific knowledge
   3. Design and conduct research projects independently

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Use statistical tools and models for the problems that he or she will encounter

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
   2. Formulate and solve advanced engineering problems
   3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   4. Acquire scientific knowledge
   5. Design and conduct research projects independently

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Develop a statistical model

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
   2. Formulate and solve advanced engineering problems
   3. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
   4. Acquire scientific knowledge

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Introduction to probability, Distribution of random variables, Independent variables.
Week 2:	expected values and moments of random variables, Covariance matrix, error propagation, correlation and weight matrices.
Week 3:	Normal distribution, Special distributions, Estimation of table values for normal distribution using MATLAB software package.
Week 4:	Gamma distribution, Beta distribution, Multivarite normal distribution, Independence of normally distributed random variables.
Week 5:	Lineer functions of normally distributed random variables, sum of normally distributed random variables.
Week 6:	Test distributions, Chi-square distribution, F distribution, table values for F distribution using MATLAB software package.
Week 7:	t distribution, Quadratic forms, distribution of quadratic forms, Independence of lineer and quadratic forms.
Week 8:	Parametre estimations in linear models, Point esti,mation, Best unbiased estimation, Method of Least Squares, Maxiumum likelihood method.
Week 9:	Exam
Week 10:	Gauss-Markoff model, Definition an linearization, Distribution of adjustment results in Gauss-Markoff models.
Week 11:	Gauss-Markoff model with constraints.
Week 12:	Hypothesis testing for Gauss-Markoff models with constraints.
Week 13:	Outlier detection methods, An application with a sample data set using MATLAB.
Week 14:	Deformation analysis, Outlier detection for pairs and groups of observations.
Week 15*:	General review.
Week 16*:	Final exam
Textbooks and materials:
Recommended readings:	Koch, K. R., 1988, Parameter Estimation and Hypothesis Testing in Linear Models, Berlin, Springer-Verlag. Mikhail, E. M., 1976, Observations and Least Squares, New-York, Dun-Donnelley Publisher. Rao, C. R., Mitra, S. K., 1971, Generalized Inverse of Matrices and its Applications, New-York, John Wiley and Sons.
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	9	40
Other in-term studies:		0
Project:		0
Homework:		0
Quiz:		0
Final exam:	16	60
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	3	14
Practice, Recitation:	0	0
Homework:	6	10
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	15	1
Mid-term:	1	1
Personal studies for final exam:	10	2
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)