Survey And Cadastre
Associate	TR-NQF-HE: Level 5	QF-EHEA: Short Cycle	EQF-LLL: Level 5

General course introduction information

Course Code:

MHKD126

Course Name:

Introduction to Statistics

Course Semester:

Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	6

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Compulsory

Course Level:

Associate

TR-NQF-HE:5. Master`s Degree

QF-EHEA:Short Cycle

EQF-LLL:5. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Öğr.Gör. BAYRAM KILINÇ

Course Lecturer(s):

Öğr.Gör. BAYRAM KILINÇ

Course Assistants:

Course Objective and Content

Course Objectives:	The aim of this course is to gain the ability to learn and apply the basic definitions and calculations of probability and statistics.
Course Content:	General Concepts, Averages, Regression-Correlation, Normal Distribution, Random Variables, Hypothesis Tests, Confidence Intervals

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) General concepts of probability and statistics
2 - Skills
Cognitive - Practical
1) Understand and comprehend the concept of probability
3 - Competences
Communication and Social Competence
1) To be able to apply the forecasting topic
Learning Competence
Field Specific Competence
1) To be able to apply the hypothesis tests
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Introduction to Statistics, Definition of Statistics, Basic Concepts	Lecture Notes
2)	Summarizing Digital Information, Series Types (Time, Space, Division), Graphical Representation of Series	Lecture Notes
3)	Measures of Central Tendency (Sensitive Averages)	Lecture Notes
4)	Measures of Central Tendency (Non-Sensitive Means)	Lecture Notes
5)	Probability Theory	Lecture Notes
6)	Discrete Random Variables and Probability Distributions (Binomial - Poisson Distributions)	Lecture Notes
7)	Continuous Random Variables and Probability Distributions (Normal Distributions)	Lecture Notes
7)	Continuous Random Variables and Probability Distributions (Normal Distributions)	Lecture Notes
8)	Midterm Exam
9)	Sampling and Sampling Distributions	Lecture Notes
10)	Statistical Forecasting (Point and Interval Estimates)	Lecture Notes
11)	Hypothesis Testing	Lecture Notes
12)	Chi-Square Test	Lecture Notes
13)	Simple Linear Regression	Lecture Notes
14)	Simple and partial correlation	Lecture Notes
15)	Final Exam
16)	Final Exam

Sources

Course Notes / Textbooks:	Lecture Notes
References:	Hoca ders notu

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4
Program Outcomes
1) He/she knows main topics about profession with also has knowledge and awareness about social resposibility, ethical values and princibles of profession.
2) He/she has knowledge about basic mathematichal computing and problem solving about the issues of profession.
3) He/ she has knowledge about algorithm, basic programming, data structures and types, database management systems spatial database for operation of the software which are required by profession.
4) He/she has knowledge about all measurement, calculation, and exercising in the fields of survey and cadastre.
5) He/she has knowledge about legislation, ability of follow the changes and has command of the process in the fields of survey and cadastre.
6) He/she has ability to use technological tools and programs, know working and scientific principles of these technology that existed in profession area
7) He/she knows main topics, expertising, legislation and urban reneval issues about real estate.
8) He/she has command of application all kinds of engineering project to area.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	He/she knows main topics about profession with also has knowledge and awareness about social resposibility, ethical values and princibles of profession.	5
2)	He/she has knowledge about basic mathematichal computing and problem solving about the issues of profession.	5
3)	He/ she has knowledge about algorithm, basic programming, data structures and types, database management systems spatial database for operation of the software which are required by profession.	5
4)	He/she has knowledge about all measurement, calculation, and exercising in the fields of survey and cadastre.	3
5)	He/she has knowledge about legislation, ability of follow the changes and has command of the process in the fields of survey and cadastre.	4
6)	He/she has ability to use technological tools and programs, know working and scientific principles of these technology that existed in profession area	3
7)	He/she knows main topics, expertising, legislation and urban reneval issues about real estate.	4
8)	He/she has command of application all kinds of engineering project to area.	4

Learning Activity and Teaching Methods

Peer Review
Brainstorming/ Six tihnking hats
Individual study and homework
Lesson
Homework
Problem Solving
Q&A / Discussion
Web Based Learning

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework
Application
Observation
Bilgisayar Destekli Sunum

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Workload
Course Hours	14	42
Application	4	12
Study Hours Out of Class	14	42
Homework Assignments	11	33
Quizzes	5	15
Preliminary Jury	2	9
Final	2	12
Total Workload		165