At the end of this course, the students; 1) Can understand specific data considerations, regression models, time series and their patterns. 2) Can understand basic forecasting models from various time series data.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
NA
COURSE DEFINITION
COURSE CONTENTS
WEEK
TOPICS
1st Week
Covariance, Correlation Coefficients
2nd Week
Introduction to Regression
3rd Week
Simple Linear Regression Method
4th Week
Multiple Linear Regression
5th Week
Applying Multiple Linear Regression
6th Week
Performance Indicators
7th Week
Multiple Linear Relationship Problems
8th Week
Midterm
9th Week
Introduction to Time Series Analysis
10th Week
Moving Averages
11th Week
Exponential Smoothing
12th Week
Time Series and Regression Applications
13th Week
Time Series Analysis Computer Applications
14th Week
Time Series Analysis Computer Applications
RECOMENDED OR REQUIRED READING
Introduction to Time Series Analysis and Forecasting, Yaffee R. A. ve McGee, M. (2000) Regression Models for Time Series Analysis, Kedem, B., Fokianos, Konstantinos, (2002), (Wiley and Sons)
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Lecture,Discussion
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
40
Quiz
4
10
Total(%)
50
Contribution of In-term Studies to Overall Grade(%)
50
Contribution of Final Examination to Overall Grade(%)
50
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
2
2
Preparation for Quiz
4
2
8
Individual or group work
14
4
56
Preparation for Final exam
1
20
20
Course hours
14
3
42
Preparation for Midterm exam
1
17
17
Laboratory (including preparation)
Final exam
1
2
2
Homework
Quiz
4
1
4
Total Workload
151
Total Workload / 30
5,03
ECTS Credits of the Course
5
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
