TOROS ÜNİVERSİTESİ
Bilgi Paketi Ders Kataloğu

Course Information

FORECASTING METHODS
Code	Semester	Theoretical	Practice	National Credit	ECTS Credit
		Hour / Week
INE308	Spring	3	0	3	4

Prerequisites and co-requisites	None
Language of instruction	English
Type	Elective
Level of Course	Bachelor's
Lecturer	Assistant Professor Türker Ertem
Mode of Delivery	Face to Face
Suggested Subject	None
Professional practise ( internship )	None
Objectives of the Course	This course is an introduction to the widely used and effective methods of forecasting and regression. The aim is to introduce students ways to aid managerial decision-making by applying a statistical approach and quantitative analysis. The emphasis will be upon the use of mathematical methodology and the written communication of statistical results.
Contents of the Course	An introduction to forecasting. Basic statistical concepts. Regression Analysis: Simple linear regression. Multiple linear regression. Least squares estimates of parameters. Hypothesis testing and confidence intervals in linear regression models. Testing of models. Data analysis and appropriateness of models. Linear time series models. Moving average. Autoregressive and/or ARIMA models. Estimation, data analysis, and forecasting with time series models. Forecasting errors and confidence intervals.

Learning Outcomes of Course

#	Learning Outcomes
1	Students shall collect data for forecasting processes and analyses
2	Students shall analyse the relationship between the variabes
3	Students shall forecast the future by evaluating current variables
4	Students shall identify the form and direction of the relationship between the variables
5	Students shall make forcasting hypothesis

Course Syllabus

#	Subjects	Teaching Methods and Technics
1	An Introduction to Forecasting Case Studies to Bad Sampling Forecasting and Data, Forecasting Methods, Errors in Forecasting Choosing a Forecasting Technique An Overview of Quantitative Forecasting Techniques	lecturing, problem solving, discussing
2	A Review of Elementary Statistical Concepts Statistical Preliminaries Essential Probability Distributions The Central Limit Theorem	lecturing, problem solving, discussing
3	Confidence Intervals Large-Sample Confidence Intervals for a Population Mean Confidence Intervals for Proportions Small-Sample Confidence Intervals for a Population Mean	lecturing, problem solving, discussing
4	Confidence Intervals for the Difference Between Two Means Confidence Intervals for the Difference Between Two Proportions Small-Sample Confidence Intervals for the Difference Between Two Means Prediction Intervals and Tolerance Intervals	lecturing, problem solving, discussing
5	Hypothesis Testing Large-Sample Tests for a Population Mean Drawing Conclusions from the Results of Hypothesis Tests Tests for a Population Proportion	lecturing, problem solving, discussing
6	Small-Sample Tests for a Population Mean Large-Sample Tests for the Difference Between Two Means Small-Sample Tests for the Difference Between Two Means Tests for the Difference Between Two Proportions	lecturing, problem solving, discussing
7	Time Series Forecasting Theory Types of Studies, Mathematical Structure Cross-Sectional Study vs Time Series Study Types of Analysis	lecturing, problem solving, discussing
8	Choosing the Best Model Components of a Time Series Different Time Series Processes White Noise	lecturing, problem solving, discussing
9	Moving Average Weighted Moving Average Exponential Smoothing Forecasting Error Examples in R Programming	lecturing, problem solving, discussing
10	AR Models, MA Models ARMA Models, ARIMA Models Autocorrelation Function Partial Autocorrelation Function Detecting AR, MA, ARMA, and ARIMA Models	lecturing, problem solving, discussing
11	Correlation and Simple Linear Regression Covariance, Correlation Joint Distributions The Least-Squares Line	lecturing, problem solving, discussing
12	Uncertainties in the Least-Squares Coefficients Checking Assumptions and Transforming Data	lecturing, problem solving, discussing
13	Multiple Regression The Very Basics The Multiple Regression Model	lecturing, problem solving, discussing
14	Confounding and Collinearity Model Selection	lecturing, problem solving, discussing
15
16	Final Exam

Course Syllabus

#	Material / Resources	Information About Resources	Reference / Recommended Resources
1	Bowerman, B. L., O'Connell, R. T., and Koehler, A. B. Forecasting, Time Series, and Regression	Thomson Brooks/Cole Publishing
2	Gilchrist, W. Statistical Forecasting	John Wiley & Sons Ltd
3	Shmueli, G. Practical Time Series Forecasting with R	Axelrod Schnall Publishers

Method of Assessment

#	Weight	Work Type	Work Title
1	20%	Mid-Term Exam	Mid-Term Exam
2	80%	Final Exam	Final Exam

Relationship between Learning Outcomes of Course and Program Outcomes

#	Learning Outcomes	Program Outcomes	Method of Assessment
1	Students shall collect data for forecasting processes and analyses	1͵4͵11	1͵2
2	Students shall analyse the relationship between the variabes	1͵4͵11	1͵2
3	Students shall forecast the future by evaluating current variables	1͵4͵11	1͵2
4	Students shall identify the form and direction of the relationship between the variables	1͵4͵11	1͵2
5	Students shall make forcasting hypothesis	1͵4͵11	1͵2

PS. The numbers, which are shown in the column Method of Assessment, presents the methods shown in the previous table, titled as Method of Assessment.

Work Load Details

#	Type of Work	Quantity	Time (Hour)	Work Load
1	Course Duration	14	3	42
2	Course Duration Except Class (Preliminary Study, Enhancement)	14	2	28
3	Presentation and Seminar Preparation	0	0	0
4	Web Research, Library and Archival Work	0	0	0
5	Document/Information Listing	0	0	0
6	Workshop	0	0	0
7	Preparation for Midterm Exam	1	6	6
8	Midterm Exam	1	2	2
9	Quiz	0	0	0
10	Homework	0	0	0
11	Midterm Project	0	0	0
12	Midterm Exercise	0	0	0
13	Final Project	0	0	0
14	Final Exercise	0	0	0
15	Preparation for Final Exam	1	10	10
16	Final Exam	1	2	2
				90