Faculty Of Engıneerıng
Industrıal Engıneerıng (Englısh)
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 |
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 | ||||