Course Name Code Semester T+U Hours Credit ECTS
Applied Regression Analysis ENM 519 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Prof.Dr. SEMRA BORAN
Course Lecturers
Course Assistants
Course Category
Course Objective Aim of the course is to build the best mathematical model that explains relations among dependent and independent variables and estimate structural analysis by using the model mentioned.
Course Content Relationship between variables, analysis of correlation, simple linear regression, confidences of the regression models, nonlinear regression, Assumptions of Multiple regression models and diversions from these assumptions
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Research of the relationship between variables Lecture, Question-Answer, Drilland Practice, Testing, Homework,
2 Building the modeling based on the variables Motivations to Show, Drilland Practice, Lecture, Homework, Testing,
3 Estimating and analyzing regression models Lecture, Drilland Practice, Testing, Homework,
Week Course Topics Preliminary Preparation
1 Introduction to Regression analysis, Definitions and goals of Regression Analysis, type of data for Regression
2 Simple Linear Regression, Ordinary Least Square Methods for Regression Parameter Estimation
3 Standard Error of Regression Models and Regression Coefficients
4 Correlation and Determination Coefficients and Significance Tests
5 Multiple Regression Models and Their Assumptions, Least-squares estimation of Multiple regression coefficients
6 Confidence intervals for regression coefficients, coefficient of elasticity
7 Multiple determination coefficient, Analysis of variance for validity of regression model
8 Simple and multiple nonlinear regression models
9 The assumptions of multiple regression models, Investigate of the normality for the error term
10 Determining the Autocorrelation problems and solution methods
11 Assumption of Homoscedasticity, Heteroscedasticity problems and solution methods
12 Problem of Multicollinearity and solution methods
13 Alternative methods for selecting variables for the multiple linear regression models
14 Statistical package programs application for solution the regression models
Resources
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 The aim of the course is to reach the information in depth and in depth by conducting scientific research in the field of engineering, to evaluate, interpret and apply the information. X
2 Ability to complete and apply knowledge by scientific methods using limited or missing data; to integrate information from different disciplines. X
3 To be able to construct engineering problems, develop methods to solve them and apply innovative methods in solutions. X
4 Ability to develop new and original ideas and methods; develop innovative solutions in system, part or process designs. X
5 Ability to design and apply analytical, modeling and experimental research; to analyze and interpret complex situations encountered in this process. X
6 Identify the information and data needed, reach them and evaluate them at an advanced level. X
7 Leadership in multi-disciplinary teams, developing solutions to complex situations and taking responsibility. X
8 To be able to convey the process and results of his / her studies systematically and clearly in written or oral form in national and international environments in or out of that field. X
9 Interpreting comprehensive information about modern techniques and methods applied in engineering and their limits. X
10 Awareness about new and developing practices of the profession; to examine and learn them when necessary. X
11 To understand the social and environmental dimensions of engineering applications and to adapt to the social environment. X
12 To observe social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 60
1. Kısa Sınav 20
1. Ödev 20
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 2 32
Mid-terms 1 15 15
Quiz 1 10 10
Assignment 1 15 15
Performance Task (Laboratory) 1 30 30
Total Workload 150
Total Workload / 25 (Hours) 6
dersAKTSKredisi 6