# Probability and Statistics II (IE202) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Probability and Statistics II IE202 3 1 0 3 6.5
Pre-requisite Course(s)
IE 201
Course Language English N/A Bachelor’s Degree (First Cycle) Face To Face Lecture, Discussion, Question and Answer, Problem Solving, Team/Group, Project Design/Management. Dr. Öğr. Üyesi Gözdem DURAL SELÇUK Research Assistant Şevval KILIÇOĞLU Research Assistant Efe Can RÜBENDİZ The course aims to expose students to basic concepts of statistical inference, linear regression and correlation, forecasting and experimental design. The students who succeeded in this course; Students will be exposed to several types of decision making problems of industry that can be solved by statistical inference and hypothesis testing. Students will be able to develop simple and multiple-parameter linear models that can be utilized for prediction and forecasting in industrial planning and management. Students will reinforce their problem solving skills and their analytical thinking ability. Students will become familiar with a suitable statistical package through computer-based statistical analysis. Sampling distributions, point estimation, confidence intervals and interval estimation, hypothesis testing, simple linear regression and correlation, multiple linear regression, analysis of variance with a single factor

### Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Fundamentals of Sampling Distributions and Data Descriptions [1] pages 224-244
2 One-and-two-sample estimation problems [1] pages 253-276
3 One-and-two-sample estimation problems [1] pages 253-276
4 One-and-two-sample estimation problems [1] pages 253-276
5 One-and-two-sample tests of hypotheses [1] pages 283-344
6 One-and-two-sample tests of hypotheses [1] pages 283-344
7 One-and-two-sample tests of hypotheses [1] pages 283-344
8 Midterm I
9 Simple Linear Regression [1] pages 401-440
10 Simple Linear Regression [1] pages 401-440
11 ANOVA and its applications [1] pages 449-502
12 Multiple Linear Regression [1] pages 449-502
13 Midterm II
14 Design of Experiments [1] pages 514-544
15 Industrial Engineering Topics: Forecasting, Quality and Simulation Applications
16 Industrial Engineering Topics: Forecasting, Quality and Simulation Applications

### Sources

Course Book 1. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, 5th Edition, John Wiley and Sons, 2011. 2. Walpole, R.E., Myers, R.H., Myers, S.L. and Ye, K., Probability and Statistics for Engineers and Scientists, Prentice Hall, 2007. 3. Milton, J.S. and Arnold, J.C., Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, McGraw-Hill, 4th edition, 2002. 4. Ross, S. Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, 3rd edition, 2004. 5. Triola, M.F., Essentials of Statistics, Addison Wesley, 2nd edition, 2004. 6. Hines, W.W. and Montgomery, D.A., Probability and Statistics in Engineering and Management Science, John Wiley, 1990. 7. Navidi,W. Statistics for Engineers and Scientists, McGraw-Hill, 2008. 8. Mendenhall, W. and Sincich, T. Statistics for Engineering and the Sciences. Prentice Hall, 2007.

### Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work 1 10
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 15
Presentation - -
Project 1 15
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 20
Toplam 8 100
 Percentage of Semester Work 80 20 100

### Course Category

Core Courses X

### The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge of mathematics, science and engineering to Industrial Engineering; an ability to apply theoretical and practical knowledge to model and solve engineering problems. X
2 An ability to identify, formulate and solve complex engineering problems; an ability to select and apply proper analysis and modeling methods. X
3 An ability to design a complex system, process, tool or component to meet desired needs within realistic constraints; an ability to apply modern design.
4 An ability to develop, select and put into practice techniques, skills and modern engineering tools necessary for engineering practice; an ability to use information technology effectively. X
5 An ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or disciplinary research topics. X
6 An ability to work individually, on teams, and/or on multidisciplinary teams.
7 Ability to communicate effectively in Turkish orally and in writing; knowledge of at least one foreign language; effective report writing and understand written reports, preparing design and production reports, making effective presentations, giving and receiving clear and understandable instruction.
8 A recognition of the need for, and an ability to engage in life-long learning; an ability to use information-seeking tools and to follow the improvements in science and technology.
9 An ability to behave according to the ethical principles, an understanding of professional and ethical responsibility. Information on standards used in industrial engineering applications.
10 Knowledge of business applications such as project management, risk management and change management. A recognition of entrepreneurship, innovativeness. Knowledge of sustainable improvement.
11 Information on the effects of industrial engineering practices on health, environment and security in universal and societal dimensions and the information on the problems of the in the field of engineering of the era. Awareness of the legal consequences of engineering solutions.
12 An ability to design, development, implementation and improvement of integrated systems that include human, materials, information, equipment and energy.
13 Knowlede on appropriate analytical, computational and experimental methods to provide system integration.

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application 14 2 28
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project 1 15 15
Report
Homework Assignments 3 6 18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 12 12