Statistical Applications for Industrial Engineers

Course Code:

ISE 256

Course Period:

Spring

Course Type:

Core

Credits:

4

Theoric:

3

Practice:

2

Laboratory Hour:

0

ECTS:

8

Prerequisite Courses:

Probability

Introduction to Industrial Engineering

Course Language:

English

Courses given by:

Zeynep Gökçe İşlier

Course Objectives:

To teach the basic principles and methods of statistics.

Course Content:

Descriptive statistics, random sampling, sample distributions, point estimates, confidence intervals, hypothesis tests, goodness of fit tests, independence tests, linear regression and correlation, statistical package applications.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Laboratory 4: Case Study

Course Evaluation Methods:

A: Exam, B: Experiment, C: Homework, D: Project, E: …

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Course Learning Outcomes

Course Learning Outcomes	Detailed Program Outcomes	Teaching Methods	Assessment Methods
Able to use statistical methods for problems encountered in engineering applications.	1a, 1b	1	A
Ability of analyzing the data and interpreting the results.	5b	1	A
Ability of; Estimating parameter in distributions. Finding confidence intervals. Doing hypothesis tests.	1b	1	A
Able to conduct goodness of fit tests and independence tests for grouped data.	1b	1	A
Able to determine sample size according to certain criteria.	1b	1	A
Can make correlation and regression analysis.	5b	1	A
Can use the statistics package MINITAB.	4b	1, 3	A

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction. Descriptive statistics.	Textbook
2	Random Sampling. Sample Statistics. Sample Mean, Variance and Proportion.	Textbook
3	Sampling Distribution.	Textbook
4	Normal ve t distributions.	Textbook
5	Chi-square ve F Distributions.	Textbook
6	Point estimate. Unbiasedness. Least Squares.	Textbook
7	MIDTERM I	Textbook
8	Confidence Intervals for variance and proportion.	Textbook
9	Hypothesis Tests.	Textbook
10	Hypothesis Tests for mean.	Textbook
11	Hypothesis Tests for variance.	Textbook
12	MIDTERM II	Textbook
13	Goodness of fit and indepence tests.	Textbook
14	Regression and correlation. MINITAB Applications.	Textbook

Recommended Sources

RECOMMENDED SOURCES

Textbook

Probability and Statistics for Engineers and Scientists, 9th Ed. (Global Ed.)

R. E. Walpole, R. H. Myers, S. L. Myers and K. Ye , Pearson Education

2016.

Additional Resources

Applied Statistics and Probability for Engineers, D. C. Montgomery and G.

C. Runger, Wiley

Material Sharing

MATERIAL SHARING
Documents
Assignments	Exercises from textbook, app. 30 quesitons.
Exams	1 Midterm, Final, 4 Quiz.

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Midterm	1	30
Quiz	4	20
Attendance		5
Total		55
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		45
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		55
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM OUTCOMES
No	Program Learning Outcomes	check √
1a	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline,	√
1b	Ability to use theoretical and applied knowledge in these areas in complex engineering problems.	√
2a	Ability to identify, formulate, and solve complex engineering problems,
2b	Ability to select and apply proper analysis and modeling methods for this purpose.
3a	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result,
3b	Ability to apply modern design methods for this purpose.
4a	Ability to devise, select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice.
4b	Ability to employ information technologies effectively.	√
5a	Ability to design experiments for investigating complex engineering problems or discipline specific research questions,
5b	Ability to conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.	√
6a	Ability to work efficiently in intra-disciplinary teams,
6b	Ability to work efficiently in multi-disciplinary teams,
6c	Ability to work individually.
7a	Ability to communicate effectively in Turkish, both orally and in writing,
7b	Knowledge of a minimum of one foreign language,
7c	Ability to write effective reports and comprehend written reports, prepare design and production reports,
7d	Ability to make effective presentations,
7e	Ability to give and receive clear and intelligible instructions.
8a	Recognition of the need for lifelong learning, ability to access information, ability to follow developments in science and technology,
8b	Ability to continue to educate him/herself.
9a	Consciousness to behave according to ethical principles and professional and ethical responsibility.
9b	Knowledge on standards used in engineering practice.
10a	Knowledge about business life practices such as project management, risk management, change management.
10b	Awareness in entrepreneurship and innovation.
10c	Knowledge about sustainable development.
11a	Knowledge about the global and social effects of engineering practices on health, environment, and safety,
11b	Knowledge about contemporary issues of the century reflected into the field of engineering.
11c	Awareness of the legal consequences of engineering solutions.

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration	14	5	70
Hours for off-the-classroom study (Pre-study, practice)	14	6	84
Exercises	14	4	56
Midterm	1	2	2
Final	1	2	2
Total Work Load			214
Total Work Load / 25 (h)			8,56
ECTS Credit of the Course			8

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Statistical Applications for Industrial Engineers

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