Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1IE525ADVANCED STATISTICS3+0+037,515.10.2024

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program INDUSTRIAL ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To introduce the role of statistics in research and engineering practice.
To develop skills in data gathering and analysis.
To provide tools to interpret experimental results.
To provide sufficient background to support further studies in industrial engineering.
Course Content The course describes advanced statistical methods which include the discovery and exploration of complex multivariate relationships of random variates. Topics include descriptive statistics, parameter estimation, confidence intervals, hypothesis testing, linear regression, and multi-linear regression. The course includes computer implementations using available up-to-date statistical software.
Course Methods and Techniques Lectures and Canvas
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Uğur Satıç yok ugur.satic@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Montgomery, Douglas C., and Runger, George C. Applied Statistics and Probability for Engineers. Wiley, 2013.
Tutorial questions from Canvas.
Lecture notes (Available in Canvas)
Course Notes Montgomery, Douglas C., and Runger, George C. Applied Statistics and Probability for Engineers. Wiley, 2013.
Documents canvas
Assignments canvas
Exams canvas

Course Category
Field %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı 1 % 30
Proje/Çizim 2 % 30
Final examination 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Proje 2 25 50
Ders dışı çalışma 14 4 56
Yüz Yüze Ders 14 3 42
Ders Dışı Ara Sınav 1 27 27
Final Sınavı 1 35 35
Total Work Load   Number of ECTS Credits 7,5 210

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Demonstrate understanding of descriptive statistics by summarizing data numerically and graphically
2 Compute point estimates and confidence intervals for unknown parameters of distributions.
3 Perform hypotheses testing with one or two samples
4 Construct and interpret linear regression models
5 : Be able to use a statistical software. (e.g., R, Python, SPSS)
6 Be able to work in a team and share the results of a statistical analysis (written and orally) with peers in a meaningful and professional manner.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Descriptive Statistics with graphics Skim the section and bring a question so we can solve it in class together. Ders kitabının 6. bölümü
2 Point Estimation of Parameters and Sampling Distributions Skim the section and bring a question so we can solve it in class together. Section 7 of Applied Statistics and Probability for Engineers
3 Statistical Intervals for a Single Sample Skim the section and bring a question so we can solve it in class together Section 8 of Applied Statistics and Probability for Engineers
4 Statistical Intervals for a Single Sample (part 2) Skim the section and bring a question so we can solve it in class together Section 8 of Applied Statistics and Probability for Engineers
5 Statistical Inference for Two Samples Skim the section and bring a question so we can solve it in class together Section 10 of Applied Statistics and Probability for Engineers
6 Statistical Inference for Two Samples (part 2) Skim the section and bring a question so we can solve it in class together Section 10 of Applied Statistics and Probability for Engineers
7 Midterm Exam Check shared questions. Tutorial sets
8 Active Learning Week: Project presentations. Topic: present a research paper from the lıterature related to our lecture topics
9 Tests of Hypotheses for a Single Sample Skim the section and bring a question so we can solve it in class together Section 9 of Applied Statistics and Probability for Engineers
10 Tests of Hypotheses for Two Samples Skim the section and bring a question so we can solve it in class together Section 10 of Applied Statistics and Probability for Engineers
11 Simple Linear Regression and Correlation Skim the section and bring a question so we can solve it in class together Section 11 of Applied Statistics and Probability for Engineers
12 Multiple Linear Regression (part 1) Skim the section and bring a question so we can solve it in class together Section 12 of Applied Statistics and Probability for Engineers
13 Multiple Linear Regression (part 2) Skim the section and bring a question so we can solve it in class together Section 12 of Applied Statistics and Probability for Engineers
14 Project presentations. Topic: present a research paper from the lıterature related to our lecture topics

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 5 4 1
C2 5 4 1
C3 5 4 1
C4 5 5 1
C5 4 4 2 4
C6 5 5 3 3 5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77274&lang=en
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