• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS END 2313 INTRODUCTION TO PROBABILITY AND STATISTICS COMPULSORY 3 0 0 4

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR ŞEBNEM DEMIRKOL AKYOL

Offered to

Industrial Engineering

Course Objective

In this course, the basics of probability and probability distributions, population sampling concepts and descriptive statistics are given.

Learning Outcomes of the Course Unit

1   1. To evaluate the probabilities of one-dimensional and multi-dimensional random events using conditional probability, Bayes Theorem, etc. by using probability laws 2   2. Ability to analyze the dependent/independent relationship between random variables and the type/degree of the relationship 3   3. Ability to calculate necessary statistics using probability density and cumulative distribution functions for single and multi-dimensional, discrete and continuous random variables. 4   4. Creating solutions to statistical engineering problems using discrete and continuous probability distributions 5   5. Ability to calculate sample statistics values in line with the population and sample relationship 6   6. Ability to calculate descriptive statistics and create a Histogram using data

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Statistics 2 Probability Theory and Probability Laws 3 Random Variables and Fundamental Properties 4 Discrete Random Variables: Probability Density Function, Expected Value, and Variance 5 Continuous Random Variables: Probability Density Function, Expected Value, and Variance 6 Joint Random Variables I: Probability Density Function, Marginal Functions, Expected Value and Variance 7 Joint Random Variables II: Independency, Conditional Density, Covariance and Correlation 8 Special Discrete Random Variables I: Discrete Uniform Distribution, Bernoulli Distribution, Binomial Distribution, Negative Binomial Distribution 9 Special Discrete Random Variables II: Hypergeometric Distribution, Poisson Distribution 10 Special Continuous Random Variables I: Continuous Uniform Distribution, Gamma Distribution, Exponential Distribution 11 Special Continuous Random Variables II: Normal Distribution, Chi-square Distribution, t-distribution, F distribution 12 Population and Sampling Relationship 13 Random Sample, Sample Statistics and Descriptive Statistics 14 Histograms

Recomended or Required Reading

Main source: 1. D. C. Montgomery and G. C. Runger, (1999). Applied Statistics and Probability for Engineers, 2nd Edition. John Wiley and Sons, USA. Auxiliary resources: 1. R. E. Walpole, R. H. Myers, S. L. Myers, (1998). Probability and Statistics for Engineers and Scientists, 6th Edition. Prentice Hall, USA.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 ASG ASSIGNMENT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE*0.35+ASG *0.15+FIN * 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE*0.35+ASG *0.15+RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm(35%) + Homework(15%) + Final(50%)

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

sebnem.demirkol@deu.edu.tr

Office Hours

Office hours will be declared.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 14 2 28 Preparation for midterm exam 1 12 12 Preparation for final exam 1 12 12 Preparing assignments 1 4 4 Final 1 2 2 Midterm 1 1 1 TOTAL WORKLOAD (hours) 101

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 LO.1 2 4 2 LO.2 2 4 2 2 2 LO.3 2 4 2 2 2 LO.4 2 4 2 LO.5 2 4 2 2 2 LO.6 2 4 2 2 2