COURSE UNIT TITLE

: STATISTICS I

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
END 2303 STATISTICS I COMPULSORY 3 0 0 5

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 Scientific Preparatory (Msc)
Industrial Engineering Scientific Preparatory (Msc Without Thesis)
Industrial Engineering Scientific Preparatory (Phd)
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   Gaining knowledge of calculating the probabilities of one-dimensional and multi-dimensional random events by using sing probability laws such as conditional probability and Bayes' Theorem
2   Creating awareness to analyze the dependency/independency relationship between random variables and the type/degree of the relationship
3   Gaining the knowledge of calculating the necessary statistics by using probability density and cumulative distribution functions for single and multidimensional, discrete and continuous random variables
4   To raise awareness of creating solutions to statistical engineering problems by using discrete and continuous probability distributions
5   Gaining knowledge of calculating sample statistics values in line with population and sample relationship
6   Gaining knowledge of calculating descriptive statistics and creating histograms 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 I
2 Some Laws of Probability and Random Variables
3 Discrete Random Variables: Probability Density Function, Expected Value and Variance
4 Continuous Random Variables: Probability Density Function, Expected Value and Variance
5 Bivariate Random Variables I: Probability Density Function, Marginal Functions, Expected Value and Variance
6 Bivariate Random Variables II: Independence, Conditional Density, Covariance and Correlation
7 Special Discrete Random Variables I: Discrete Uniform Dist., Bernoulli Dist., Binomial Dist., Negative Binomial Dist.
8 Midterm Exam I
9 Special Discrete Random Variables II: Hypergeometric Dist., Poisson Dist.
10 Special Continuous Random Variables I: Continuous Uniform Dist., Gamma Dist., Exponential Dist.
11 Special Continuous Random Variables II: Normal Dist., Chi-square Dist., t-Dist., F Dist.
12 Midterm Exam II
13 Relationship between Population and Sampling
14 Random Sampling, Histograms, Sample Statistics

Recomended or Required Reading

Textbooks:
1. D. C. Montgomery and G.C. Runger, (1999). Applied Statistics and Probability for Engineers, 2nd Edition. John Wiley and Sons, USA.

Reference books:
1. R. E. Walpole, R. H. Myers, S. L. Myers, (1998). Probability and Statistics for Engineers and Scientists, 6th Edition. Prentice Hall, USA.
2. Fikri Akdeniz. (2010). Olasılık ve Istatistik, 15.Baskı. Nobel Yayın Dağıtım, Adana.

Planned Learning Activities and Teaching Methods

Instructor notes will be given using blackboard and visual presentations. Additionally, it will be further supported by computer lab work.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 QUZ QUIZ
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE*0.40 + QUZ *0.10 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE*0.40 + QUZ *0.10 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm exam (50%)+Final exam (50%)

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Tel: 301 76 22
e-mail: seren.ozmehmet@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Tutorials 1 3 3
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparing assignments 1 4 4
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 119

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.14
LO.24
LO.344
LO.4554
LO.545
LO.6455