Kocaeli University / European Credit Transfer System (ECTS)

Name of Lecturer(s)

Prof. Dr. Abdülkadir AYGÜNOĞLU
Associate Prof. Dr. Süleyman EKEN
Research Assistant Seda BALTA
Research Assistant Dr. İrem ÇAY

Learning Outcomes of the Course Unit
Program Competencies-Learning Outcomes Relation

Learning Outcomes of the Course Unit

1) Apply sample spaces and events for random experiments with graphs, tables, lists
2) Calculate and Comment on interpret probabilities and use probabilities of outcomes to calculate probabilities of events in discrete/ continuous sample spaces and and calculate conditional probabilities of events
3) Determine the independence of events and use independence to calculate probabilities
4) Use Bayes’ theorem to calculate conditional probabilities
5) Explain random variables
6) Determine probabilities from (discrete/continuous) probability mass functions
7) Calculate means and variances for discrete/continuous random variables
8) Use joint probability mass functions and joint probability density functions to calculate probabilities, and calculate marginal and conditional probability distributions from joint probability distributions

Program Competencies-Learning Outcomes Relation

Program Competencies

Learning Outcomes

High

No relation

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Not Required

Course Contents

This course covers ;introduction, statistics, some concepts used in statistics, processing of the data, statistical probability, measures of variability, probability theory, conditional probability, Bayes Theorem,probability distributions,probability distribution function,characteristics of probability distributions,measures, some special probability distributions,special discrete distributions,special permanent,distributions,two-dimensional probability distribution,some two-dimensional distributions, two random elimination of one variable, probability density function of a random variable, characteristic function of a random variable and application of a communication channel.

Weekly Schedule

1) Course orientation, Welcome
2) Combinatorics
3) Intro Probability
4) Conditional Probability
5) Independence
6) Discrete Random Variables
7) Variance
8) Midterm exam
9) Poisson distribution
10) Continous Random Variables
11) Normal distribution
12) Joint Probability
13) Discrete Joint probability and Conditional joint probability
14) Covariance and Correlation
15) Measures of central tendency
16) Final exam

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Contribution of Midterm Examination to Course Grade	50%
Contribution of Final Examination to Course Grade	50%
Total	100%

Language of Instruction

Turkish

Work Placement(s)