COURSE UNIT TITLE

: PROBLEM BASED LEARNING I

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 2015 PROBLEM BASED LEARNING I COMPULSORY 2 0 0 4

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR TUĞBA YILDIZ

Offered to

Statistics
Statistics(Evening)

Course Objective

This course provides generating research questions, discussion on the choice of appropriate mathematical statistical approaches and case studies: application of mathematical statistical methods to various fields. Discussion and evaluation of previously done studies from the literature.

Learning Outcomes of the Course Unit

1   Use the Basic Discrete Distributions (Bernoulli, Binomial, Geometric, Negative Binomial, Hypergeometric, and Poisson) with Their Properties
2   Use the Basic Continuous Distributions (Uniform, Normal, Standard normal, Exponential, Gamma) with Their Properties
3   Obtain Moments and Moment Generating Functions of Random Variables.
4   Obtain Basic Two-Variable Statistics (covariance, correlation) Using the Joint Distributions, Conditional Distributions
5   Obtain the Distributions of the Functions of Random Variables

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Random variables, Moments and Moment Generating Functions
2 Special Discrete Distributions and Moment Generating Functions (Bernoulli, Binomial, Geometric, Negative Binomial, Hypergeometric, and Poisson)
3 Special Continuous Distributions and Moment Generating Functions (Uniform, Normal, Standard Normal, Exponential, Gamma, Weibull)
4 Joint probability distributions, Marginal distributions
5 Joint probability distributions, Marginal distributions
6 Conditional Distributions, Independent Random Variables
7 Expected Values of Bivariate Distributions
8 Conditional Expected Value, Conditional Variance and Its Properties
9 Covariance and Correlation
10 Methods for Distributions of Functions of a random variable (CDF and Transformation Methods)
11 Methods for Distributions of Functions of two or more random variables (CDF and Transformation Methods)
12 Methods for Distributions of Functions of two or more random variables (Transformation Methods)
13 Distributions of Sums of Random Variables
14 General Overview

Recomended or Required Reading

Textbook(s):
L. J. Bain and M. Engelhardt, Introduction to Probability and Mathematical Statistics, 2nd Edition, Duxbury, 1992.
Supplementary Book(s):
1. R. J. Larsen and M. L. Marx, An Introduction to Mathematical Statistics and Its Applications, 4th Edition, Prentice Hall.
2. I. Miller and M. Miller, John E. Freund's Mathematical Statistics with Applications, 7 edition Prentice Hall, 2003.H. Taha, Operations Research, McGraw Hill, 7th edition, 2003

Planned Learning Activities and Teaching Methods

Problem based learning, class discussion.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 VZ Vize
2 FN Final
3 BNS BNS VZ * 0.40 + FN * 0.60
4 BUT Bütünleme Notu
5 BBN Bütünleme Sonu Başarı Notu VZ * 0.40 + BUT * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Since this is an active learning system based lecture, the grades will be evaluated with 40% midterm+60% final exam. Class participation is an important criterion for evaluating the student's achievements.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and Quiz-final exam dates-times must be followed. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

e-mail: tugba.ozkal@deu.edu.tr
tel : 0232 301 86 02

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Preparation for midterm exam 1 12 12
Preparation for final exam 1 24 24
Preparations before/after weekly lectures 1 14 14
Midterm 1 2 2
Final 1 2 2
Participating Lectures and Field Studies 1 14 14
TOTAL WORKLOAD (hours) 96

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.15554
LO.25554
LO.35554
LO.45554
LO.55554