COURSE UNIT TITLE

: MATHEMATICAL STATISTICS I

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EMT 2011 MATHEMATICAL STATISTICS I COMPULSORY 3 0 0 4

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR IPEK DEVECI KOCAKOÇ

Offered to

Econometrics (Evening)
Econometrics

Course Objective

Mathematical aspect of statistics by emphasizing the cumulative distribution functions of discrete and continuous probability models, probability density function, moment generating functions, the factorial moment generating functions and discrete models, also used directly in this context, to teach mathematical structures.

Learning Outcomes of the Course Unit

1   To be able to understand the science of statistics applications to the econometrics, business administration, operation research and similar areas
2   To be able to comprehend the methods which are used to find expected value and variances of probability density functions
3   To be able to analyze the structure of the basic mathematical theory of statistics for discrete distributions
4   To be able to analyze the structure of the basic mathematical theory of statistics for continuos distributions
5   To be able to apply the science of statistics in the econometrics, business administration, operation research and similar areas

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic Set Theory and Probability Theory
2 Conditional Probability, Statistical Independent, Random Variables and Cumulative Distirbuton Function
3 Probability distribution functions, Probability Density Functions and they are to be analyzed on the basic discrete and continuous distributions
4 The consept of expected value, the properties of expected value, The population mean, variance and standart deviation for discrete and continuous random variables, moment around the origin and mean
5 Moment Generating Functions, Factorail Moment Generating Functions, Why do we use Moment generating function (Maclaurin Expansion aid analysis)
6 Bernoulli Trials, Bernoulli Distribution, Binomial Distribution an Discrete Uniform Distribution
7 Geometric Distribution, Negative Binomail Dsitribution and negative binomial expansion
8 Mid-term
9 Mid-term
10 Discrete Uniform Distribution, Exponential Distribution
11 Normal distribution, mathematical features the normal distribution
12 Normal distribution, mathematical features the normal distribution
13 Gamma Functions, The properties of Gamma Functions, Gamma distributions (two parameter)
14 Some important theorems for moment generating and probability generating functions

Recomended or Required Reading

1-Mood, Graybill & Boes, Introduction to the Theory of Statistics, McGraw Hill, NY
2-Hogg R V & Craig A T, Introduction to Mathematical Statistics, 4th Ed, Macmillan, London
3-Ross S M, A First Course in Probability, Academic Press, NY

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 MTEG MIDTERM GRADE MTEG * 1
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60


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

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparations before/after weekly lectures 12 2 24
Preparation for midterm exam 1 20 20
Preparation for final exam 1 28 28
Midterm 1 1 1
Final 1 1 1
TOTAL WORKLOAD (hours) 110

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.11
LO.21
LO.311
LO.41
LO.51