COURSE UNIT TITLE

: STATISTTICS II

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ERA 1402 STATISTTICS II ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

Offered to

Econometrics (Evening)
Econometrics

Course Objective

Economics and business data to make statistical evaluations, review, existence, basic accounts and formulations, to gain skills to use statistical results and statistical reasoning to develop.

Learning Outcomes of the Course Unit

1   To be able to define the properites of estimators and importance in the theory of statistical estimation
2   To be able to define the populations according to the representative sample taken
3   To be able to establish the relationship between probability distributions and hypothesis testing.
4   To be able to propose appropriate regression models the dependent and independent variables.
5   To be able to test hypothesis tests and confidence intervals by setting appropriate objectives
6   To be able to interpret regression models according to the the relationships
7   To be able to identify representative sample for populations

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

HAZIRLIK - FOREIGN LANGUAGE PREPARATION CLASS

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Confidence Intervals: For populations means , population is normal :population variance is known, Student t distribution, For populations means , population is normal :population variance is not known, For population proprotions ,
2 Chi-spuare ( ) distribution, Confidence interval for population variance, population are normal, Confidence inreval for the difference of two populations means , populations are normal, Confidence interval for paired samples which are normal, Confidence interval for the difference of two independent sample means
3 Confidence interval for the difference of two populations proprotions. (large samples), Sample size for the confidence interval for population means , population is normal, population variance is known, samples size for the confidence interval for the population proprotion.
4 Hypothesis Testing: basic consepts, hypothesis testing for normal population mean: population variance is known, hypothesis testing for normal population mean: population variance is not known, ( small and large samples ), hypothesis testing for population proportion( large samples)
5 Hypothesis testing for normal population variance, hypothesis testing for two populations means: Paired Saples, Independent Samples, hypothesis testing for two population proportions, F distibution, hypothesis testing of two populations variances, calculation of type II error ( )
6 Chi- square goodness of fits test ( uniform, binom, poisson and normal),rxc independent test
7 Regression analysis: Simple linear regression, mean square estimation, the assuptions of lenear regression, the significant test for ( t test ), the confidence interval for
8 Mid-Term
9 Mid-Term
10 Analysis of variance for regression model, the estimation of coefficient of deternination and significant test, the estimation of correlaiton coefficient and significant test
11 One way and two way anova ( analysis of variance )
12 Index Number : A simple index, Chain Index, Time and Space Indexes, Fixed Based Index, Variable-Based Index, an index to the other transition, main (base) Circuit Identification, indexes Average, Weighted Indexes, Some Important Indexes
13 Time Series Analysis and Estimating: A Time Series Components, Moving Averages, Determination of the Effect of Seasonal Using Moving Averages
14 Simple random sampling, Stratified sampling, cluster sampling, systematic sampling Introducing the Social Sciences Applications

Recomended or Required Reading

Statistics for Business and Economics, Paul Newbold

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


Further Notes About Assessment Methods

None

Assessment Criteria

Mid-term exam 40%
Final-exam 60%

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Prof.Dr. Levent ŞENYAY: levent.senyay@deu.edu.tr
Prof.Dr.Şenay ÜÇDOĞRUK: s.ucdogruk@deu.edu.tr
Prof.Dr. M.Vedat PAZARLIOĞLU: vedat.pazarlioglu@deu.edu.tr
Doç.Dr. Cenk ÖZLER: cenk.ozler@deu.edu.tr
Doç. Dr. Ali Kemal ŞEHIRLIOĞLU: kemal.sehirlioglu@deu.edu.tr
Doç. Dr. Kadir ERTAŞ: kadir.ertas@deu.edu.tr
Yrd. Doç. Dr, Hamdi EMEÇ: hamdi.emec@deu.edu.tr
Yrd. Doç. Dr, Istem KÖYMEN KESER: istem.koymen@deu.edu.tr
Yrd. Doç. Dr. Mehmet AKSARAYLI: mehmet.aksarayli@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparation for midterm exam 1 20 20
Preparations before/after weekly lectures 12 3 36
Preparation for final exam 1 25 25
Final 1 1 1
Midterm 1 1 1
TOTAL WORKLOAD (hours) 119

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.111
LO.21
LO.31
LO.41
LO.51
LO.61
LO.71

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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