• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS IBF 1008 STATISTICS II COMPULSORY 3 0 0 5

Offered By

Faculty of Economics and Administrative Sciences

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ALI KEMAL ŞEHIRLIOĞLU

Offered to

Management Information Systems Economics Econometrics (Evening) Business Administration Economics (Evening) Business Administration (Evening) Econometrics Faculty of Economics and Administrative Sciences

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

None

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 assumptions of linear regression 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

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 25 25 Preparation for final exam 1 28 28 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 115

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 LO.1 5 LO.2 5 LO.3 5 LO.4 5 5 LO.5 5 LO.6 5 LO.7 5