COURSE UNIT TITLE

: STATISTICAL INFERENCE

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 2018 STATISTICAL INFERENCE COMPULSORY 4 0 0 7

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR BURCU HÜDAVERDI AKTAŞ

Offered to

Statistics
Statistics(Evening)

Course Objective

This course introduces students to the basic theory behind the development and assessment of statistical analysis techniques in the areas of point and interval estimation and hypothesis testing.

Learning Outcomes of the Course Unit

1   Explain the concepts of random sample, statistics and order statistics
2   Define the fundamental sampling distributions and its properties
3   Understand how to derive point estimators and their properties
4   Understand the theory behind confidence intervals
5   Understand the theory behind hypothesis testing and basic concepts of it (simple and composite hypotheses, rejection region, Type I and Type II error, power function, etc.)
6   Use the theory behind normal-based inference procedures for the one and two-sample problems
7   Obtain the most powerful critical region for simple hypothesis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

IST 2017 - Mathematical Statistics

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Random Samples, Statistics, Order Statistics
2 Empirical DistributionsConvergence concepts (Central limit theorem, Law of Large Numbers)
3 Sampling Distributions(Z, chi-square, t, F distributions)
4 Point Estimation, Point Estimation Methods (Method of Moments, Maximum Likelihood Method)
5 Properties of an estimator (Unbiasedness and Relative efficiency, Mean-squared error)
6 Properties of estimators (Cramer-Rao inequality, minumum variance, efficiency)
7 Properties of estimators (Consistency and other asymptotic properties)
8 Properties of estimators ( Asymptotic properties of estimators,Sufficient Statistics)
9 Interval Estimation and Pivotal Quantity
10 Confidence Interval Based on One-Sample
11 Confidence Interval Based on Two-Samples
12 Basic concepts of hypothesis testing (simple and composite hypotheses, rejection region, Type I and Type II error)
13 Power function and power of the test, theory behind normal-based inference procedures and applications for the one sample problems
14 Theory behind normal-based inference procedures for the two sample problems and applications

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.

Planned Learning Activities and Teaching Methods

Lecture notes, presentations, homeworks and exams.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 QUZ QUIZ
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.30 + RST * 0.40


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

Further Notes About Assessment Methods

None

Assessment Criteria

30% of the midterm exam, 20% of the quiz-homework and 50% of the final exam

Language of Instruction

English

Course Policies and Rules

Attendance is an essential requirement of this course and is the responsibility of the student. 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 www.fef.deu.edu.tr

Contact Details for the Lecturer(s)

DEU, Faculty of Sciences, Department of Statistics
Prof.Dr. Burcu Hüdaverdi
e-mail:burcu.hudaverdi@deu.edu.tr
Tel: 0232 301 85 97

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 4 56
Preparations before/after weekly lectures 14 1 14
Preparing assignments 2 12 24
Preparation for final exam 1 40 40
Preparation for quiz etc. 2 15 30
Final 1 2 2
Final Assignment 2 2 4
Quiz etc. 2 1 2
TOTAL WORKLOAD (hours) 172

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.15335
LO.25335
LO.35335
LO.45335
LO.55335
LO.65335
LO.75335