COURSE UNIT TITLE

: ESTIMATION AND HYPOTHESIS TESTING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ERA 0008 ESTIMATION AND HYPOTHESIS TESTING ELECTIVE 4 0 0 6

Offered By

Faculty Of Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR BURCU HÜDAVERDI AKTAŞ

Offered to

Biology
Computer Science
Mathematics
Physics
Chemistry
Statistics

Course Objective

The main goal is that students learn the basic concepts and methods of parametric statistical inference, emphasizing the importance of the normal distribution when obtaining estimators and testing hypothesis about population parameters. The students should be able to apply these procedures to solve practical problems.

Learning Outcomes of the Course Unit

1   Explain the concepts of random sample and 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 and most powerful test for simple hypothesis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Concepts of random sample and statistics.
2 Convergence concepts (Central limit theorem, Law of Large Numbers)
3 Sampling Distributions(Z, chi-square, t, F)
4 Point Estimation, Point Estimation Methods (Method of Moments, Maximum Likelihood Method)
5 Properties of estimator (Bias and Variance; Mean-squared error)
6 Properties of estimator (Cramer-Rao inequality, efficiency, consistency and other asymptotic properties)
7 Sufficient Statistics
8 Interval Estimation and Pivotal Quantity
9 Confidence Interval Based on One-Sample
10 Confidence Interval Based on Two-Sample
11 Application
12 Basic concepts of hypothesis testing (simple and composite hypotheses, rejection region, Type I and Type II error) / Power function and power of the test
13 The theory behind normal-based inference procedures for one/two sample problems
14 Most Powerful Test / Most powerful critical region for simple hypothesis

Recomended or Required Reading

Textbook(s)/References/Materials:
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 format built around the textbook readings, applications and examples. Questions and discussion are encouraged. Lecture, homeworks and exams.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 QUZ QUIZ
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + QUZ * 0.20 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + QUZ * 0.20 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Participation and evaluation of exams, homeworks.

Language of Instruction

English

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 homework delivery must be on time. Any unethical behaviour that occurs either in presentations or in exams will be dealt with as outlined in school policy.

Contact Details for the Lecturer(s)

Contact Details for the Instructor:
Prof. Dr. Burcu Hüdaverdi
e-mail: burcu.hudaverdi@deu.edu.tr
tel: +90-232-3018603

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 2 24
Tutorials 2 2 4
Preparations before/after weekly lectures 13 2 26
Preparation for midterm exam 1 20 20
Preparation for final exam 1 30 30
Preparation for quiz etc. 2 10 20
Preparing assignments 2 10 20
Midterm 1 2 2
Final 1 2 2
Quiz etc. 2 1 2
TOTAL WORKLOAD (hours) 150

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.144
LO.244
LO.344
LO.444
LO.544
LO.644
LO.744