COURSE UNIT TITLE

: APPLICATION OF RESAMPLIC METHODS WITH R

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4153 APPLICATION OF RESAMPLIC METHODS WITH R ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASISTANT PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics
Statistics(Evening)

Course Objective

The purpose of this course is to learn students the re-sampling methods and to apply these methods to the estimation, confidence intervals, hypothesis testing using the R language.

Learning Outcomes of the Course Unit

1   Describing the basic concepts of re-sampling
2   Comprehending the jackknife, permutation tests and bootstrap methods
3   Using bootstrap methods to estimate parameter
4   Using of bootstrap hypothesis tests
5   Applying bootstrap in regression models
6   Writing R functions for re-sampling applications
7   Building simulations studies with R

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 The concept of re-sampling
2 Basic properties of R language, operators, data types
3 Functions in R and writing function
4 Introduction to the jackknife and bootstrap methods
5 Using bootstrap to estimate parameter
6 Bootstrap confidence intervals; bootstrap percentile, bootstrap-t
7 Bootstrap confidence intervals; BCa
8 Midterm exam
9 Implementation of bootstrap CI with R
10 Permutation tests
11 Bootstrap hypothesis tests
12 Bootstrapping regression models; bootstrapping pairs
13 Bootstrapping regression models; bootstrapping residuals
14 R packages:boot and bootstrap, bootstrap functions in R, Simulation studies with R

Recomended or Required Reading

Textbook(s):
Chernic M.R.,Bootstrap Methods: A Guide Practitioners and Researchers, 2nd Ed.,2007.
Supplementary Book(s):
1. Efron B., Tibshirani R.J., An Introduction to the Bootstrap, 1993.
2. Braun W.J., Murdoch D.J., A First Course in Statistical Programming with R, Cambridge, 2009.
3. Good P.I., Resampling Methods, 3rd Ed., 2006.
References:
Materials: Lecture slides

Planned Learning Activities and Teaching Methods

Lecture, homework assignments, examples and PC laboratory exercises.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 + ASG * 0.10 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + ASG * 0.10 + RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homeworks.

Language of Instruction

English

Course Policies and Rules

Student responsibilities:
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 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 http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Faculty of Sciences Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
Phone:+90 232 301 86 04

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for final exam 1 30 30
Preparing assignments 1 10 10
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 25 25
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 125

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.1443
LO.2443
LO.3443
LO.4443
LO.5443
LO.645435
LO.745435