COURSE UNIT TITLE

: APPLICATION OF RESAMPLING METHODS WITH R

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4102 APPLICATION OF RESAMPLING METHODS WITH R ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE 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 resampling methods to estimate standard error
4   Using of bootstrap to construct confidence intervals
5   Bootstrapping in regression
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 Monte Carlo methods in inference
5 Introduction to the jackknife and bootstrap, Using resampling to estimate standard error
6 Bootstrap confidence intervals; bootstrap percentile, bootstrap-t
7 Bootstrap confidence intervals; BCa
8 Implementation of bootstrap CI with R
9 Permutation tests
10 Bootstrap hypothesis tests
11 Bootstrapping regression models; bootstrapping pairs
12 Bootstrapping regression models; bootstrapping residuals
13 Simulation studies with R
14 Simulation studies with R

Recomended or Required Reading

Textbook(s):
Davison A.C., Hinkley D.V., Bootstrap Methods and their Application, 1997.
Efron B., Tibshirani R.J., An Introduction to the Bootstrap, 1993.
Supplementary Book(s):
Chihara L., Hesterberg T., Mathematical Statistics with Resampling and R, 2011.
Zieffler A.S., Harring R.H., Long J.D., Comparing Groups Randomization and Bootstrap Methods Using R, 2011.
Materials: Lecture slides

Planned Learning Activities and Teaching Methods

Lecture, examples and PC laboratory exercises.

Assessment Methods

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


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

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams.

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 Science 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
Web Search and Library Research 2 7 14
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 21 21
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