EEE5022 Applied StatisticsBahçeşehir UniversityDegree Programs PSYCHOLOGYGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
PSYCHOLOGY
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
EEE5022 Applied Statistics Fall 3 0 3 9
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. SAEID KARAMZADEH
Course Lecturer(s): Prof. Dr. SELİM ZAİM
Recommended Optional Program Components: none..........
Course Objectives: The course introduces fundamental topics in statistics and implements its applications to industrial, medical, financial, energy and similar type very large-size datasets to infer meaninful statistical results. The course is for gradute students with no significant background on this subject. Implementations will be performed on the open source statistical software R. Introduction to R programming will be given.

Learning Outcomes

The students who have succeeded in this course;
I. Identify basic terms in statistics.
II. Gain ability to use and apply basic methods and programming tools used in statistics over various engineering disciplines.
III. Ability to explore data and its relationships.
IV. Ability to perform hypothesis testing for statistical problems.
V. Perform statistical inference over statistical data.

Course Content

Topics include: Introduction to R programming, Sampling, Data Exploration, Exploring Relationships, Probability, Random Variables and Probability Distributions, Estimation, Hypothesis Testing, Statistical Inference, Multiple Testing Correction, ANOVA, Analysis of Categorical Variables, Regression Analysis, Bayesian Analysis, Survival Analysis, Over Representation Analysis, Meta Analysis.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction
2) Introduction to R statistical programming
3) Term Project
4) Data Exploration with R
5) Visualizing and Summarizing Relationships
6) Probability and Random Variables
7) Estimation in datasets
8) Hypothesis Testing for various engineering applications
9) Statistical Inference over various large datasets
10) ANOVA
11) Analysis of Categorical Variables
12) Regression and Bayesian Analysis
13) Survival analysis
14) Over Representation Analysis

Sources

Course Notes / Textbooks: Principles of Applied Statistics (Paperback), by D. R. Cox, Christl A. Donnelly 2011 ISBN-10: 1107644453 | ISBN-13: 978-1107644458
References: Introductory Statistics with R Peter Dalgaard 2011 ISBN 978-0-387-79053-4

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Project 1 % 30
Midterms 1 % 30
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 30
PERCENTAGE OF FINAL WORK % 70
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 14 42
Project 1 30
Midterms 1 40
Final 1 50
Total Workload 204

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) To develop an interest in the human mind and behavior, to be able to evaluate theories using empirical findings, to understand that psychology is an evidence-based science by acquiring critical thinking skills.
2) To gain a biopsychosocial perspective on human behavior. To understand the biological, psychological, and social variables of behavior.
3) To learn the basic concepts in psychology and the theoretical and practical approaches used to study them (e.g. basic observation and interview techniques).
4) To acquire the methods and skills to access and write information using English as the dominant language in the psychological literature, to recognize and apply scientific research and data evaluation techniques (e.g. correlational, experimental, cross-sectional and longitudinal studies, case studies).
5) To be against discrimination and prejudice; to have ethical concerns while working in research and practice areas.
6) To recognize the main subfields of psychology (experimental, developmental, clinical, cognitive, social and industrial/organizational psychology) and their related fields of study and specialization.
7) To acquire the skills necessary for analyzing, interpreting and presenting the findings as well as problem posing, hypothesizing and data collection, which are the basic elements of scientific studies.
8) To gain the basic knowledge and skills necessary for psychological assessment and evaluation.
9) To acquire basic knowledge of other disciplines (medicine, genetics, biology, economics, sociology, political science, communication, philosophy, anthropology, literature, law, art, etc.) that will contribute to psychology and to use this knowledge in the understanding and interpretation of psychological processes.
10) To develop sensitivity towards social problems; to take responsibility in activities that benefit the field of psychology and society.
11) To have problem solving skills and to be able to develop the necessary analytical approaches for this.
12) To be able to criticize any subject in business and academic life and to be able to express their thoughts.