STATISTICAL DATA ANALYSIS

BİL554

Course Code: BİL554
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree With Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Professor Nizami Gasilov

Learning Outcomes

At the end of this course, the students;

Select data collecting technique, prepare raw data for analysis.
Study with numeric data, use statistical data analysis tools.
Use any data analysis tool and/or a statistical packaged software.
Define statistical analysis results and explanation of executive effects of it.
Make reliability analysis of results.
Report analysis results.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

None

Course Description

Preparing the data and making it ready for analysis. To be able to determine the appropriate analysis methods and to apply these methods. To gain the ability to use at least one programming language or package program and report the results.

Course Contents

WEEK	TOPICS
1^st Week	Course introduction. Basic definitions and concepts.
2^nd Week	Introduction to R and R Studio. Data structures and data input in R.
3^rd Week	Data structures in R: Vectors, matrices, arrays. Data frame and lists. Operations on matrices and vectors
4^th Week	Descriptive statistics: Calculating frequency tables and descriptive statistics with R
5^th Week	Data visualization
6^th Week	Some discrete and continuous distributions. R applications
7^th Week	Analysis of normality and evaluation of assumptions and application of R
8^th Week	Mid-term exam.
9^th Week	Parametric tests (one and two samples). R applications
10^th Week	Analysis of variance. R applications
11^th Week	Non-Parametric tests. R applications
12^th Week	Chi-square independence test - R applications.
13^th Week	Correlation analysis-R applications
14^th Week	Linear Regression analysis-R applications.

Recommended or Required Reading

(1) Cotton, R. (2013). Learning R. O?Reilly Media, Inc.
(2) Fischetti, T. (2015). Data Analysis with R. Packt Publishing.
(3) Demir, İ (Editör). (2017). R ile Uygulamalı İstatistik. Papatya Bilim.
(4) Peter Daalgard (2008). Introductory Statistics with R, Springer.
(5) Gareth J., Witten D., Hastie T., Tibshirani R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.

Planned Learning Activities and Teaching Methods

Lecture
Questions/Answers
Problem Solving
Practice

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Assignment	2	10
Project	1	20
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	2	2
Preparation for Quiz
Individual or group work
Preparation for Final exam	1	70	70
Course hours	14	3	42
Preparation for Midterm exam	1	40	40
Laboratory (including preparation)
Final exam	1	2	2
Homework	2	15	30
Project	1	120	120
Total Work Load			306
Total Work Load / 30 (h)			10.2
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4	LO5	LO6
K1	X	X
K2
K3		X
K4		X	X
K5		X	X
K6	X	X		X
K7
K8
K9					X
K10				X
K11
K12				X	X	X

	LO1	LO2	LO3	LO4	LO5	LO6
K1	X	X
K2
K3		X
K4		X	X
K5		X	X
K6	X	X		X
K7
K8
K9					X
K10				X
K11
K12				X	X	X

	LO1	LO2	LO3	LO4	LO5	LO6
K1	X	X
K2
K3		X
K4		X	X
K5		X	X
K6	X	X		X
K7
K8
K9					X
K10				X
K11
K12				X	X	X

	LO1	LO2	LO3	LO4	LO5	LO6
K1	X	X
K2
K3		X
K4		X	X
K5		X	X
K6	X	X		X
K7
K8
K9					X
K10				X
K11
K12				X	X	X