STATISTICAL ANALYSIS PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
MTH217	STATISTICAL ANALYSIS	6	3	3	5

GENERAL INFORMATION
Language of Instruction :	English
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. OĞUZHAN ÖZTAŞ
Instructor(s) of the Course Unit	Assist.Prof. SAJEDEH NOROZPOUR SIGAROODI
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	To equip students with the competence to identify and formulate concrete statistical problems encountered in the real and service sectors and to develop appropriate solution proposals for addressing such problems.
Contents of the Course Unit:	Hypothesis Testing, Hypothesis Tests for Differences Between Two Populations and Population Proportions, One-Way Analysis of Variance (ANOVA), F-Test (ANOVA) and Its Applications, Parametric and Nonparametric Hypothesis Tests, Chi-Square Test, Linear Regression Analysis and Its Applications, Determination of the Correlation Coefficient, Index Numbers, and Applications Using SPSS / Minitab / MATLAB.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)

Upon successful completion of this course, students will be able to: I. Interpret, identify, and solve statistical problems. II. Formulate, define, and interpret hypotheses for problems involving uncertainty within established confidence limits. III. Solve statistical problems using software tools and evaluate the sensitivity of the obtained results. IV. Perform analysis of variance (ANOVA) and interpret the results.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	-	Continuous Random Variables, Continuous Probability Distributions, and the Normal Distribution	Lecture
2	-	Applications of Continuous Random Variables, Continuous Probability Distributions, and the Normal Distribution	Lecture
3	-	Standard Normal Distribution	Lecture
4	-	Point and Interval Estimation	Lecture
5	-	Hypothesis Testing	Lecture
6	-	Hypothesis Testing: One-Sample Proportion and Two-Sample Proportion Tests	Lecture
7	-	Hypothesis Testing: One-Sample Mean and Two-Sample Mean Tests	Lecture
8	-	Hypothesis Testing and Applications	Lecture
9	-	Hypothesis Testing and Applications	Lecture
10	-	MID-TERM EXAM	-
11	-	Chi-Square Tests	Lecture
12	-	Chi-Square Methods and Applications	Lecture
13	-	Time Series Analysis	Lecture
14	-	One-Way Analysis of Variance (ANOVA) and Tukey’s HSD Test	Lecture
15	-	Analysis of Variance (ANOVA) and Related Application Examples	Lecture
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Montgomery, D. C., & Runger, G. C. (2019). Applied Statistics and Probability for Engineers (7th ed.). Wiley.
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2020). Statistics for Business and Economics (14th ed.). Cengage Learning.
Devore, J. L. (2015). Probability and Statistics for Engineering and the Sciences (9th ed.). Cengage Learning.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	40		Classical Exam
Short Exam	1	10
Final Exam	1	50		Classical Exam
TOTAL	3	100

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Explains the fundamental engineering concepts of computer science and relates them to the groundwork of computer science.			2

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Uses theoretical and practical knowledge coming from mathematics, probability, statistics and various other branches of life sciences, to find solutions to engineering problems.						5

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Determines the components and the underlying process of a system and designs an appropriate computational model under reasonable constraints.					4
2	Designs a computer-aided conceptual model with modern techniques.	0

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Determines, detects and analyzes the areas of computer science applications and develops appropriate solutions.		1
2	Identifies, models and solves computer engineering problems by applying appropriate analytical methods.		1
3	Determines and uses the necessary information technologies in an efficient way for engineering applications.			2

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Possess the responsibility and ability to design and conduct experiments for engineering problems by collecting, analyzing and interpreting data.	0
2	Possess the ability to conduct effective individual study.				3
3	Takes responsibility as a team work and contributes in an effective way.				3

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	14	3	42
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	1	15	15
Project Work	0	0	0
Seminar	1	3	3
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	1	2	2
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	2	2
Preparation for the Final Exam	0	0	0
Mid-Term Exam	1	2	2
Preparation for the Mid-Term Exam	0	0	0
Short Exam	1	15	15
Preparation for the Short Exam	0	0	0
TOTAL	34	0	123