TOROS ÜNİVERSİTESİ
Bilgi Paketi Ders Kataloğu

Course Information

STATISTICS
Code	Semester	Theoretical	Practice	National Credit	ECTS Credit
		Hour / Week
MAN213	Fall	3	0	3	5

Prerequisites and co-requisites	None
Language of instruction	English
Type	Required
Level of Course	Bachelor's
Lecturer	Assist.Prof.Dr. Kenan ORÇANLI
Mode of Delivery	Face to Face
Suggested Subject	None
Professional practise ( internship )	None
Objectives of the Course	It is important for business managers to numerically analyze their environmental factors and business functions. The use of statistical techniques in the transformation of data that business managers encounter in the decision-making process can provide great benefits. For this purpose, it will be useful to learn the basic statistical methods as well as their assumptions and limitations. The aim of this course is to understand and apply basic statistical methods used in social sciences.
Contents of the Course	Permutations, Combinatins, Probability, Discrete and Continuous random variables with their probability distributions and expectations, Samping distributions.

Learning Outcomes of Course

#	Learning Outcomes
1	will be able to define data and summarize the relationship between datas
2	will be able to create and use graphs for categorical and numerical data, and to describe relationships between variables
3	will be able to use measures of central tendency, variation, and shape, and use population summary measures
4	will be able to assess outcomes and events in a probability experiment, apply basic rules of probability
5	will be able to apply the concept of statistical independence and use Bayes' Theorem
6	will be able to use mean and standard deviation for discrete and continuous random variables
7	will be able to use and apply some special probability distributions, and the normal approximation to the binomial distribution
8	will be able to determine the skewness and curtosis of datas

Course Syllabus

#	Subjects	Teaching Methods and Technics
1	What is Statistics? Basic Statistics Concepts	Lecturing, Discussion
2	Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation	Lecturing, Discussion
3	Describing Data: Numerical Measures (Measures of Location)	Lecturing, Problem Solving
4	Describing Data: Numerical Measures (Measures of Dispersion)	Lecturing, Problem Solving
5	Describing Data: Numerical Measures (Measures of Division)	Lecturing, Problem Solving
6	A Survey of Probability Concepts	Lecturing, Problem Solving
7	Random Variable and Probability Distribution	Lecturing, Problem Solving
8	Mid-Term Exam.	online Written exam
9	Discrete Probability Distributions (Bernoulli and Binom Probability Distributions)	Lecturing, Problem Solving
10	Discrete Probability Distributions (Poisson and Hipergeometrik Probability Distributions)	Lecturing, Problem Solving
11	Continuous Probability Distributions (The Uniform Probability Distribution and Normal Probability Density Function)	Lecturing, Problem Solving
12	Continuous Probability Distributions (The Standart Normal Probability Distributions)	Lecturing, Problem Solving
13	Sampling, Sampling Methods and Sampling Probability Distribution of Means	Lecturing, Problem Solving
14	Sampling Probability Distribution of Ratios and Error in Sampling	Lecturing, Problem Solving
15	General Review	Lecturing, Problem Solving
16	Final Exam	online Written exam

Course Syllabus

#	Material / Resources	Information About Resources	Reference / Recommended Resources
1	Basic Statistics for Business and Economics	Earl K.Bowen Martin K.Starr	Reference Textbook
2	Introduction to Statistics	David R.Anderson Dennis J. Sweeney	Suggested Textbook
3	Elementary Statistics	Allan G.Bluman	Suggested Textbook

Method of Assessment

#	Weight	Work Type	Work Title
1	20%	Mid-Term Exam	Mid-Term Exam
2	20%	Homework	Homework
3	60%	Final Exam	Final Exam

Relationship between Learning Outcomes of Course and Program Outcomes

#	Learning Outcomes	Program Outcomes	Method of Assessment
1	will be able to define data and summarize the relationship between datas	2͵4͵5	1͵2
2	will be able to create and use graphs for categorical and numerical data, and to describe relationships between variables	2͵4͵5	1͵2
3	will be able to use measures of central tendency, variation, and shape, and use population summary measures	2͵4͵5	1͵2
4	will be able to assess outcomes and events in a probability experiment, apply basic rules of probability	2͵4͵5	1͵2
5	will be able to apply the concept of statistical independence and use Bayes' Theorem	2͵4͵5	1͵2
6	will be able to use mean and standard deviation for discrete and continuous random variables	2͵4͵5	1͵2
7	will be able to use and apply some special probability distributions, and the normal approximation to the binomial distribution	2͵4͵5	1͵2
8	will be able to determine the skewness and curtosis of datas	2͵4͵5	1͵2

PS. The numbers, which are shown in the column Method of Assessment, presents the methods shown in the previous table, titled as Method of Assessment.

Work Load Details

#	Type of Work	Quantity	Time (Hour)	Work Load
1	Course Duration	14	3	42
2	Course Duration Except Class (Preliminary Study, Enhancement)	14	3	42
3	Presentation and Seminar Preparation	0	0	0
4	Web Research, Library and Archival Work	0	0	0
5	Document/Information Listing	0	0	0
6	Workshop	0	0	0
7	Preparation for Midterm Exam	1	16	16
8	Midterm Exam	1	2	2
9	Quiz	0	0	0
10	Homework	1	9	9
11	Midterm Project	0	0	0
12	Midterm Exercise	0	0	0
13	Final Project	0	0	0
14	Final Exercise	0	0	0
15	Preparation for Final Exam	1	27	27
16	Final Exam	1	3	3
				141