• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Explain hypotheses tests

   Contribution to Program Outcomes

   1. Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
   2. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
   3. Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.

   Method of assessment

   1. Written exam
2. Apply hypotheses tests

   Contribution to Program Outcomes

   1. Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
   2. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.

   Method of assessment

   1. Written exam
3. Interpret analysis results

   Contribution to Program Outcomes

   1. Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
   2. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.

   Method of assessment

   1. Written exam

Contents
Week 1:	Introduction to Probability
Week 2:	Basics of Probability
Week 3:	Conditional Probability, Bayes Theorem
Week 4:	Random Variables, Discrete Random Variables
Week 5:	Discrete Distributions
Week 6:	Special Discrete Distributions: Bernoulli Experiment, Binomial Distribution, Hypergeometric Distribution
Week 7:	Continuous Random Variables
Week 8:	Special Continuous Distributions, Midterm Exam.
Week 9:	Basic statistical concepts, data, population, sampling
Week 10:	Data collecting, Frequency Tables, Graphs
Week 11:	Data summarizing, Describing Data with Averages
Week 12:	Describing variability: range, variance, standard deviation
Week 13:	Relationships: Covariance and Correlation
Week 14:	Regression Analysis, Interpretation
Week 15*:	-
Week 16*:	Final exam
Textbooks and materials:	• Introduction to Probability and Statistics by Mendenhall, Beaver and Beaver. • A First Course in Probability by Ross S. Prentice Hall Inc.
Recommended readings:	Ş. B. Şenol, “Uygulamalı Istatistik”, E.Ü. Fen Fakültesi Yayınları, No:150, (1993) * K. Özdemir, “Paket Programlar ile Istatiksel Veri Analizi” 1. Cilt, Kaan Kitabevi, (2002) * R. V. Hogg, A. T. Groig,” Introduction to Mathematical Statistics”, (1995) * J. Neter, W. Wasserman, “Applied Statistics”, (1988)
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	40
Other in-term studies:		0
Project:		0
Homework:		0
Quiz:		0
Final exam:	16	60
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	3	14
Practice, Recitation:	0	0
Homework:	0	0
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	20	1
Mid-term:	2	1
Personal studies for final exam:	20	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)