Issue date: 01.10.2019

Revision date:01.10.2019

Revision No:00

DF Board Decision No: -

Course Name: Probability and Statistics

Degree: Undergraduate

Code

Year/Semester

Local Credits

ECTS Credits

Course Implementation, Hours/Week

Course

Tutorial

Laboratory

MATH 222Y

3/1

2,5

3

2

1

-

Department

Marine Engineering

Instructors

Contact Information

Office Hours

Web page

www.pirireis.edu.tr

Course Type

 Compulsory

Course Language

English

Course Prerequisites

-

Course Category by Content, %

Basic Sciences

Engineering Science

Engineering Design

Humanities

50

10

---

40

Course Description

Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables. Topics in statistics include sample mean and variance, distributions, regression and hypothesis testing.

Course Objectives

This course is designed for engineering students to enrich them in probability theory and  to use of probability tools for statistical inference in engineering problems

Course Learning Outcomes

Upon succesful completion of the course students are expected to

1.  know probability theory for both discrete and continuous random variables.
2.  know many applications of probability theory
3. learn how to use probability theory for many problems
4. use probability theory they will able to make statistical inference for many engineering problems

Instructional Methods and Techniques

Lecture, problem solving.

Tutorial Place

-

Co-term Condition

-

Textbook

Probability and Statistics for Engineers and scientists, R.E. Walpole, R.H. Myers, S. L. Myers, K. Ye, Seventh Edition; Prentice Hall

Other References

· Mathematical Statistics, I. Miller, M. Miller, Sixth Edition; Prentice Hall

· A First Course in Probability, S. Ross, Fifth Edition; Prentice Hall

Homework & Projects

---

Laboratory Work

---

Computer Use

---

Other Activities

---

Assessment Criteria

Activities

Quantity

Effects on Grading, %

Attendance

Midterm

1

40

Quiz

3

10

Homework

Term Paper/Project

Laboratory Work

Practices

Tutorial

Seminar

Presentation

Field Study

Final Exam

1

50

TOTAL

100

Effects of Midterm on Grading, %

50

Effects of Final on Grading, %

50

TOTAL

100

ECTS/

WORKLOAD TABLE

Activities

Count

Hours

Total

Workload

Lecture

14

3

42

Midterm

1

8

8

Quiz

3

2

6

Homework

Term Paper/Project

Laboratory Work

Practices

Tutorial

Seminar

Presentation

Field Study

Final Exam

1

12

12

Total Workload

68

Total Workload/25

68/25

Course ECTS Credits

3

Week

Topics

Course Outcomes

1

Review of some Combinatorics: The Basic principle of Counting, Permutations, Combinations, Binomial Coefficients.

Statistical Inference, The role of Probability, The Sample Mean, Measures of Variability, Discrete and Continuous Data, Graphical Methods and Data Description.

I

2

Sample space, Events, Probability of an Event, Some Rules of Probability

I-II

3

Conditional Probability, Independent Events, Baye’s Rule

I-II

4

Concept of a Random Variable, Discrete Probability Distributions, Continuous Probability Distributions.

I-II

5

Joint Probability Distributions, Conditional Probability Distributions, Probability Density Functions.

I-II-III

6

Expected value of a Random Variable, Variance and Covariance.

I-II-III

7

Expected Values and Variances of Linear combinations of Random Variables, Chebyhev’s Theorem, Moments Generating Functions, Conditional Expectations.

I-II-III

8

MIDTERM

9

Discrete Uniform Distribution, Binomial and Multinomial Distributions, Negative Binomial and Geometric Distributions, Poisson Distribution.

I-II-III

10

Continuous Uniform Distribution, Normal Distribution, Gamma and Exponential Distributions, Chi-squared Distribution.

II-III

11

Random Sampling, Some Important Statistics, Sampling Distributions, t-distribution, F-distribution.

II-III

12

Distributions of Sampling Statistics, Parameter Estimation

III-IV

13

Statistical Hypotheses: General Concepts, Testing a Statistical Hypotheses

III-IV

14

Introduction to Regression Analysis

III-IV

Program Outcomes

Level of Contribution

1

2

3

a

An ability to apply knowledge of mathematics, science, and engineering

X

b

An  ability to design and conduct experiments, as well as to analyze and interpret data

X

c

An ability to design a system, component or process to meet desired needs

X

d

Ability to function on multi-disciplinary teams

X

e

An ability to identify, formulate, and solve engineering problems

X

f

An understanding of professional and ethical responsibility

X

g

An ability to communicate effectively

X

h

The broad education necessary to understand the impact of engineering solutions in a global and societal context

X

i

A recognition of the need for, and an ability to engage in life-long learning

X

j

A knowledge of contemporary issues

X

k

An ability to use the techniques, skills and modern engineering tools necessary for engineering practice

X

l

An ability to apply legal, societal and environmental knowledge in maritime transport and in all respective modes of transport operations.

X

m

An ability to interpret and analysis of the data regarding maritime management and operations, recognition and solution of problems for decision making process.

X

Course

Outcomes

I

II

III

IV

Programme Outcomes

a

X

X

b

X

X

X

X

c

X

X

d

e

X

X

X

X

f

g

h

i

j

k

X

X

X

X

l

m

X

X