Information Package / Course Catalogue
MATH 228 Probability and Statistics for Engineering II
MEF University
Degree Programs
Industrial Engineering
General Information For Students
Diploma SupplementErasmus Policy Statement
Industrial Engineering
Bachelor Length of the Programme: 4 Number of Credits: 240 TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF: Level 6

ECTS Course Information Package

School/Faculty/Institute Faculty of Engineering
Course Code MATH 228
Course Title in English Probability and Statistics for Engineering II
Course Title in Turkish Mühendislik için Olasılık ve İstatistik II
Language of Instruction EN
Type of Course Exercise,Lecture
Level of Course Introductory
Semester Spring,Fall
Contact Hours per Week
Lecture: 3 Recitation: Lab: 1 Other:
Estimated Student Workload 172 hours per semester
Number of Credits 7 ECTS
Grading Mode Standard Letter Grade
Pre-requisites MATH 227 - Probability and Statistics for Engineering I
Co-requisites None
Expected Prior Knowledge Prior knowledge of basic concepts of probability is expected
Registration Restrictions Only undergraduate students
Overall Educational Objective To acquire basic knowledge of statistical analysis concepts with on hands applications using modern tools
Course Description The aim of the course is to give the fundamentals of statistical analysis. This course introduces the basics of statistics for engineers to summarize numerical and categorical data obtained from surveys, experiments, etc. The topics include different data types, measures of location, variability, shape, and association between variables. The fundamental concepts of estimation, confidence intervals, hypothesis testing and apply appropriate tests for population mean, proportion, variance and difference, independence, and goodness to fit. Students will be able to apply Analysis of Variance and Linear Regression using modern tools.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) Summarize numerical and categorical data;
2) Gain an understanding of the basic concepts of sampling distributions;
3) Design, solve and interpret the results of hypothesis tests;
4) Conduct and analyze the results of experiments and evaluate the accuracy of the results;
5) Function effectively and evaluate the composition, organization, and performance of a team;
6) Organize and deliver effective written and verbal communications.
Program Learning Outcomes/Course Learning Outcomes 1 2 3 4 5 6
1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3) An ability to communicate effectively with a range of audiences
4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies

Relation to Program Outcomes and Competences

N None S Supportive H Highly Related
     
Program Outcomes and Competences Level Assessed by
1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics H Exam
2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors N
3) An ability to communicate effectively with a range of audiences H Project
4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts N
5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives H Project
6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions H Project
7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies N
Prepared by and Date ŞİRİN ÖZLEM , March 2024
Course Coordinator ŞİRİN ÖZLEM
Semester Spring,Fall
Name of Instructor

Course Contents

Week Subject
1) U1, Introduction Data, data types, sources of data
2) U2, Descriptive Statistics Summarizing data for categorical and numerical variables
3) U3, U4, Measures of location, variability and distribution shape
4) U5, U6, Box plots, weighted mean, outliers Measures of association between two variables Summarizing data for two variables
5) U7, Law of large numbers and the central limit theorem
6) U8, Sampling Distributions and Interval Estimation
7) U9, Hypothesis Testing Basics Stages of statistical analysis
8) U10a, U10b, Hypothesis Testing Involving Single Sample Testing population variance
9) U11, Hypothesis Testing Involving Two Samples Distinguishing paired and unpaired samples
10) U12, U13, ANOVA, Testing the ratio of population variances
11) U14, U15, Linear Regression
12) U16, Goodness to Fit test and Testing independence
13) Project studies
14) Project studies
15) Final Exam/Project Presentation Period
16) Final Exam/Project Presentation Period
Required/Recommended ReadingsRequired: Applied Probability and Statistics for Engineers, D.C. Montgomery, G.C. Runger, John Wiley & sons, 2011 Recommended: Probability and Statistics for Engineers, R. L. Sheaffer, M. Mulekar. J.T. McClave, Duxbury Press, 2010; Probability and Statistics for Engineers, R. L. Sheaffer, J.T. McClave, Duxbury Press, 1994
Teaching MethodsLecture/Exercise/
Homework and Projects-
Laboratory WorkStudents will apply the methods they learned using Excel at the laboratory hours.
Computer UseRequired
Other ActivitiesNone
Assessment Methods
Assessment Tools Count Weight
Project 1 % 20
Midterm(s) 2 % 80
TOTAL % 100
Course Administration sirin.ozlem@mef.edu.tr

Course Instructor: Asst. Prof. Şirin Özlem, email: sirin.ozlem@mef.edu.tr., office: A block, 5th floor Pre-lecture videos: We will use udacity videos for this course. We will complete two free lessons (intro to descriptive statistics and intro to inferential statistics). Lecture time will be devoted to discussion, application and additional material that is NOT COVERED ON VIDEOS. Attendance/participation: According to YÖK regulations, students are required to attend at least 70% of the lectures. Students are expected to prepare for the lecture via pre-lecture videos and reading materials and attend the lectures. Formal use of e-mails: The course instructor assumes that any information sent through email will be received in 24 hours, unless a system problem occurs. Grading and evaluation: There will ve midterm exams and a final project Missing midterm exam: With a document of excuse approved by the faculty Missing final exam: Faculty regulations.

ECTS Student Workload Estimation

Activity No/Weeks Hours Calculation
No/Weeks per Semester Preparing for the Activity Spent in the Activity Itself Completing the Activity Requirements
Course Hours 14 1 3 1 70
Laboratory 14 2 1 42
Project 1 20 4 24
Midterm(s) 3 10 2 36
Total Workload 172
Total Workload/25 6.9
ECTS 7