| 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 |
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| 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 |
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| Expected Prior Knowledge | Prior knowledge of basic concepts of probability is expected | |||||
| Co-requisites | None | |||||
| 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 Description in Turkish | Bu ders istatistiksel analize giriş dersi olarak tasarlanmıştır. Dersin amacı farklı kaynaklardan elde edilen verilerin analiz yöntemleri konusunda öğrencileri geliştirmektir. Dersin içeriği farklı veri tiplerini, bunların merkezi eğilim, dağılım ve şekil parametrelerinin tespiti, birden fazla değişken arasındaki ilişkinin incelenmesini de içermektedir. Öğrencilerin dersi başarıyla tamamlamaları halinde evren ortalaması, varyansı, ilişkili ve ilişkisiz örneklemler ortalama farkları gibi parametreler için güvenirlik aralığı hesaplayabilmeleri, hipotez testlerini yapabilmeleri ve bu testlerin sonuçlarını yorumlayabilmeleri beklenmektedir. Ayrıca öğrencilerin bağımsızlık testlerini yapabilmeleri, doğrusal regresyon modellerini kurabilmeleri, model parametrelerini hesaplayabilmeleri ve bunları kullanarak istatistiksel tahmin yapabilmeleri beklenmektedir. |
Course Learning Outcomes and CompetencesUpon 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) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||||
| 6) Internalization and dissemination of professional ethical standards. | ||||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | ŞİRİN ÖZLEM , March 2024 |
| Course Coordinator | TUBA AYHAN |
| Semester | Spring,Fall |
| Name of Instructor | Asst. Prof. Dr. ŞİRİN ÖZLEM |
| 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 Readings | Required: 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 Methods | Lecture/Exercise/ | ||||||||||||||||||
| Homework and Projects | - | ||||||||||||||||||
| Laboratory Work | Students will apply the methods they learned using Excel at the laboratory hours | ||||||||||||||||||
| Computer Use | Students will apply the methods they learned using Excel at the laboratory hours | ||||||||||||||||||
| Other Activities | - | ||||||||||||||||||
| Assessment Methods |
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| 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. |
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| 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 | ||||||