| School/Faculty/Institute |
Graduate School |
| Course Code |
ITC 501 |
| Course Title in English |
Probability, Statistics, and Random Processes |
| Course Title in Turkish |
Probability, Statistics, and Random Processes |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Introductory |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
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| Estimated Student Workload |
177 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
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| Pre-requisites |
None |
| Expected Prior Knowledge |
Basic probability knowledge |
| Co-requisites |
None |
| Registration Restrictions |
Only Graduate Students |
| Overall Educational Objective |
To learn the basic statistical concepts with on hands applications using modern tools to summarize and analyze data via graphical and quantitative tools, to drive conclusions using statistical analysis and 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 students are expected to learn 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 Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Summarize numerical and categorical data by frequency distribution, histograms, and computing descriptive statistics (mean, median, variance) by hand and\or using Excel and\or Google Sheets. Analyze and interpret association between two variables using covariance and correlation coefficient by hand and/or using Excel and\or Google Sheets
2) Understand the basic concepts of sampling distributions, estimation, confidence intervals, and hypothesis testing (type I and II errors), explain the differences among various statistical techniques and identify an appropriate technique for a given set of variables and research questions.
3) Design, solve and interpret the results of hypothesis tests (t-test, z-test, chi-square test, F-test) related to population mean, population proportion, population differences, goodness to fit, tests of independence, and population variances.
4) Design, solve and interpret the results of Analysis of Variance (ANOVA) to compare population means. Analyze numerical data by graphs, create and test the validity of a simple and multiple linear regression models using Excel. Understand the use of regression models in prediction and estimation.
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| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
| 1) An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. |
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| 2) An ability to acquire scientific and practical knowledge in mechatronics and robotics. |
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| 3) A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. |
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| 4) An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. |
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| 5) An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. |
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| 6) An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. |
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| 7) An ability to follow new and developing practices in the profession and to apply them in their work. |
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| 8) An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. |
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| 9) An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. |
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| 10) An understanding of the social and environmental aspects of mechatronics and robotics applications. |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
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Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. |
H |
|
| 2) |
An ability to acquire scientific and practical knowledge in mechatronics and robotics. |
H |
|
| 3) |
A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. |
S |
|
| 4) |
An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. |
N |
|
| 5) |
An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. |
S |
|
| 6) |
An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. |
N |
|
| 7) |
An ability to follow new and developing practices in the profession and to apply them in their work. |
N |
|
| 8) |
An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. |
S |
|
| 9) |
An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. |
N |
|
| 10) |
An understanding of the social and environmental aspects of mechatronics and robotics applications. |
N |
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| Prepared by and Date |
, |
| Course Coordinator |
UTKU KOÇ |
| Semester |
Fall |
| Name of Instructor |
Asst. Prof. Dr. UTKU KOÇ |
Course Contents
| Week |
Subject |
| 1) |
1. Data and Statistics
1.1 Data, data types, sources of data
2. Descriptive Statistics
2.1 Summarizing data for categorical variables |
| 2) |
2. Descriptive Statistics (Student Outcome 1)
2.2 Summarizing data for quantitative variables
2.3 Measures of location
2.4 Measures of variability
2.5 Measures of distribution shape
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| 3) |
2. Descriptive Statistics
2.6 Box plots
2.7 Measures of association between two variables
2.8 Summarizing data for two variables
2.9 The weighted mean and working with grouped data
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| 4) |
3. Sampling Distributions and Interval Estimation
3.1 Point estimation
3.2 Sampling distribution of sample mean
3.3 Law of large numbers and the central limit theorem
3.4 Sampling distribution of sample proportion
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| 5) |
3. Sampling Distributions and Interval Estimation
3.5 Interval estimation for population mean, known variance
3.6 Interval estimation for population mean, unknown variance
3.7 Interval estimation for population differences
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| 6) |
3. Sampling Distributions and Interval Estimation
3.7 Interval estimation for population differences (continued)
3.8 Interval estimation for population variance
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| 7) |
4. Hypothesis Testing Basics
4.1 Null and alternative hypothesis
4.2 Type I and II errors
4.3 Statistical techniques for hypothesis testing
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| 8) |
5. Hypothesis Testing Involving Single Sample
5.1 Testing population mean, known variance
5.2 Testing population mean, unknown variance
5.3 Testing population proportion
5.4 Testing population variance
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| 9) |
6. Hypothesis Testing Involving Multiple Samples
6.1 Testing population differences, known variances
6.2 Testing population differences, unknown variances
6.3 Testing population differences, paired samples
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| 10) |
6. Hypothesis Testing Involving Multiple Samples
6.4 Testing the ratio of population variances
6.5 Goodness to Fit test and Testing independence
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| 11) |
7. Analysis of Variance |
| 12) |
8. Linear Regression
8.1 Simple linear regression
8.2 Prediction and estimation using linear regression
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| 13) |
8. Linear Regression
8.3 Multiple regression
8.4 Logistic regression
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| 14) |
Review on deciding the appropriate tests for different problems
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| 15) |
Final Examination Period |
| 16) |
Final Examination Period |