Course Name Code Semester T+U Hours Credit ECTS
Engineering Statistics-I ENM 205 3 4 + 0 4 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Compulsory
Course Coordinator Dr.Öğr.Üyesi GÜLTEKİN ÇAĞIL
Course Lecturers Dr.Öğr.Üyesi GÜLTEKİN ÇAĞIL, Dr.Öğr.Üyesi TİJEN ÖVER ÖZÇELİK,
Course Assistants
Course Category
Course Objective

Introducing students to the probability theory, and teaching basic statistical concepts

Course Content

Organizing and analyzing data, introduction to probability, rules of probability, random variables

# Course Learning Outcomes Teaching Methods Assessment Methods
1 Student will be able to classify raw data, determine frequency distribution and calculate central location measurements and variability of data Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
2 Student will be able to construct visual data displays including the histogram and box plot according to central location and variability measurements, and interpret frequency distribution of data Lecture, Question-Answer, Drilland Practice, Demonstration, Testing, Homework,
3 Student will be able to calculate moments of data, and interpret skewness and kurtosis of distribution. Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
4 Student will be able to understand and describe sample spaces and events for random experiments with graphs, tables, lists Discussion, Drilland Practice, Lecture, Question-Answer, Testing, Homework,
5 Student will be able to interpret probabilities and use probabilities of outcomes to calculate probabilities of events in discrete/ continuous sample spaces and and calculate conditional probabilities of events Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
6 Student will be able to determine the independence of events and use independence to calculate probabilities Lecture, Question-Answer, Discussion, Drilland Practice, Testing, Homework,
7 Student will be able to use Bayes’ theorem to calculate conditional probabilities Lecture, Question-Answer, Discussion, Drilland Practice, Testing, Homework,
8 Student will be able to understand random variables Lecture, Question-Answer, Discussion, Drilland Practice, Testing, Homework,
9 Student will be able to determine probabilities from (discrete/continuous) probability mass functions and the reverse Lecture, Question-Answer, Discussion, Drilland Practice, Testing, Homework,
10 Student will be able to determine probabilities from cumulative distribution functions and cumulative distribution functions from probability mass functions, and the reverse Lecture, Question-Answer, Discussion, Drilland Practice, Testing, Homework,
11 Student will be able to calculate means and variances for discrete/continuous random variables Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
12 Student will be able to use joint probability mass functions and joint probability density functions to calculate probabilities, and calculate marginal and conditional probability distributions from joint probability distributions Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
13 Student will be able to interpret and calculate covariances and correlations between random variables Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
14 Student will be able to calculate probabilities, determine means and variances for each of the discrete /continuous probability distributions presented Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
15 Student will be able to select an appropriate discrete/continuous probability distribution to calculate probabilities in specific applications Lecture, Question-Answer, Discussion, Drilland Practice, Demonstration, Testing, Homework,
Week Course Topics Preliminary Preparation
1 Introduction to basic concepts of statistics
2 Organizing and analyzing data, Graphical representations and measures
3 Measures of Central Tendency
4 Deviation, Distribution, Variability
5 Asymmetry and kurtosis Measurements
6 Independent events, Conditional Probability, Bayes´ theorem
7 Random variables and distribution of a random variable
8 Conditional Probability, multiplication and Addition rule, and Bayes' Theorem
9 Random Variable, Discrete and Continuous Probability Density Function
10 Probability Distribution Function
11 Distribution of Two or More Dimensional Random Variables
12 Compound Probability Distribution Function and Mariginal Functions
13 Expected Value and Moments
14 Covariance and Correlation
Resources
Course Notes <p>www.gultekincagil.com</p> <p>lecture notes published on this link</p>
Course Resources
  1. Serper, Ö., “Applied Statistics - 1”, Ezgi Kitapevi, 2014.
  2. Serper, Ö., “Applied Statistics - 2”, Ezgi Kitapevi, 2014.
  3. Ersöz, F., Ersöz, T., “Statistical Data with Analysis IBM SPSS, Elit Publications, 2018
  4. Arslan, İ., Statistical Programming with R ”, Pusula Yayıncılık ve İletişim, 2018
  5. Topal, B., Probability Statistics Lecture Notes
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 Engineering graduates with sufficient knowledge background on science and engineering subjects of their related area, and who are skillful in implementing theoretical and practical knowledge for modelling and solving engineering problems. X
2 Engineering graduates with skills in identifying, describing, formulating and solving complex engineering problems, and thus,deciding and implementing appropriate methods for analyzing and modelling. X
3 Engineering graduates with skills in designing a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; for this purpose, skills in implementing modern design methods. X
4 Engineering graduates with skills in developing, selecting and implementing modern techniques and tools required for engineering applications as well as with skills in using information technologies effectively. X
5 Engineering graduates with skills in designing and conducting experiments, collecting data, analyzing and interpreting the results in order to evaluate engineering problems. X
6 Engineering graduates who are able to work within a one discipline or multi-discipline team,as well as who are able to work individually
7 Engineering graduates who are able to effectively communicate orally and officially in Turkish Language as well as who knows at least one foreign language
8 Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology
9 Engineering graduates with well-structured responsibilities in profession and ethics
10 Engineering graduates having knowledge about practices in professional life such as project management, risk management and change management, and who are aware of innovation and sustainable development. X
11 Engineering graduates having knowledge about universal and social effects of engineering applications on health, environment and safety, as well as having awareness for juridical consequences of engineering solutions.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 50
1. Kısa Sınav 10
1. Ödev 30
2. Kısa Sınav 10
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 10 10
Assignment 6 6 36
Performance Task (Laboratory) 1 5 5
Final examination 1 5 5
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Total Workload 152
Total Workload / 25 (Hours) 6.08
dersAKTSKredisi 6