Information Package
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Program of Mechanical Engineering (English 30%)

MAK331

STATISTICS OF ENGINEERING

Course Code
MAK331
Semester
Sixth Term (Spring)
Theory+Practice
3 + 0
ECTS
5
Course TypeCompulsory Course
Course LevelBachelor's Degree
Year of Study3
SemesterSixth Term (Spring)
ECTS5
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. Can interpret statistical data sets by applying numerical and graphical methods used in summarizing them.
  2. Can solve problems by learning the definition and rules of probability.
  3. Can learn and apply the concepts of random variables and probability and distribution functions of random variables.
  4. Have the ability to select some discrete and continuous probability distributions appropriate to the problems, apply them and relate them to real life problems.
  5. Can define the basic concepts of statistics (mass, sample, random sampling, sampling distribution, etc.).
  6. Can use confidence intervals and sampling distributions in hypothesis tests and make inferences.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme ComponentsNone
Course DescriptionNecessities of experiments, General measurements systems, Experimental methods, Measurements techniques, Basic measurement aperatus, Calibration techniques, Reability concept, Error analysis and uncertanty, Probability concept and basic theorems of probability. Independence, conditional probability and Bayes' rule. Random variable and functions. Considerable discrete and continuous distributions. Distributions of random variables' functions. Subject of statistics and its method. Unit, light, mass, data analysis, central tendency measurements. Sampling and sampling methods, sampling distributions.
Course Contents
WEEK TOPICS
1st Week Introduction to Statistics and Data Analysis
2nd Week Basic Definitions, Interpretations of Probability
3rd Week Probability Axioms, Conditional Probability
4th Week Independence, Bayes' Theorem
5th Week Random Variables, Continuous and Discrete Probability Distributions, Expected Value and Variance
6th Week Binomial Distribution, Poisson Distribution
7th Week Uniform Distribution, Exponential Distribution, Applications in Component and System Reliability
8th Week Midterm Examination
9th Week Normal Distributions
10th Week Applications of Normal Distributions
11th Week Sampling Statistics
12th Week Central Limit Theorem
13th Week Hypothesis Testing
14th Week Hypothesis Testing, Confidence Interval
15th Week Regression (Linear Regression Analysis)
Recommended or Required ReadingLecture Book:

Probability and Statistics for Engineers and Scientists 9th Ed. (2018) by Walpole, R. E., Myers, R. H., Myers S. L., Ye, K. Pearson

Reference Book(s):

Applied Statistics and Probability for Engineers by D. C. Montgomery and G. C. Runger
Planned Learning Activities and Teaching Methods
  • Lecture
  • Problem Solving
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 45
Total(%) 45
Contribution of In-term Studies to Overall Grade(%) 45
Contribution of Final Examination to Overall Grade(%) 55
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam
Preparation for Quiz
Individual or group work
Preparation for Final exam
Course hours
Preparation for Midterm exam
Laboratory (including preparation)
Final exam
Homework
Total Work Load0
Total Work Load / 30 (h)0
ECTS Credit of the Course5
Language of InstructionEnglish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4 LO5 LO6
K1 X X X X X X
K2 X X X X
K3
K4 X X X X
K5 X X X X
K6
K7
K8
K9
K10
K11