Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3MTHM230Statistics and Probability3+2+04521.10.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Mechanical Engineering (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course The course aims to develop data analysis, interpretation and decision-making skills under uncertainty. In this course, students will be provided with the following specific objectives:
1. Ability to collect, organize, and analyze data.
2. Understanding probability concepts and probability distributions.
3. To be able to interpret data with descriptive and inferential statistical methods.
4. To be able to comprehend logical decision-making processes in situations of uncertainty.
5. To be able to apply statistics and probability to real-world problems.
Course Content What is Statistics, Explanation of Data, Definition of Data: Numerical Measures, Defining Data: Representation of Data, Probability Concepts, Discrete Probability Distributions, Continuous Probability Distributions, Sampling Methods and Central Limit Theorem, Estimation and Confidence Intervals, Single Sample Hypothesis Testing, Chi-Square Test, F Test and Analysis of Variance, Regression and Correlation
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Asist Prof. ÖZER ÖZTÜRK ozer.ozturk@gedik.edu.tr
Name of Lecturers None
Assistants Research Assist. SERKAN MUTLU serkan.mutlu@gedik.edu.tr
Work Placement(s) No

Recommended or Required Reading
Resources 1.Basic Statistics For Business and Economics, Douglas A. LİND, William G. MARCHAL, Samuel A. WATHEN, Fifth Edition, 2006. 2.Uygulamalı İstatistik 1-2, Prof.Dr. Özer SERPER, Filiz Kitabevi, İstanbul-1996.
Course Notes Lectures, Question-Answer, Homework.
Assignments Dönem ödevi
Exams Vize ve Final Sınavları

Course Category
Mathematics and Basic Sciences %30
Engineering %10
Social Sciences %0
Education %0
Science %0
Health %0
Field %60

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Project 1 % 20
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Mid-terms 1 10 10
Practice 5 3 15
Project 1 10 10
Final examination 1 10 10
Total Work Load   Number of ECTS Credits 5 129

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Have the competence to collect, organize, analyze and interpret data sets.
2 Present data effectively with visualization and graphics.
3 Apply descriptive and inferential statistical methods and interpret the results of these methods.
4 Understands and applies the basic principles of probability and probability distributions.
5 Test statistical hypotheses and can evaluate their validity by analyzing their results.
6 Evaluate statistical results from a critical point of view.
7 Understand and can use statistical methods in the real world.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 WHAT IS STATISTICS? 1.1. Introduction 1.2. Types of Statistics 1.3. Descriptive Statistics 1.4. Inferential Statistics 1.5. Types of Variables 1.6. Levels of Measurement
2 DESCRIBING DATA: FREQUENCY 2.1 Distributions and Graphic Presentation 2.2 Relative Frequency Distribution 2.3 Graphic Presentation of a Frequency Distribution
3 DESCRIBING DATA: NUMERICAL MEASURES 3.1 The Population Mean 3.2 The Sample Mean 3.3 Properties of the Arithmetic Mean 3.4 The Weighted Mean 3.5 The Median 3.6 The Mode 3.7 The Geometric Mean 3.8 Why Study Dispersion? 3.9 Measures of Dispersion 3.9.1 Range 3.9.2 Mean Deviation 3.9.3 Variance and Standard Deviation 3.10 Chebyshev's Theorem 3.11 The Empirical Rule
4 DESCRIBING DATA: DISPLAYING AND EXPLORING DATA 4.1 Dot Plots 4.2 Quartiles, Deciles, and Percentiles 4.3 Box Plots 4.4 Skewness 4.5 Describing the Relationship between Two Variables
5 A SURVEY OF PROBABILITYCONCEPTS 5.1 What Is a Probability? 5.2 Approaches to Assigning Probabilities 5.3 Classical Probability 5.4 Empirical Probability 5.5 Subjective Probability 5.6 Some Rules for Computing Probabilities 5.6.1 Rules of Addition 5.6.2 Rules of Multiplication 5.7 Contingency Tables 5.8 Tree Diagrams 5.9 Principles of Counting 5.10 The Multiplication Formula 5.11 The Permutation Formula 5.12 The Combination Formula
6 DISCRETE PROBABILITY DISTRIBUTIONS 6.1 What Is a Probability Distribution? 6.2 Random Variables 6.2.1 Discrete Random Variable 6.2.2 Continuous Random Variable 6.3 The Mean, Variance, and Standard Deviation of a Probability Distribution 6.4 Binomial Probability Distribution 6.5 How Is a Binomial Probability Distribution Computed 6.6 Poisson Probability Distribution
7 CONTINUOUS PROBABILITY DISTRIBUTIONS 7.1 The Family of Uniform Distributions 7.2 The Family of Normal Probability Distributions 7.3 The Standard Normal Distribution 7.4 The Empirical Rule 7.5 Finding Areas under the Normal Curve
8 SAMPLING METHODS AND THE CENTRAL UMIT THEOREM 8.1 Sampling Methods 8.2 Reasons to Sample 8.3 Simple Random Sampling 8.4 Systematic Random Sampling 8.5 Stratified Random Sampling 8.6 Cluster Sampling 8.7 Sampling "Error" 8.8 Sampling Distribution of the Sample Mean 8.9 The Central Limit Theorem 8.10 Using the Sampling Distribution of the Sample Mean
9 ESTIMATION AND CONFIDENCE INTERVALS 9.1 Point Estimates and Confidence Intervals 9.2 Known 0' or a Large Sample 9.3 Unknown Population Standard Deviation and a Small Sample 9.4 A Confidence Interval for a Proportion 9.5 Finite-Population Correction Factor 9.6 Choosing an Appropriate Sample Size
10 ONE-SAMPLE TESTS OF HYPOTHESIS 10.1 What Is a Hypothesis? 10.2 What Is Hypothesis Testing? 10.3 Five-Step Procedure for Testing a Hypothesis 10.4 One-Tailed and Two-Tailed Tests of Significance 10.5 Testing for a Population Mean with a Known Population Standard Deviation 10.6 p-Value in Hypothesis Testing 10.7 Tests Concerning Proportions
11 Kİ-KARE TESTİ 11.1 Ki-Kare Bölünmesi 11.2 Ki-Kare Testi 11.3 Ki-Kare Uygunluk Testi 11.4 Ki-Kare Bağımsızlık Testi 11.5 Ki-Kare Homojenlik Testi
12 F TEST AND ANALYSIS OF VARIANCE (ANOVA) 12.1 F Distribution 12.2 Analysis of Variance 12.3 Analysis of Univariate Variance 12.4 F test
13 REGRESSION AND CORRELATION 12.1 Scattering Diagram 12.2 Simple Linear Regression and Correlation 12.3. Multiple Linear Regression and Correlation
14 General Review

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5
C1 5 5
C2 5 5
C3 5 5
C4 5 5
C5 5 5
C6 5 5
C7 5 5

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