Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3MIS 221Probability and Statistics I3+0+03521.09.2023

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Management Information Systems (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course To teach foundational knowledge and skills in the concepts of probability and basic statistical methods
Course Content Introduction to statistics, basic concepts of statistics, data presentation, summary measures,introduction to probability, discrete and continuous probability models
Course Methods and Techniques Lecturing, discussion and submission.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Çiğdem TOPÇU GÜLÖKSÜZ
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Hogg R.V., Craig., McKean J.W. Introduction to Mathematical Statistics, 7th Edition, Pearson Education Inc.,2012.
Rohatgi, V.K., A. K. Md. Ehsanes Saleh. An Introduction to Probability and Statistics, 3rd Edition, John Wiley, 2015.
Ross, S. M. Introduction to Probability Models, 10th Edition, Elsevier, 2010.
Course Notes Paul, N., William, L. C., & Betty, M. T. (2020). Statistics for Business & Economics (-Global Edition).
• Hsu, H. P. (1997). Schaum's outline of theory and problems of probability, random variables, and random processes. New York.

Course Category
Mathematics and Basic Sciences %100

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 % 40
Final examination 1 % 60
Total
2
% 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 5 70
Assignments 14 2 28
Mid-terms 1 2 2
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 5 144

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Define and explain core concepts in statistics, classify different types of data.
2 Understand the foundational principles of probability and the rules governing the calculation of probabilities.
3 Identify, describe, and apply discrete probability distributions, especially the binomial and Poisson distributions.
4 Recognize and utilize continuous probability distributions, notably the uniform and normal distributions.
5 Describe the concept of a sampling distribution and its significance in inferential statistics.
6 Explain and apply the Central Limit Theorem to real-world scenarios.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Statistics: Overview of statistics and its significance.
2 Data Collection and Presentation (Part I); Methods of data collection: observational studies, experiments, and surveys, Organizing data: frequency tables and histograms.
3 Data Collection and Presentation (Part II); Graphical representations: pie charts, bar graphs, and scatterplots, basics of data quality: sources of errors, outliers, and biases.
4 Measures of Central Tendency; Mean, median, mode, Properties and applications in real-world scenarios.
5 Measures of Dispersion; Range, variance, and standard deviation, Coefficient of variation and interpretation
6 Introduction to Probability; Basics of probability, sample spaces, and events, The addition rule and multiplication rule.
7 Midterm Exam
8 Further Probability Concepts; Conditional probability and independent events, Counting principles: permutations and combinations.
9 Discrete Probability Distributions (Part I); Random variables and probability mass functions, Expected value and variance.
10 Discrete Probability Distributions (Part II); Binomial distribution: properties, mean, and variance, Poisson distribution: introduction and applications.
11 Continuous Probability Distributions (Part I); Probability density functions, The uniform distribution.
12 Continuous Probability Distributions (Part II); The normal distribution: properties and applications, Using z-scores to find probabilities and percentiles.
13 Sampling Distributions; Basics of sampling and introduction to sampling distributions, Distribution of the sample mean and the Central Limit Theorem.
14 Estimation and Confidence Intervals; Point estimation, Interval estimation for means and proportions.
15 Introduction to Hypothesis Testing; Formulating null and alternative hypotheses, Type I and Type II errors.
16 Final Exam

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

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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