Develop the statistical thinking and probability foundations needed to analyze data, quantify uncertainty, and solve engineering and computing problems.
Table Of Content
Course Information
| Course Title | Probability & Statistics for Engineering and Computing |
| Course Code | STAT 301 – STAT 305 |
| Field | Probability, Statistics & Data Analysis |
| Level | Undergraduate |
| Course Type | University Teaching |
| Applications | Engineering, Computing & Data Analysis |
| Instructor | Prof. Mostafa A. Elhosseini |
From Data to Decisions
Learn how probability and statistics provide the mathematical foundation for analyzing uncertainty, interpreting data, and making evidence-based decisions.
What You Will Learn
Students will learn how to:
- Organize, summarize, and visualize data.
- Understand fundamental probability concepts.
- Apply counting rules and probability laws.
- Work with discrete and continuous random variables.
- Analyze important probability distributions.
- Calculate expectation, variance, and other descriptive measures.
- Understand sampling distributions and the Central Limit Theorem.
- Construct and interpret confidence intervals.
- Perform statistical hypothesis tests.
- Analyze relationships using correlation and regression.
- Apply statistical methods to engineering and computing problems.
Course Materials
Weekly lecture materials covering the core concepts of probability, statistics, data analysis, and statistical inference.
| Week | Title | Presentation | Handout |
|---|---|---|---|
| 1 | What Is Statistics? | Real-World Applications & Key Concepts Explained | ||
| 2 | When Technology Computes, You Interpret | ||
| 3 | Statistics – What Is It and Why? | ||
| 4 | From Sample to Significance | ||
| 5 | Sampling | ||
| 6 | Convenient Sampling and Random Variance | ||
| 7 | Types of Statistical Populations | ||
| 8 | Independence and Types of Data | ||
| 9 | Types of Data Explained: Why It Matters in Statistics | ||
| 10 | Descriptive Statistics – Mean and Standard Deviation | PDF | |
| 14 | Descriptive Statistics – Outliers, Median, and Trimmed Mean | ||
| 15 | Descriptive Statistics – Mode, Range, Quartiles, and Percentage | ||
| 16 | Frequency Table: Proportion, Percentage & Summary Stats | ||
| 17 | Graphical Summarization – Stem and Leaf Plot | PDF | |
| 18 | Graphical Summarization – Dot Plot Part 1 | ||
| 19 | Graphical Summarization – Dot Plot and Statistical Summaries Part 2 | PDF | |
| 20 | Graphical Summarization – Dot Plot and Skewness Part 3 | ||
| 21 | Graphical Summarization – Histogram Part 1 | ||
| 22 | Graphical Summarization – Histogram and Continuous Data Part 2 | ||
| 23 | Graphical Summarization – Example of a Histogram Part 3 | ||
| 24 | Graphical Summarization – How to Read and Understand Histograms Part 4 | ||
| 25 | Graphical Summarization – Histogram and Class Interval Start & End Points Part 5 | ||
| 26 | Graphical Summarization – Histogram with Unequal Class Widths Part 6 | ||
| 27 | Graphical Summarization – Implementing Histograms Using Python 🐍 Part 7 | ||
| 28 | Graphical Summarization – Boxplots and How to Create Them | PDF | |
| 29 | Symmetry, Skewness, and Statistical Measures | ||
| 30 | Understanding Kurtosis in Data Analysis | ||
| 31 | The Association Between Two Categorical Variables | ||
| 32 | Scatter Plots & Correlation | فهم العلاقات بين المتغيرات الكمية | ||
| 33 | Regression: From Data Points to Real-World Predictions | ||
| 34 | Probability – Fundamental Concepts | PDF | |
| 35 | Probability – Examples to Illustrate Fundamental Concepts | ||
| 36 | Probability – Combining Events | PDF | |
| 37 | Probability – Definition and How to Calculate It | ||
| 38 | Probability – Example of How to Calculate Probability | ||
| 39 | Probability – Example of Axiom 3 and Mutually Exclusive Events | ||
| 40 | Probability – The Addition Rule in Probability | ||
| 41 | Probability – Event A but Not Event B | ||
| 42 | Probability – Solved Examples | ||
| 43 | Counting and Combinatorics in Probability | PDF | |
| 44 | Counting Methods – Combinations | PDF | |
| 45 | Probability – Conditional Probability | PDF | |
| 46 | Probability – Conditional Probability and Independent Events | ||
| 47 | Conditional Probability – The Multiplication Rule | ||
| 48 | Conditional Probability – The Law of Total Probability | ||
| 49 | Conditional Probability – Bayes’ Theorem | PDF | |
| 50 | Conditional Probability – Reliability Analysis | PDF | |
| 51 | Random Variables – Concept and Importance | PDF | |
| 52 | Random Variable – Solved Examples | PDF | |
| 53 | Random Variable – Probability Mass Function (PMF) | PDF | |
| 54 | Random Variable and Cumulative Distribution Function (CDF) | PDF | |
| 55 | Discrete Random Variable – Mean and Variance | PDF | |
| 56 | Discrete Random Variable – Examples of Mean and Variance | PDF | |
| 57 | Continuous Random Variable and Probability Density Function (PDF) | PDF | |
| 58 | Continuous Random Variable – Mean, Variance, and Chebyshev’s Theorem | PDF | |
| 59 | Bernoulli Distribution | Mean & Variance Calculations | PDF | |
| 60 | Binomial Distribution – Conditions & Probability Mass Function | PDF | |
| 61 | Solved Examples | Binomial Distribution | PDF | |
| 62 | Walmart Gender Bias Lawsuit – Case Study | Binomial Distribution | ||
| 63 | Mean and Variance | Binomial Distribution | PDF | |
| 64 | Analyzing Racial Discrimination – Case Study | Binomial Distribution | PDF | |
| 65 | The Normal Distribution – What, Why, and How | PDF | |
| 66 | Normal Distribution and the Z-Table | PDF | |
| 67 | Normal Distribution – Solved Examples | PDF |
Tutorial Sheets
Guided problem-solving sheets designed to reinforce key probability and statistics concepts through worked exercises and practice.
| # | Description | |
|---|---|---|
| Intro to Statistics | Sheet Model Ans | |
| Randomness and Variability | Sheet Model Ans | |
| Types of Data – Distribution | Sheet Model Ans | |
| Summary Statistics | Sheet Model Ans | |
| Box PLot | Sheet Model Ans | |
| 1 | Frequency Table | Sheet Model Ans |
| 2 | Probability – Basic Ideas | Sheet Model Ans |
| Probability – Union//Intersection//Complement//Independent//Mutually Exclusive | Sheet Model Ans |
Course Notes
Concise notes and quick-reference materials designed to support revision of key probability and statistics concepts.
| # | Topic | |
|---|---|---|
| 1 |
Exams & Model Answers
Access selected assessment materials and corresponding model answers for review and exam preparation.
| # | Exam | Model Answer |
|---|---|---|
| Taibah University Semester 1 – Final Exam Oct 2025 | ||
| Taibah University Semester 1 – Final Exam DEC 2025 | ||
| Taibah University Semester 1 – Midterm Exam DEC 2024 | ||
| Taibah University Semester 1 – Midterm Exam Nov 2025 | ||
| Taibah University Semester 1 – Quiz Exam Oct 2024 | ||
| Taibah University Semester 1 – Midterm 2 Exam Nov 2024 | ||
| Taibah University Semester 1 – Midterm 1 Exam Oct 2024 | ||
| Taibah University Semester 1 – Final Exam Oct 2024 | ||
| Taibah University Semester 1 – Midterm Exam Oct 2023 | ||
| Taibah University Semester 1 – Final Exam Dec 2023 | ||
| Taibah University Semester 1 – Quiz Exam 01 2023 | ||
| Taibah University Semester 1 – Quiz Exam 02 2023 | ||
| Taibah University Semester 1 – Quiz Exam 1 Sep 2022 | ||
| Taibah University Semester 1 – Midterm Exam Oct 2022 | ||
| Taibah University Semester 1 – Final Exam Nov 2022 | ||
| Taibah University Semester 1 – Quiz Exam Sep 2021 | ||
| Taibah University Semester 1 – Midterm Exam Oct 2021 | ||
| Taibah University Semester 1 – Final Exam Jan 2022 |
References
For Probability & Statistics for Engineering and Computing, I would list these as the main references:
- Douglas C. Montgomery & George C. Runger, Applied Statistics and Probability for Engineers. Wiley.
- Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers & Keying Ye, Probability & Statistics for Engineers & Scientists. Pearson. This remains a strong fit for engineering, science, and computing students.
- Sheldon M. Ross, A First Course in Probability, 10th ed. Pearson. Excellent for building a stronger probability foundation with many examples and exercises.
- Jay L. Devore, Probability and Statistics for Engineering and the Sciences. Cengage.
- Mario F. Triola, Elementary Statistics. Pearson.
- William Mendenhall, Robert J. Beaver & Barbara M. Beaver, Introduction to Probability and Statistics. Cengage.
Video Lectures
Supplementary video lectures provide step-by-step explanations, worked examples, and problem-solving support for the main topics covered in Probability & Statistics for Engineering and Computing.
Statistics and Probability 101
Supplementary video lectures provide step-by-step explanations, worked examples, and problem-solving support for the main topics covered in Probability & Statistics for Engineering and Computing.
Last Update:
September 10, 2026
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