Welcome

Course information

Course name
Introduction to Probability
Course code
STAT 211
Semester
Fall 2026
Department
Mathematics and Statistics
College
Arts and Sciences
University
Qatar University
Credit hours
3
Prerequisites
STAT 102 and MATH 102
Dr. Mohamed Chaouch

Dr. Mohamed Chaouch

Office
BCR-D217
Phone
4403-4612

0.1 Assessment and grade distribution

The course grade is calculated using the following assessment weights.

Assessment Weight Details
Quizzes 20% Four quizzes; only the best three are counted. No makeup quizzes.
Comprehensive homework 15% Four assignments
Collaborative flipped-classroom activity 3% Selected topic
In-class activities 2% Ongoing
Midterm examination 25% Scheduled around Week 7
Final examination 35% Comprehensive
Total 100%

Final letter grades follow this scale.

Letter grade Percentage Grade points
A 90–100 4.00
B+ 85–<90 3.50
B 80–<85 3.00
C+ 75–<80 2.50
C 70–<75 2.00
D+ 65–<70 1.50
D 60–<65 1.00
F Below 60 0.00

Important attendance warning. Students are expected to attend all classes and are responsible for monitoring and recording their attendance from the first day of class. The maximum permitted absence is 25% of all classes. This 25% limit includes medical absences, social absences, and all other absences. A student who exceeds the limit will receive a failing grade regardless of academic performance. A valid excuse does not increase the 25% allowance; when applicable, University regulations may permit withdrawal rather than continued enrollment.

0.2 Course description

This course introduces the main ideas of probability and random variables. Topics include random experiments, sample spaces, events, axioms and rules of probability, equally likely sample spaces, counting techniques, conditional probability, random variables, expected value, moment generating functions, standard discrete and continuous distributions, and joint, marginal, and conditional distributions for bivariate random variables.

0.3 Student learning outcomes

By the end of this course, students should be able to:

  1. Identify basic probability experiments and interpret them.
  2. Apply the basic axioms and rules of probability.
  3. Distinguish between discrete and continuous random variables.
  4. Describe the main characteristics of standard probability distributions.
  5. Work with joint, marginal, and conditional distributions and related summary measures.

0.4 Delivery and learning approach

The course is designed as an interactive set of notes. Each chapter contains short explanations, worked examples, quick checks, and practice tasks. Students are encouraged to read actively, run the R code, and pause at each checkpoint before opening the solution.

0.5 Learning resources

Main textbook: Miller and Miller, John E. Freund’s Mathematical Statistics with Application

Additional references: Hogg, McKean, and Craig, Introduction to Mathematical Statistics; Mood and Graybill, Introduction to the Theory of Statistics.

0.6 How to use this course

  • Read one chapter at a time.
  • Run the R code chunks and modify them.
  • Try each “Check your understanding” section before opening the answer.
  • Use the chapter summary and practice exercises for review.

0.7 Chapter roadmap

  1. Introduction to R
  2. Techniques of Integration
  3. Set Theory and Counting Principles
  4. Probability Concepts and Conditional Probability
  5. Random Variables and Probability Distributions
  6. Bivariate Random Variables

0.8 Course conventions

Throughout the book, \(\mathbb{P}(A)\) denotes the probability of event \(A\), \(\mathbb{E}(X)\) the expectation of a random variable, and \(\operatorname{Var}(X)\) its variance. Monetary examples use generic currency units unless a currency is stated explicitly.

Recommended workflow: read the explanation, attempt the checkpoint without looking at the answer, run the R chunk, and then change one input to test whether you understand the result.

0.9 Visual guide

The colored boxes have the same meaning throughout the course:

  • yellow introduces a formal definition;
  • green states a theorem, rule, or important mathematical result;
  • blue interprets a formula or explains why it matters;
  • gray contains a worked example;
  • burgundy highlights a finance application;
  • purple highlights an actuarial application.