Syllabus
A PDF of the syllabus is available here.
Description
The Probability and Statistics in Engineering course introduces students to probability and statistics, emphasizing their importance and utility in solving problems relevant to civil, environmental, mechanical, and industrial engineering. Core areas covered include:
- Basic probability concepts
- The role of uncertainty in engineering design
- Sampling and inference (hypothesis testing, confidence intervals, ANOVA)
- Probability distribution model fitting and testing
- Regression and correlation analyses
Objectives
CEE 260/MIE 273 aims to introduce statistical methods in engineering and develop your ability to analytically apply these methods in your engineering practice.
Outcomes
By the end of this course, you will:
- Understand fundamental concepts of probability, such as independence, expectation, error propagation, and density functions
- Identify, apply, and evaluate appropriate probability models for different systems
- Use statistical methods to describe processes and make inferences about systems from data
- Perform regression analyses, test hypotheses, and calculate confidence intervals to solve engineering problems
- Develop and apply computational and numerical approaches to quantify uncertainty
- Gain proficiency with Python for statistical analysis
Textbook
Required Text:
Diez, David, Cetinkaya-Rundel, Mine, and Barr, Christopher. OpenIntro Statistics (4th Ed.), 2019.
https://leanpub.com/openintro-statistics
Supplementary Reading:
William Navidi. Statistics for Engineers and Scientists. Fifth Edition, McGraw-Hill Education, 2020.
Other Resource:
Kunin et al. Seeing Theory
Prerequisite
MATH 132 (or equivalent).
Policies
The goal is to introduce you to the fundamentals of probability and statistics within an engineering context. Slides will be used and annotated electronically in class, and made available prior to lectures. The course aims to foster an equitable and inclusive learning environment that encourages curiosity and active learning. You are expected to come prepared, having completed readings and homework, and to engage with new material. Frequent questions will be asked, and you are encouraged to ask questions as well.
Overview of Assessments
You will be evaluated based on in-class activities, problem sets, labs, a midterm, and a project. The breakdown is as follows:
| Assessment | Number | Unit % | Total % | Explanation |
|---|---|---|---|---|
| In-Class Activities | 24 | 1 | 20 | 4 absences excused |
| Problem Sets (PS) | 9 | 4 | 36 | |
| Labs | 8 | 2 | 14 | lowest one dropped |
| Midterm Exam | 1 | 12 | 10 | |
| Regression Project | 1 | 20 | 20 | |
| TOTAL | 100 |
Grades are assigned individually, with no predetermined grade spread or curve. After the drop date, grades will not change due to class composition. The grading scale is:
| Grade | Range (%) |
|---|---|
| A | 93–100 |
| A– | 90–92 |
| B+ | 87–89 |
| B | 83–86 |
| B– | 80–82 |
| C+ | 77–79 |
| C | 73–76 |
| C– | 70–72 |
| D | 60–69 |
| F | ≤60 |
In-class Activities
Each lecture includes an activity to be completed individually or in groups, such as short readings, interactive quizzes/polls, or worksheets. Please bring your laptop to all lectures.
Problem Sets
Problem sets are assigned weekly and due Tuesdays at 12:59pm via Gradescope. Solutions are posted after the due date. Some problem sets will also include a guided Python problem (Jupyter Notebook/Colab) with clear objectives. Assignments help you master key probabilistic and statistical functions in Python and practice visualization. You will submit an .ipynb (Jupyter Notebook/Colab) file with your responses. Follow instructions carefully for efficient and fair evaluation.
Note: Late problem sets will not be graded except for emergencies, illness (with proof), or prior permission for exigencies.
Exam
The midterm is a take-home, open-book exam, typically with a 2-hour completion window. Submit as a PDF document.
Project
The project is a structured assignment focused on linear regression, completed in groups of 5. Detailed instructions will be provided after the midterm. Deliverables include a proposal, report, and presentation.