ESE 326-Probability & Statistics for Engineers

Description

The purpose of this course is to equip students with a mindset and toolkit to engage with real-world data in a meaningful and rigorous way. Probability theory is a mathematically rich field that gives fruitful context to many techniques built in earlier math classes.

The course has (loosely) three sections:

• Weeks 1-3: Probability Foundations

• Weeks 4-10: Random Variables & Distributions

• Weeks 11-15: Descriptive Statistics & Hypothesis Testing

Just like modern statistics, the course will be interlaced with computational methods (primarily using R).

Timings

• Lecture: MW 10-11:20am in Whitaker 100

• Office hours: M 3:30-5pm, Tu 4-5pm, W 11:30-12:30pm in Green 2155

The AIs for this course and their recitation times are:

• Yuxiao Wang- Tuesdays 12-1pm, Brauer 2010

• Wensi Li- Tuesdays 1-2pm, Brauer 2010

• Mae Martel- Tuesdays 3-4pm, Brauer 2010

• Zaid Ahmed- Tuesdays 4-5pm, Brauer 2010

• Praneel Panchigar- Fridays 9-10am, Green 1117.

Recitations are weekly starting 9/5.

Resources

The main resource for the course is:

• Probability, Statistics, and Data: A Fresh Approach Using R, Darrin Speegle & Bryan Clair (freely available online, also as a physical book)

We may also draw on:

• Introduction to Probability and Statistics for Engineers and Scientists, Sheldon Ross (freely available online)

Note that this is a freely available resource originally published through Elsevier - I suggest you take a look at this website describing the many issues with Elsevier's business practices.

• An evocative description of a similar course at UPenn.

Assessment

There are five types of work to be submitted during the course:

• Reading quizzes: weekly T/F quizzes submitted through Gradescope (two attempts per quiz, graded for completion).

• Homework: ~biweekly homework sets submitted through Gradescope (including a component using R).

• Reflections: ~monthly reflections on your experience and growth through the semester.

• Exams: two exams, both in class, one halfway through and one on the final day of instruction.

• Projects: two projects, one social and one computational; in the first you will critique uses of data in the media, in the second you will use computer methods to analyse real-world data sets.

Grade

• Participation (20%)

• Projects (25%)

• Homework (25%)

• Exam #1 - Wednesday 10/18 in class (15%)

• Exam #2 - Wednesday 12/6 in class (15%)

Participation: This includes completion of weekly reading quizzes and monthly self-reflections and attendance to six AI sessions. I will allow for one missed reflection and two missed quizzes without impacting your grade. After this point, each missed reflection, quiz or session counts as -1% of your grade.

Resurrection final: We will use a resurrection final in this course. This means that we will calculate two grades: G1 = grade calculated as above, and G2 = grade calculated with exam #1 counting 0% and exam #2 counting 30%. Your final grade will be max(G1, G2).

Piazza points: Each time you post a question or answer about the course content (i.e. not just administrative) on Piazza you will receive 1 bonus point. These count as 1 point towards your overall homework score up to a maximum of 10 points. For example, if you dropped 15 points on homework throughout the semester and asked 5 questions on Piazza, your score for homework would be as if you only dropped 10 points. The deadline for Piazza points is 11:59pm on 12/10.

Virtual testing: I will offer a virtual option for exams in circumstances such as personal emergencies and quarantine due to sickness or potential exposure to covid-19.

Late submissions: Homework and projects are due at 11:59pm on the day of their deadline. I will accept submissions up to one hour late subject to a 10% penalty (of your total score, for instance if you scored 90% this would be counted as 81%).

Makeups: Because of the features above I don't allow for makeups under any circumstances.

Homework: Each homework contains a praxis prompt where you will practice an academic skill: communication (via recording a video solution to one problem), collaboration (partnering with someone else to complete a problem and providing feedback), education (preparing a mock lesson plan to explain a concept).

Evaluations: Feedback is incredibly important to making this class thrive. Everyone will get a 0.5% grade boost if (on average) 70% of the class responds to evaluations at various points in the semester.

Integrity

• Attempting to cheat in this course is unacceptable and will be strongly penalised. A first offense will be penalised with a zero grade on the relevant piece of assessment. A second offense will be penalised with an immediate fail grade.

• Collaboration is permitted (actually encouraged!) on homework assignments, however each student must write up solutions in their own words.  Please write the names of any other students you have collaborated with at the top of each assignment. Significant similarities between submissions from different students that fail to mention any collaboration counts as an act of cheating and will be penalised as such.

Syllabus (tentative)