Sample space and probability theory. Density and distribution functions of single and multiple discrete and continuous random variables. Functions of random variables. Expectation, variance and transforms. Independence, covariance and correlation. Central limit theorem, weak/strong law of large numbers. Introduction to random processes. Confidence intervals, hypothesis testing, simple linear regression techniques, chi-square minimization methods. (3 credits)
📊 EE 31100-R meets Tuesdays & Thursdays from 3:30
- 4:45p
in Shepard 20. |
Prerequisites: Calculus III (MATH 20300 or MATH 21300),
with a minimum grade of C.
Course instructor: Tushar Jois (Office hours: Wed 3:30 - 4p in ST638)
Course text: Sheldon M. Ross, Introduction to Probability and Statistics for
Engineers and Scientists, 6th Edition (9780128243466).
Note: The 4th and 5th editions of the above book are also fine. I have ensured that all problems assigned are the same across all three versions.
Each of the above goals corresponds to one unit of the course.
Formative assignments are designed to get you familiar with the material and try out concepts. As they are for practicing, formative assignments are graded only to ensure completion of assigned tasks. However, content from these assignments will appear on the exams. It is important to complete these assignments with full effort to truly comprehend all of the material; attending lectures is insufficient. The following are this course's formative assignments:
Summative assessments on the other hand, are designed to evaluate your progress in the course. These form the majority of your final grade in the course. Content on these assessments will be derived from lecture material and assigned problems. The following summative assessments will be utilized in the course:
The course will be weighted as follows:
| 30% | Midterm Exam 1 (in class) |
| 30% | Midterm Exam 2 (in class) |
| 20% | Final Exam (take home) |
| 10% | Problem Sets (completion, 7 total) |
| 10% | Exit Questions (completion, at least 1 per week) |
The following grade scales will apply to weighted scores, at a minimum:
| 100%: A+ | 99-92%: A | 91-90%: A- |
| 89-88%: B+ | 87-82%: B | 81-80%: B- |
| 79-78%: C+ | 77-72%: C | 71-70%: C- |
| 69-60%: D | < 60%: F |
The instructor may choose to curve all class grades up at the end of the course, and the above cutoffs could shift, which might improve your grade. Note that this is not guaranteed, and would occur at the instructor's sole discretion.
There will be no extra credit assignments in the course.
If you ever have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, I invite you to share directly with me, the department, or university administration. We promise that we will take your communication seriously and seek mutually acceptable resolutions and accommodations. Reporting will never impact your course grade. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).
If you are struggling with anxiety, stress, depression, or other mental health-related concerns, please consider visiting the CCNY Counseling Center. If you are concerned about a friend, please encourage that person to seek out their services.
You are welcome to bring a family member to class on occasional days when your responsibilities require it (for example, if emergency childcare is unavailable, or for the health needs of a relative). Please be sensitive to the classroom environment, and if your family member becomes uncomfortably disruptive, you may leave the classroom and return as needed.
Academic dishonesty is prohibited in The City University of New York. Penalties for academic dishonesty include academic sanctions, such as failing or otherwise reduced grades, and/or disciplinary sanctions, including suspension or expulsion.
Academic integrity is at the core of a college or university education. Faculty assign essays, exams, quizzes, projects, and so on both to extend the learning done in the classroom and as a means of assessing that learning. When students violate the academic integrity policy (i.e., “cheat”), they are committing an act of theft that can cause real harm to themselves and others including, but not limited to, their classmates, their faculty, and the caregivers who may be funding their education. Academic dishonesty confers an unfair advantage over others, which undermines educational equity and fairness. Students who cheat place their college's accreditation and their own future prospects in jeopardy.
On every exam, you will sign the following pledge: “I agree to complete this exam without unauthorized assistance from any person, materials or device.”
Take note of the midterm exam dates. I expect all students to take these exams in person; please let the course staff know of any issues at least two weeks before any potential absences.
If you are unable to keep up with the course, or expect to miss class due to extenuating circumstances, please inform the course staff as soon as possible.
Take my advice: don't fall behind!