Introduces the basic notions of Probability: random variables, expectation, conditioning, and the standard distributions (Binomial, Poisson, Exponential, Normal). This course also covers the Law of Large Numbers and Central Limit Theorem as they apply to statistical questions: sampling from a random distribution, estimation, and hypothesis testing.
Ongoing course
Time: MWF 09:10 AM – 10:00 AM
Location: Zoom
Office hours: Click here
Grading
Homework: 25% – Weekly homework (you must turn them in)
Midterm I (02/15): 20%
Midterm II (04/14): 25%
Final (to be announced) : 30%
Homework sets
Homework #4: Published on Canvas
Due to 03/05
Homework #3: Published on Canvas
Due to 02/15
Homework #2: Published on Canvas
Due to 02/08
Homework #1: 2.2, 2.3 and 2.5
Due to 01/29
All problems refer to our textbook
Textbook
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Course notes
Binomial Distribution: concrete problem – MATH 3510
Modeling concrete problems with binomial distributions
Binomial Distribution – MATH 3510
Introduction to the most important discrete distribution
Sample Space with equally likely outcomes – MATH 3510
Formulas and examples when we have a sample space with equally likely outcomes
Law of Large Numbers and Simulations – MATH 3510
Comments on Law of Large Numbers and simulations
Introduction to the course – MATH 3510
General comments about probability theory and our course!
