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 #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!