Q: There are a large number of urns with each urn having as many balls as there are urns. There is exactly 1 red ball within each urn and the rest are white. You are allowed to draw exactly one ball from each of the urns in sequence. If you ever get a red ball, you win a prize. What is the probability that you will win?
The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (And Everone Else!)
A: Assume there are \(n\) urns. This implies that there are \(n\) balls per urn with exactly 1 red ball and \(n-1\) white balls. The probability that you would draw a red ball from a given urn is \(\frac{1}{n}\) implying the probability that you would not win from a given urn is \(1 - \frac{1}{n}\). The probability of not winning at all is
$$ P(\text{no win}) = (1 - \frac{1}{n})\times (1 - \frac{1}{n})\ldots \text{n times}\\ = (1 - \frac{1}{n})^{n}$$
Note, that for large \(n\), the above has a limit which converges to
$$ \lim_{n\to\infty}(1 - \frac{1}{n})^{n} = \frac{1}{e}$$
This further implies that the probability of winning is \( 1 - P(\text{no win}) = \frac{e - 1}{e} = 63\%\)
If you are looking to learn probability, here are some great books to own.
Fifty Challenging Problems in Probability with Solutions (Dover Books on Mathematics)
This book is a great compilation that covers quite a bit of puzzles. What I like about these puzzles are that they are all tractable and don't require too much advanced mathematics to solve.
Introduction to Algorithms
This is a book on algorithms, some of them are probabilistic. But the book is a must have for students, job candidates even full time engineers & data scientists
Introduction to Probability Theory
An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition
The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else!)
Introduction to Probability, 2nd Edition
The Mathematics of Poker
Good read. Overall Poker/Blackjack type card games are a good way to get introduced to probability theory
Let There Be Range!: Crushing SSNL/MSNL No-Limit Hold'em Games
Easily the most expensive book out there. So if the item above piques your interest and you want to go pro, go for it.
Quantum Poker
Well written and easy to read mathematics. For the Poker beginner.
Bundle of Algorithms in Java, Third Edition, Parts 1-5: Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms (3rd Edition) (Pts. 1-5)
An excellent resource (students/engineers/entrepreneurs) if you are looking for some code that you can take and implement directly on the job.
Understanding Probability: Chance Rules in Everyday Life A bit pricy when compared to the first one, but I like the look and feel of the text used. It is simple to read and understand which is vital especially if you are trying to get into the subject
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) This one is a must have if you want to learn machine learning. The book is beautifully written and ideal for the engineer/student who doesn't want to get too much into the details of a machine learned approach but wants a working knowledge of it. There are some great examples and test data in the text book too.
Discovering Statistics Using R
This is a good book if you are new to statistics & probability while simultaneously getting started with a programming language. The book supports R and is written in a casual humorous way making it an easy read. Great for beginners. Some of the data on the companion website could be missing.
The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (And Everone Else!)
A: Assume there are \(n\) urns. This implies that there are \(n\) balls per urn with exactly 1 red ball and \(n-1\) white balls. The probability that you would draw a red ball from a given urn is \(\frac{1}{n}\) implying the probability that you would not win from a given urn is \(1 - \frac{1}{n}\). The probability of not winning at all is
$$ P(\text{no win}) = (1 - \frac{1}{n})\times (1 - \frac{1}{n})\ldots \text{n times}\\ = (1 - \frac{1}{n})^{n}$$
Note, that for large \(n\), the above has a limit which converges to
$$ \lim_{n\to\infty}(1 - \frac{1}{n})^{n} = \frac{1}{e}$$
This further implies that the probability of winning is \( 1 - P(\text{no win}) = \frac{e - 1}{e} = 63\%\)
If you are looking to learn probability, here are some great books to own.
Fifty Challenging Problems in Probability with Solutions (Dover Books on Mathematics)
This book is a great compilation that covers quite a bit of puzzles. What I like about these puzzles are that they are all tractable and don't require too much advanced mathematics to solve.
Introduction to Algorithms
This is a book on algorithms, some of them are probabilistic. But the book is a must have for students, job candidates even full time engineers & data scientists
Introduction to Probability Theory
An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition
The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else!)
Introduction to Probability, 2nd Edition
The Mathematics of Poker
Good read. Overall Poker/Blackjack type card games are a good way to get introduced to probability theory
Let There Be Range!: Crushing SSNL/MSNL No-Limit Hold'em Games
Easily the most expensive book out there. So if the item above piques your interest and you want to go pro, go for it.
Quantum Poker
Well written and easy to read mathematics. For the Poker beginner.
Bundle of Algorithms in Java, Third Edition, Parts 1-5: Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms (3rd Edition) (Pts. 1-5)
An excellent resource (students/engineers/entrepreneurs) if you are looking for some code that you can take and implement directly on the job.
Understanding Probability: Chance Rules in Everyday Life A bit pricy when compared to the first one, but I like the look and feel of the text used. It is simple to read and understand which is vital especially if you are trying to get into the subject
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) This one is a must have if you want to learn machine learning. The book is beautifully written and ideal for the engineer/student who doesn't want to get too much into the details of a machine learned approach but wants a working knowledge of it. There are some great examples and test data in the text book too.
Discovering Statistics Using R
This is a good book if you are new to statistics & probability while simultaneously getting started with a programming language. The book supports R and is written in a casual humorous way making it an easy read. Great for beginners. Some of the data on the companion website could be missing.
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