Two dice are thrown. Use the sample space (2.4) to answer the following questions. (a) What is the probability of being able to form a two-digit number greater than 33 with the two numbers on the dice? (Note that the sample point 1, 4 yields the two-digit number 41 which is greater than 33, etc.) (b) Repeat part (a) for the probability of being able to form a two-digit number greater than or equal to 42 (c) Can you find a two-digit number (or numbers) such that the probability of being able to form a larger number is the same as the probability of being able to form a smaller number? [See note, part (a).]

Short Answer

Expert verified
(a) 1/2, (b) 5/12, (c) The number is 36.

Step by step solution

01

Title - Understanding the Sample Space

When two dice are thrown, each die has 6 faces. Therefore, the total number of possible outcomes is 6 × 6 = 36. The outcomes can be represented as pairs (x, y) where x and y vary from 1 to 6. The sample space S = {(1,1), (1,2), …, (6,6)}.
02

Title - Identifying Two-Digit Numbers Greater than 33 (Part a)

To form a two-digit number greater than 33, consider x as the tens digit and y as the units digit. We need to check pairs where 10x + y > 33. Find and count all pairs satisfying 10x + y > 33.
03

Title - Probability Calculation for Part (a)

Identify valid pairs: (4,1), (4,2), ..., (4,6); (5,1), (5,2), ..., (5,6); (6,1), (6,2), ..., (6,6). Total such outcomes = 18. Probability P = Number of favorable outcomes / Total possible outcomes = 18 / 36 = 1/2.
04

Title - Identifying Two-Digit Numbers Greater than or Equal to 42 (Part b)

For numbers greater than or equal to 42, check pairs where 10x + y >= 42. Identify these pairs.
05

Title - Probability Calculation for Part (b)

Valid pairs are: (4,2), (4,3), ..., (4,6); (5,1), (5,2), ..., (5,6); (6,1), (6,2), ..., (6,6). Total such outcomes = 15. Probability P = 15 / 36 = 5/12.
06

Title - Finding Two-Digit Number with Equal Probabilities (Part c)

Want P(10x + y > k) = P(10x + y < k). Both probabilities should be equal for some k. Calculate probabilities stepwise and compare.
07

Title - Verification of Equal Probabilities (Part c)

Finding k = 36 satisfies P(10x + y > 36) = P(10x + y < 36) = 18/36 = 1/2. Therefore, 36 is the number that splits equal probability for larger and smaller numbers.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Sample Space
When discussing probability, especially with dice, it’s important to understand the concept of the sample space. The sample space is a set of all possible outcomes. For two dice, each die has 6 faces. When thrown together, each face on the first die can pair with each face on the second, giving us a total of 36 possible outcomes. These pairs can be represented as (x, y), where both x and y can be any number from 1 to 6. So, for two thrown dice, the sample space S consists of pairs like (1,1), (1,2), all the way to (6,6). Identifying all these possibilities is the foundation of calculating probabilities.
Favorable Outcomes
Favorable outcomes are the set of outcomes that satisfy our event of interest. For example, in exercise (a), we need to find outcomes where we can form a two-digit number greater than 33 using the numbers rolled on two dice. To build a two-digit number, consider the first number (x) as the tens place and the second number (y) as the units place. We need pairs where the number 10x + y is greater than 33. By listing all valid combinations like (4,1), (4,2), ..., (6,6), we find that there are 18 favorable outcomes. It’s these specific pairs that we use to determine the probability for this particular scenario.
Two-Digit Numbers
Forming two-digit numbers with dice involves creative thinking. In exercise (a), a two-digit number, more than 33, needs to be structured as 10 times the first roll plus the second roll. This brings in a compelling twist as we consider our pairs like (4,1) which gives 41, and (6,5) which gives 65. Moving on to exercise (b), we need pairs such that the two-digit number is at least 42. Combinations like (4,2) producing 42 start to become valid now. Hence, the new numbers stretch from 42 all the way to 66. This detailed breakdown helps understand why certain numbers qualify and how probabilities change as criteria change.
Probability Calculation
Once we have our sample space and know our favorable outcomes, we can easily calculate probabilities. Probability itself is the ratio of the number of favorable outcomes to the total number of possible outcomes. For example, in exercise (a), we have 18 favorable two-digit numbers greater than 33 out of a total of 36 possible outcomes. So, the probability P = 18 / 36 = 1/2. Similarly, for exercise (b), the probability of forming a number greater than or equal to 42 is found using 15 favorable outcomes over 36, giving us P = 15 / 36 = 5/12. Understanding this division and simplification helps in grasping the core of probability calculation.

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Most popular questions from this chapter

(a) Suppose you have two quarters and a dime in your left pocket and two dimes and three quarters in your right pocket. You select a pocket at random and from it a coin at random. What is the probability that it is a dime? (b) Let \(x\) be the amount of money you select. Find \(E(x)\). (c) Suppose you selected a dime in (a). What is the probability that it came from your right pocket? (d) Suppose you do not replace the dime, but select another coin which is also a dime. What is the probability that this second coin came from your right pocket?

(a) Suppose that Martian dice are regular tetrahedra with vertices labeled 1 to \(4 .\) Two such dice are tossed and the sum of the numbers showing is even. Let \(x\) be this sum. Set up the sample space for \(x\) and the associated probabilities. (b) Find \(E(x)\) and \(\sigma_{x}\). (c) Write the exact binomial expression for the probability of exactly fifteen \(2^{\prime}\) s in 48 tosses of a Martian die. Evaluate it by calculator. (d) Evaluate (c) using the normal approximation. (e) Evaluate (c) using the Poisson distribution.

Five cards are dealt from a shuffled deck. What is the probability that they are all of the same suit? That they are all diamonds? That they are all face cards? That the five cards are a sequence in the same suit (for example, \(3,4,5,6,7\) of hearts)?

Define the sample variance by \(s^{2}=(1 / n) \sum_{i=1}^{n}\left(x_{i}-\bar{x}\right)^{2} .\) Show that the expected value of \(s^{2}\) is \([(n-1) / n] \sigma^{2} .\) Hints: Write $$ \begin{aligned} \left(x_{i}-\bar{x}\right)^{2} &=\left[\left(x_{i}-\mu\right)-(\bar{x}-\mu)\right]^{2} \\ &=\left(x_{i}-\mu\right)^{2}-2\left(x_{i}-\mu\right)(\bar{x}-\mu)+(\bar{x}-\mu)^{2} \end{aligned} $$ Find the average value of the first term from the definition of \(\sigma^{2}\) and the average value of the third term from Problem 2, To find the average value of the middle term write $$ (\bar{x}-\mu)=\left(\frac{x_{1}+x_{2}+\cdots+x_{n}}{n}-\mu\right)=\frac{1}{n}\left[\left(x_{1}-\mu\right)+\left(x_{2}-\mu\right)+\cdots+\left(x_{n}-\mu\right)\right] $$ show by Problem \(7.12\) that $$ E\left[\left(x_{i}-\mu\right)\left(x_{j}-\mu\right)\right]=E\left(x_{i}-\mu\right) E\left(x_{j}-\mu\right)=0 \quad \text { for } \quad t \neq j $$ and evaluate \(E\left[\left(x_{i}-\mu\right)^{2}\right]\) (same as the first term). Collect terms to find $$ E\left(s^{2}\right)=\frac{n-1}{n} \sigma^{2} $$

A circular garden bed of radius \(1 \mathrm{~m}\) is to be planted so that \(N\) seeds are uniformly distributed oser the circular area. Then we can talk about the number \(n\) of seeds in some particular area \(A\), or we can call \(n / N\) the probability for any one particular seed to be in the area \(A\). Find the probability \(F(r)\) that a seed (that is, some particular seed) is within \(r\) of the center.

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