The following stem-and-leaf plots compare the ages of 30 actors and 30 actresses at the time they won the Oscar award for Best A
ctor or Actress. Actors Stems Actresses 2 146667 98753221 3 00113344455778 88776543322100 4 11129 6651 5 210 6 011 6 7 4 8 0 (a) What is the age of the youngest actor to win an Oscar? years (b) What is the age difference between the oldest and the youngest actress to win an Oscar? years (c) What is the oldest age shared by two actors to win an Oscar?
1 answer:
The image of the stem-and-leaf plots is in the attachment.
Answer: (a) 31 years; (b) 59 years; (c) 56 years
Step-by-step explanation: Steam and leaf is a table that shows the digits of the data value split into a "stem", which represents the first digit, and a "leaf", which is the last digit.
For example, the first row of the table in the attachment, indicate a "stem" 2 and the first number of a "leaf" is 1, so the actress has 21 years.
(a) According to the table, the youngest actor to win an Oscar has a "stem" 3 and the first "leaf" from the right is 1, so the actor has 31 years.
(b) The oldest actress is 80 and the youngest is 21, so difference is:
80 - 21 = 59
The difference is 59 years.
(c) The oldest age shared by 2 actors is 56 years.
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(a) The expected number of should a salesperson expect until she finds a customer that makes a purchase is 0.9231.
(b) The probability that a salesperson helps 3 customers until she finds the first person to make a purchase is 0.058.
Step-by-step explanation:
Let<em> </em>the random variable <em>X</em> be defined as the number of customers the salesperson assists before a customer makes a purchase.
The probability that a customer makes a purchase is, <em>p</em> = 0.52.
The random variable <em>X</em> follows a Geometric distribution since it describes the distribution of the number of trials before the first success.
The probability mass function of <em>X</em> is:
The expected value of a Geometric distribution is:
(a)
Compute the expected number of should a salesperson expect until she finds a customer that makes a purchase as follows:
This, the expected number of should a salesperson expect until she finds a customer that makes a purchase is 0.9231.
(b)
Compute the probability that a salesperson helps 3 customers until she finds the first person to make a purchase as follows:
Thus, the probability that a salesperson helps 3 customers until she finds the first person to make a purchase is 0.058.