Which of these situations fit the conditions for using Bernoulli trials? Explain. a) You are rolling 66 dice and need to get at least fourfour 33s to win the game. b) We record the distribution of home stateshome states of customers visiting our website. c) A committee consisting of 1212 men and 88 women selects a delegation of 44 to attend a professional meeting at random. What is the probability they choose all women? d) A study found that 5757% of M.B.A. students admit to cheating. A business school dean surveys all the students in the graduating class and gets responses in which cheating was admitted by 354354 of 542542 students.
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Answer:
Experiments a) and d) fit the conditions for using Bernoulli trials.
Step-by-step explanation:
A Bernoulli trial is one where the variable is random and dichotomic, that is, it only has two possible outcomes, True/Sucess/Yes/etc. or False/Failure/No/etc. Also, each experiment has the same probability of sucess than the one before and the one after, that means, they are independent. This probability can be calculated by dividing the number of sucess cases by the number of total cases.
Experiment a), where you need four 3s is a Bernoulli trial, as getting a 3 is sucess and not getting a 3 is a failure, and each roll of the dice is independent from each other.
Experiment b) is not a Bernoulli trial as they are more than 2 possible outcomes for the home state of the customer (50 in the case of the US).
Experiment c) is not a Bernoulli trial, as they will be chosen at random, but the first woman will have different chances to be chosen than the fourth one (if they are 20 people, the first one will have 1/20 and the fourth 1/17, as one can't be chosen more than one time).
Experiment d) is a Bernoulli trial, as a student either admits cheating or not, and we can assume that every response was independent from each other.
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You first have to find the percentage of the tested group that trained their birds
200 / 600 = 0.333...
Multiply this by the amount of people
200/ 600 * 1500 = 500
The answer is 500 people
Hope this helps!
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The average age of the employees in 2003 is 57.216 years. And, the average age of the employees in 2009 is 59.184 years.
Given that;
The function A(s) given by ,
A (s) = 0.328s + 50
Now for the average age of employees in 2003 and 2009 using the function A(s) = 0.328s + 50, substitute the values of s into the equation.
For the year 2003,
Since s represents the number of years since 1981,
Hence, subtract 1981 from 2003:
s = 2003 - 1981
s = 22
Now substitute this value of s into the function A(s):
A(22) = 0.328 × 22 + 50
A(22) = 7.216 + 50
A(22) = 57.216
Therefore, the average age of the employees in 2003 is 57.216 years.
Similarly, for the year 2009,
s = 2009 - 1981
s = 28
Substituting this value into the function:
A(28) = 0.328 × 28 + 50
A(28) = 9.184 + 50
A(28) = 59.184
Hence, the average age of the employees in 2009 is 59.184 years.
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Final answer:
The mathematical problem involves calculating the average age of employees at a company for the years 2003 and 2009 using the linear function A(s), where 'A(s)' represents the average age and 's' is the number of years since 1981. The calculated average ages for the employees in the years 2003 and 2009 are approximately 57 and 59 years, respectively.
Explanation:
The subject is mathematics, specifically linear functions. Based on the equation A(s) = 0.328s + 50, where 'A(s)' represents the average age of the employees and 's' represents the number of years since 1981. In the year 2003, s would be 22 (2003-1981) and in 2009, s would be 28 (2009-1981).
Substituting these values of 's' into the function gives:
For 2003, A(22) = 0.328*22 + 50 = 57.216
For 2009, A(28) = 0.328*28 + 50 = 59.184
Therefore, the average age of the employees at the company in 2003 and 2009 were approximately 57 and 59 years, respectively.
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Answer:
4 emails per minute
Step-by-step explanation:
20/5=4
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Answer:
You have 14.8 grams of fat in total.
Step-by-step explanation:
6.8 + 8 = 14.8
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Shadow pricing refers to the practice of accounting the prince of securities not on their assigned market value (as might be expected) but by their amortized costs. This can also be considered an "artificial" price assigned to a non-priced asset or accounting entry.
In this optimization model, we find a number of resource constraints which limit the changes to the resources. It is expected that these resources would not exceed the amount allocated for each particular constraint. The shadow price of a resource constraint would be zero in this example because the amount used would be less than the amount available. This means that it can fit within the established parameters, and therefore, would not need to be assigned a shadow price.