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slamgirl [31]
2 years ago
10

marty has 80 to spend at a sporting goods store. he will spend 56 on a shirt, and then buy some darts. each box of darts cost 6.

he wants to buy as many boxes as possible.
Mathematics
1 answer:
Mariana [72]2 years ago
8 0

Next time, Shay, please include the instructions. I'm assuming that our job here is to determine the largest # of boxes of darts that can be purchased.


Shirt cost + x(box of darts cost) = Initial amount

$56 + x($6/box) = $80


Then 56 + 6x = 80, or 6x= 24, or x = 4. He can buy 4 boxes of darts.


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2 years ago
Read 2 more answers
Lie detectors Refer to Exercise 82. Let Y = the number of people who the lie detector says are telling the truth.
mr_godi [17]

Answer:

a) P(Y\geq 10) = PX \leq 2) = 0.558

b) E(X) = \mu_X = np = 12*0.2 = 2.4

\sigma_X = \sqrt{np(1-p)}=\sqrt{12*0.2*(1-0.2)}=1.386

E(Y) = \mu_Y = np = 12*0.8 = 9.6

\sigma_Y = \sqrt{np(1-p)}=\sqrt{12*0.8*(1-0.8)}=1.386

For this case the expected value of people lying is 2.4 and the complement is 9.6 and that makes sense since we have a total of 12 poeple.

And the deviation for both variables are the same.

Step-by-step explanation:

Assuming this previous info : "A federal report finds that lie detector tests given to truthful persons have probability about 0.2 of  suggesting that the person is deceptive. A company asks 12 job applicants about thefts from previous employers, using  a lie detector to assess their truthfulness. Suppose that all 12 answer truthfully. Let X = the number of people who the lie  detector says are being deceptive"

For this case the distribution of X is binomial X \sim N(n=12, p=0.2)And we define the new random variable Y="the number of people who the lie detector says are telling the truth" so as we can see y is the oppose of the random variable X, and the distribution for Y would be given by:[tex] Y \sim Bin (n=12,p=1-0.2=0.8)

The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".

The probability mass function for the Binomial distribution is given as:

P(X)=(nCx)(p)^x (1-p)^{n-x}

Where (nCx) means combinatory and it's given by this formula:

nCx=\frac{n!}{(n-x)! x!}

Part a

For this case we want to find this probability:

P(Y \geq 10) = P(Y=10)+P(Y=11) +P(Y=12)

And if we find the individual probabilites we got:

P(Y=10)=(12C10)(0.8)^{10} (1-0.8)^{12-10}=0.283

P(Y=11)=(12C11)(0.8)^{11} (1-0.8)^{12-11}=0.206

P(Y=12)=(12C12)(0.8)^{12} (1-0.8)^{12-12}=0.069

And if we replace we got:

P(Y \geq 10) =0.283+0.206+0.069=0.558

And for this case if we find P(X\leq 2)=P(X=0) +P(X=1)+P(X=2)  for the individual probabilites we got:

P(X=0)=(12C0)(0.2)^0 (1-0.2)^{12-0}=0.069

P(X=1)=(12C1)(0.2)^1 (1-0.2)^{12-1}=0.206

P(X=2)=(12C2)(0.2)^2 (1-0.2)^{12-2}=0.283

P(X\leq 2)=0.283+0.206+0.069=0.558

So as we can see we have P(Y\geq 10) = P(X \leq 2) = 0.558

Part b

Random variable X

For this case the expected value is given by:

E(X) = \mu_X = np = 12*0.2 = 2.4

And the deviation is given by:

\sigma_X = \sqrt{np(1-p)}=\sqrt{12*0.2*(1-0.2)}=1.386

Random variable Y

For this case the expected value is given by:

E(Y) = \mu_Y = np = 12*0.8 = 9.6

And the deviation is given by:

\sigma_Y = \sqrt{np(1-p)}=\sqrt{12*0.8*(1-0.8)}=1.386

For this case the expected value of people lying is 2.4 and the complement is 9.6 and that makes sense since we have a total of 12 poeple.

And the deviation for both variables are the same.

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Answer:

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The fraction of the time she spent with paul is (time spent with paul)/(total time spent chatting)

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