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solniwko [45]
2 years ago
5

You are about to visit Jamestown in North Dakota, to see the world's largest buffalo monument. You know that it snows $25\%$ of

the time in North Dakota, and you are thinking about bringing your parka. It turns out that you have three friends who live in North Dakota, but they are not very trustworthy: Each tells the truth $\frac{2}{3}$ of the time, and lies for the remaining $\frac{1}{3}.$ You call your three friends, and each friend tells you that it is indeed snowing in North Dakota. Given this information, what is the probability that it is actually snowing in North Dakota
Mathematics
1 answer:
RideAnS [48]2 years ago
3 0

Answer:

The probability that it is actually snowing in North Dakota is 17%.

Step-by-step explanation:

Note: The data in the question is not properly stated. The complete question is therefore represented to correct this as follows:

You are about to visit Jamestown in North Dakota, to see the world's largest buffalo monument. You know that it snows 25% of the time in North Dakota, and you are thinking about bringing your parka. It turns out that you have three friends who live in North Dakota, but they are not very trustworthy: Each tells the truth 2/3 of the time, and lies for the remaining 1/3. You call your three friends, and each friend tells you that it is indeed snowing in North Dakota. Given this information, what is the probability that it is actually snowing in North Dakota?

The explanation to the answer is now given as follows:

The probability that it is actually snowing in North Dakota can be calculated using the following formula:

Pa = Pr * Pt ........................... (1)

Where;

Pa = the probability that it is actually snowing in North Dakota = ?

Pr = the prior probability that it snows in North Dokota = 25%, or 0.25

Pt = Probability of saying the truth that it snows = 2/3 = 0.67

Substituting the value into equation (1), we have:

Pa = 0.25 * 0.67

Pa = 0.17, or 17%

Therefore, the probability that it is actually snowing in North Dakota is 17%.

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An certain brand of upright freezer is available in three different rated capacities: 16 ft3, 18 ft3, and 20 ft3. Let X = the ra
ruslelena [56]

Answer:

E(X)=16\cdot 0.3+18\cdot 0.1+20\cdot 0.6=18.6

E(X^2)=16^2\cdot 0.3+18^2\cdot 0.1+20^2\cdot 0.6=349.2

V(X) = E(X^2)-[E(X)]^2=349.2-(18.6)^2=3.24

The expected price paid by the next customer to buy a freezer is $466

Step-by-step explanation:

From the information given we know the probability mass function (pmf) of random variable X.

\left|\begin{array}{c|ccc}x&16&18&20\\p(x)&0.3&0.1&0.6\end{array}\right|

<em>Point a:</em>

  • The Expected value or the mean value of X with set of possible values D, denoted by <em>E(X)</em> or <em>μ </em>is

E(X) = $\sum_{x\in D} x\cdot p(x)

Therefore

E(X)=16\cdot 0.3+18\cdot 0.1+20\cdot 0.6=18.6

  • If the random variable X has a set of possible values D and a probability mass function, then the expected value of any function h(X), denoted by <em>E[h(X)]</em> is computed by

E[h(X)] = $\sum_{D} h(x)\cdot p(x)

So h(X) = X^2 and

E[h(X)] = $\sum_{D} h(x)\cdot p(x)\\E[X^2]=$\sum_{D}x^2\cdot p(x)\\ E(X^2)=16^2\cdot 0.3+18^2\cdot 0.1+20^2\cdot 0.6\\E(X^2)=349.2

  • The variance of X, denoted by V(X), is

V(X) = $\sum_{D}E[(X-\mu)^2]=E(X^2)-[E(X)]^2

Therefore

V(X) = E(X^2)-[E(X)]^2\\V(X)=349.2-(18.6)^2\\V(X)=3.24

<em>Point b:</em>

We know that the price of a freezer having capacity X is 60X − 650, to find the expected price paid by the next customer to buy a freezer you need to:

From the rules of expected value this proposition is true:

E(aX+b)=a\cdot E(x)+b

We have a = 60, b = -650, and <em>E(X)</em> = 18.6. Therefore

The expected price paid by the next customer is

60\cdot E(X)-650=60\cdot 18.6-650=466

4 0
2 years ago
Choose the product. -7p 3(4p 2 + 3p - 1)
DIA [1.3K]

Answer:

14p -3

Step-by-step explanation:

-7p 3(4p 2+3p - 1)

3x4= 12p

3x3=9p

12+9= 21

21-7=14p

3x2=6

3x-3=-9

-9 +6= -3

= 14p -3

hope this helps :)

7 0
1 year ago
A group of friends has gotten very competitive with their board game nights. They have found that overall, they each have won an
Amanda [17]

Answer: \mu_x=18\text{ hours}

\sigma_x=4\text{ hours}

Step-by-step explanation:

We know that mean and standard deviation of sampling distribution is given by :-

\mu_x=\mu

\sigma_x=\dfrac{\sigma}{\sqrt{n}}

, where \mu = population mean

\sigma =Population standard deviation.

n= sample size .

In the given situation, we have

\mu=18\text{ hours}

\sigma=6\text{ hours}

n= 2

Then, the expected mean and the standard deviation of the sampling distribution will be :_

\mu_x=\mu=18\text{ hours}

\sigma_x=\dfrac{\sigma}{\sqrt{n}}=\dfrac{6}{\sqrt{2}}=4.24264068712\approx4  [Rounded to the nearest whole number]

Hence, the the expected mean and the standard deviation of the sampling distribution :

\mu_x=18\text{ hours}

\sigma_x=4\text{ hours}

7 0
2 years ago
a city us growing at the rate of 0.9% annually. if there were 3619000 residents in yhe city in 1994, find how many to the neares
BlackZzzverrR [31]
For this case we have the following equation:
 y = 3619000 (2.7) ^ 0.009t
 We must evaluate the equation for the year 2000.
 Therefore, we must replace the following value of t:
 t = 2000 - 1994
 t = 6
 Substituting we have:
 y = 3619000 (2.7) ^ (0.009 * 6)
 y = 3818407.078
 Round to the nearest ten thousand:
 y = 3820000
 Answer:
 
3820000 residents are living in that city in 2000
8 0
2 years ago
A recipe for a dessert for 6 people requires 120g of sugar. How much sugar would I need for a dessert 9 people?
zzz [600]

Answer:

180g of sugar

Step-by-step explanation:

3 0
2 years ago
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