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avanturin [10]
2 years ago
12

The weight of an adult swan is normally distributed with a mean of 26 pounds and a standard deviation of 7.2 pounds. A farmer ra

ndomly selected 36 swans and loaded them into his truck. What is the probability that this flock of swans weighs > 1000 pounds?
Mathematics
1 answer:
Snezhnost [94]2 years ago
5 0
Let X denote the random variable for the weight of a swan. Then each swan in the sample of 36 selected by the farmer can be assigned a weight denoted by X_1,\ldots,X_{36}, each independently and identically distributed with distribution X_i\sim\mathcal N(26,7.2).

You want to find

\mathbb P(X_1+\cdots+X_{36}>1000)=\mathbb P\left(\displaystyle\sum_{i=1}^{36}X_i>1000\right)

Note that the left side is 36 times the average of the weights of the swans in the sample, i.e. the probability above is equivalent to

\mathbb P\left(36\displaystyle\sum_{i=1}^{36}\frac{X_i}{36}>1000\right)=\mathbb P\left(\overline X>\dfrac{1000}{36}\right)

Recall that if X\sim\mathcal N(\mu,\sigma), then the sampling distribution \overline X=\displaystyle\sum_{i=1}^n\frac{X_i}n\sim\mathcal N\left(\mu,\dfrac\sigma{\sqrt n}\right) with n being the size of the sample.

Transforming to the standard normal distribution, you have

Z=\dfrac{\overline X-\mu_{\overline X}}{\sigma_{\overline X}}=\sqrt n\dfrac{\overline X-\mu}{\sigma}

so that in this case,

Z=6\dfrac{\overline X-26}{7.2}

and the probability is equivalent to

\mathbb P\left(\overline X>\dfrac{1000}{36}\right)=\mathbb P\left(6\dfrac{\overline X-26}{7.2}>6\dfrac{\frac{1000}{36}-26}{7.2}\right)
=\mathbb P(Z>1.481)\approx0.0693
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masha68 [24]

Answer:

39.17% probability that a woman in her 60s who has a positive test actually has breast cancer

Step-by-step explanation:

Bayes Theorem:

Two events, A and B.

P(B|A) = \frac{P(B)*P(A|B)}{P(A)}

In which P(B|A) is the probability of B happening when A has happened and P(A|B) is the probability of A happening when B has happened.

In this question:

Event A: Positive test.

Event B: Having breast cancer.

3.65% of women in their 60s get breast cancer

This means that P(B) = 0.0365

A mammogram can typically identify correctly 85% of cancer cases

This means that P(A|B) = 0.85

Probability of a positive test.

85% of 3.65% and 100-95 = 5% of 100-3.65 = 96.35%. So

P(A) = 0.85*0.0365 + 0.05*0.9635 = 0.0792

What is the probability that a woman in her 60s who has a positive test actually has breast cancer?

P(B|A) = \frac{0.0365*0.85}{0.0792} = 0.3917

39.17% probability that a woman in her 60s who has a positive test actually has breast cancer

7 0
3 years ago
Write an equation of the line in slope-intercept form through the given points: (0, 2) and (3, 3).
dybincka [34]
The answer is y=1/3x+2
4 0
2 years ago
Find 9% of $2005.00 <br> Can anyone answer this ASAP!!!
Ket [755]

Step-by-step explanation:

= \frac{9}{100} × 2005

= \frac{3609}{20}

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2 years ago
A four year old is going to spin around with his arms stretched out 100 times. From past experience, his father knows it takes a
Sati [7]

Answer:

P(X \geq0.55) \leq 0.22

Step-by-step explanation:

Using central Limit Theorem (CLT), The sum of 100 random variables;

Y=X_1+X_2+...+X_{100} is approximately normally distributed with

Y ~ N (100 × \frac{1}{3^2} ) = N ( 50, \frac{100}{9} )

The approximate probability that it will take this child over 55 seconds to complete spinning can be determined as follows;

N ( 50, \frac{100}{9} )

P(Y>55) =P(Z>\frac{55-50}{10/3})

P(Y>55) =P(Z>1.5)

P(Y>55) =\phi (-1.5)

P(Y>55) =0.0668

Using Chebyshev's inequality:

P(|X-\mu\geq K)\leq \frac{\sigma^2}{K^2}

Let assume that X has a symmetric distribution:

Then:

2P(X-\mu\geq K)\leq) \frac{\sigma^2}{K^2}

2P(X \geq \mu+K)\leq) \frac{\sigma^2}{K^2}

2P(X\geq0.5+0.05)\leq \frac{\frac{1}{\frac{3^2}{100} } }{0.05^2}               where: (\sigma^2 = \frac{1}{3^2/100})

P(X \geq0.55) \leq 0.22

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