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enyata [817]
3 years ago
12

A fuel pump at a gasoline station doesn't always dispense the exact amount displayed on the meter. When the meter reads 1.000 L,

the amount of fuel a certain pump dispenses is normally distributed with a mean of 1 L and standard deviation of 0.05 L. Let X represent the amount dispensed in a random trial when the meter reads 1.000 L Find P(X > 1.05). You may round your answer to two decimal places
Mathematics
2 answers:
pogonyaev3 years ago
8 0

Answer:

P(X > 1.05) = 1 - 0.8413 = 0.1587 = 15.87%.

Step-by-step explanation:

Problems of normally distributed samples can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

In this problem, we have that:

\mu = 1, \sigma = 0.05

Find P(X > 1.05).

This is 1 subtracted by the pvalue of Z when X = 1. So:

Z = \frac{X - \mu}{\sigma}

Z = \frac{1.05 - 1}{0.05}

Z = 1

Z = 1 has a pvalue of 0.8413

So P(X > 1.05) = 1 - 0.8413 = 0.1587 = 15.87%.

qwelly [4]3 years ago
3 0

Answer:

Step-by-step explanation:

Since the amount of fuel a certain pump dispenses is normally distributed, we would apply the normal distribution formula which is expressed as

z = (x - u)/s

Where

x = represent the amount dispensed in a random trial

u = mean amount

s = standard deviation

From the information given,

u = 1 L

s = 0.05L

We want to find P(X > 1.05) =

1 - P(X ≤ 1.05)

For x = 1.05,

z = (1.05 - 1)/0.05 = 1

Looking at the normal distribution table, the probability corresponding to the z score is 0.84

P(X > 1.05) = 1 - 0.84 = 0.16

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Answer:

P-value is less than 0.999995 .

Step-by-step explanation:

We are given that a driver's education course compared 1,500 students who had not taken the course with 1,850 students who had.

Null Hypothesis, H_0 : p_1 = p_2 {means students who took the driver's education course and those who didn't took have same chances to pass the written driver's exam the first time}

Alternate Hypothesis, H_1 : p_1 > p_2 {means students who took the driver's education course were more likely to pass the written driver's exam the first time}

The test statistics used here will be two sample Binomial statistics i.e.;

                             \frac{(\hat p_1 - \hat p_2)-(p_1 - p_2)}{\sqrt{\frac{\hat p_1(1- \hat p_1)}{n_1} + \frac{\hat p_2(1- \hat p_2)}{n_2} } } ~ N(0,1)

Here, \hat p_1 = No. of students passed the exam ÷ No.of Students that had taken the course

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  Test Statistics = \frac{(\frac{1150}{1850} -\frac{1440}{1500})-0}{\sqrt{\frac{\frac{1150}{1850}(1- \frac{1150}{1850})}{1850} + \frac{\frac{1440}{1500}(1- \frac{1440}{1500})}{1500} } } = -27.38

P-value is given by, P(Z > -27.38) = 1 - P(Z > 27.38)

Now, in z table the highest critical value given is 4.4172 which corresponds to the probability value of 0.0005%. Since our test statistics is way higher than this so we can only say that the p-value for an appropriate hypothesis test is less than 0.999995 .

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