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PilotLPTM [1.2K]
2 years ago
5

The time until recharge for a battery in a laptop computer under common conditions is normally distributed with mean of 265 minu

tes and a standard deviation of 50 minutes. a) What is the probability that a battery lasts more than four hours? Enter your answer in accordance to the item a) of the question statement (Round the answer to 3 decimal places.) b) What are the quartiles (the 25% and 75% values) of battery life? 25% value = Enter your answer; 25% value = _ minutes minutes (Round the answer to the nearest integer.) 75% value = Enter your answer; 75% value = _ minutes minutes (Round the answer to the nearest integer.) c) What value of life in minutes is exceeded with 95% probability? Enter your answer in accordance to the item c) of the question statement (Round the answer to the nearest integer.)
Mathematics
1 answer:
pochemuha2 years ago
8 0

Answer:

a) 0.691 = 69.1% probability that a battery lasts more than four hours

b) 25% value = 231

75% value = 299

c) 183 minutes

Step-by-step explanation:

Problems of normally distributed samples are 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 = 265, \sigma = 50

a) What is the probability that a battery lasts more than four hours?

4 hours = 4*60 = 240 minutes

This is 1 subtracted by the pvalue of Z when X = 240. So

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

Z = \frac{240 - 265}{50}

Z = -0.5

Z = -0.5 has a pvalue of 0.309

1 - 0.309 = 0.691

0.691 = 69.1% probability that a battery lasts more than four hours

b) What are the quartiles (the 25% and 75% values) of battery life?

25th percentile:

X when Z has a pvalue of 0.25. So X when Z = -0.675

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

-0.675 = \frac{X - 265}{50}

X - 265 = -0.675*50

X = 231

75th percentile:

X when Z has a pvalue of 0.75. So X when Z = 0.675

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

0.675 = \frac{X - 265}{50}

X - 265 = 0.675*50

X = 299

25% value = 231

75% value = 299

c) What value of life in minutes is exceeded with 95% probability?

The 100-95 = 5th percentile, which is the value of X when Z has a pvalue of 0.05. So X when Z = -1.645.

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

-1.645 = \frac{X - 265}{50}

X - 265 = -1.645*50

X = 183

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Answer:

(i) The estimated regression equation is;

\hat y ≈ 1.6896 + 0.0604·X

The coefficient of 'X' indicates that \hat y increase by a multiple of 0.0604 for each million dollar increase in sales, X

(ii) The estimated earnings for the company is approximately $4.7096 million

(iii) The standard error of estimate is approximately 29.34

The high standard error of estimate indicates that individual mean do not accurately represent the population mean

(iv) The coefficient of determination is approximately 0.57925

The coefficient of determination indicates that the probability of the coordinate of a new point of data to be located on the line is 0.57925

Step-by-step explanation:

The given data is presented as follows;

\begin{array}{ccc}Sales \ (\$million)&&Earning \ (\$million) \\89.2&&4.9\\18.6&&4.4\\18.2&&1.3\\71.7&&8\\58.6&&6.6\\46.8&&4.1\\17.5&&2.6\\11.9&&1.7\end{array}

(i) From the data, we have;

The regression equation can be presented as follows;

\hat y = b₀ + b₁·x

Where;

b₁ = The slope given as follows;

b_1 = \dfrac{\Sigma(x_i - \overline x) \cdot (y_i - \overline y)}{\Sigma(x_i - \overline x)^2}

b₀ = \overline y - b₁·\overline x

From the data, we have;

{\Sigma(x_i - \overline x) \cdot (y_i - \overline y)} = 364.05

\Sigma(x_i - \overline x)^2} = 6,027.259

\overline y = 4.2

\overline x = 41.5625

∴ b₁ = 364.05/6,027.259 ≈ 0.06040059005

b₀ = 4.2 - 0.06040059005 × 41.5625 ≈ 1.68960047605 ≈ 1.69

Therefore, we have the regression equation as follows;

\hat y ≈ 1.6896 + 0.0604·X

The coefficient of 'X' indicates that the earnings increase by a multiple of 0.0604 for each million dollar increase in sales

(ii) For the small company, we have;

X = $50.0 million, therefore, we get;

\hat y = 1.6896 + 0.0604 × 50 = 4.7096

The estimated earnings for the company, \hat y = 4.7096 million

(iii) The standard error of estimate, σ, is given by the following formula;

\sigma =\sqrt{\dfrac{\sum \left (x_i-\mu  \right )^{2} }{n - 1}}

Where;

n = The sample size

Therefore, we have;

\sigma =\sqrt{\dfrac{6,027.259 }{8 - 1}} \approx 29.34

The standard error of estimate, σ ≈ 29.34

The high standard error of estimate indicates that it is very unlikely that a given mean value within the data is a representation of the true population mean

(iv) The coefficient of determination (R Square) is given as follows;

R^2 = \dfrac{SSR}{SST}

Where;

SSR = The Sum of Squared Regression ≈ 21.9884

SST = The total variation in the sample ≈ 37.96

Therefore, R² ≈ 21.9884/37.96 ≈ 0.57925

The coefficient of determination, R² ≈ 0.57925.

Therefore, by the coefficient of determination, the likelihood of a new introduced data point to located on the line is 0.57925

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A group of students held a fundraiser last year and raised $950 for charity. This year, they raised $1,045.
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Three highways connect city A with city B. Two highways connect city B with city C.
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(a) The probability that there is no open route from A to B is (0.2)^3 = 0.008.
Therefore the probability that at least one route is open from A to B is given by: 1 - 0.008 = 0.992.
The probability that there is no open route from B to C is (0.2)^2 = 0.04.
Therefore the probability that at least one route is open from B to C is given by:
1 - 0.04 = 0.96.
The probability that at least one route is open from A to C is:
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(b)
α The probability that at least one route is open from A to B would become 0.9984. The probability in (a) will become:0.9984\times0.96=0.95846

β The probability that at least one route is open from B to C would become 0.992. The probability in (a) will become:
0.992\times0.992=0.9841

Gamma: The probability that a highway between A and C will not be blocked in rush hour is 0.8. We need to find the probability that there is at least one route open from A to C using either a route A to B to C, or the route A to C direct. This is found by using the formula:
P(A\cup B)=P(A)+P(B)-P(A\cap B)
0.9523+0.8-(0.9523\times0.8)=0.99
Therefore building a highway direct from A to C gives the highest probability that there is at least one route open from A to C.


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