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AVprozaik [17]
3 years ago
5

The number of surface flaws in a plastic roll used for auto interiors follows a Poisson distribution with a mean of 0.05 flaw pe

r square foot . Each car contains 10 ft2 of the plastic roll and ten (10) of the cars are sold to a particular rental agency. a) What is the probability that there are no flaws in a given car’s interior
Mathematics
1 answer:
ElenaW [278]3 years ago
5 0

Answer:

0.6065

Step-by-step explanation:

Probability mass function of probability distribution : P(X=x)=\frac{e^{-\lambda} \times \lambda^x}{x !}

a mean of 0.05 flaw per square foot

Each car contains 10 sq.feet of the plastic roll

Mean = 0.05

Mean = \lambda = 0.05 \times 10=0.5

We are supposed to find What is the probability that there are no flaws in a given car’s interior i.e,P(X=0)

Substitute the value in the formula

P(X=0)=\frac{e^{-0.5} \times (0.5)^0x}{0 !}

P(X=0)=\frac{e^{-0.5} \times (0.5)^0}{1}

P(X=0)=0.6065

Hence the probability that there are no flaws in a given car’s interior is 0.6065

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1 year ago
A group of researchers are interested in the possible effects of distracting stimuli during eating, such as an increase or decre
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Using the t-distribution, it is found that since the p-value of the test is of 0.0302, which is <u>less than the standard significance level of 0.05</u>, the data provides convincing evidence that the average food intake is different for the patients in the treatment group.

At the null hypothesis, it is <u>tested if the consumption is not different</u>, that is, if the subtraction of the means is 0, hence:

H_0: \mu_1 - \mu_2 = 0

At the alternative hypothesis, it is <u>tested if the consumption is different</u>, that is, if the subtraction of the means is not 0, hence:

H_1: \mu_1 - \mu_2 \neq 0

Two groups of 22 patients, hence, the standard errors are:

s_1 = \frac{45.1}{\sqrt{22}} = 9.6154

s_2 = \frac{26.4}{\sqrt{22}} = 5.6285

The distribution of the differences is has:

\overline{x} = \mu_1 - \mu_2 = 52.1 - 27.1 = 25

s = \sqrt{s_1^2 + s_2^2} = \sqrt{9.6154^2 + 5.6285^2} = 11.14

The test statistic is given by:

t = \frac{\overline{x} - \mu}{s}

In which \mu = 0 is the value tested at the null hypothesis.

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t = \frac{25 - 0}{11.14}

t = 2.2438

The p-value of the test is found using a <u>two-tailed test</u>, as we are testing if the mean is different of a value, with <u>t = 2.2438</u> and 22 + 22 - 2 = <u>42 df.</u>

  • Using a t-distribution calculator, this p-value is of 0.0302.

Since the p-value of the test is of 0.0302, which is <u>less than the standard significance level of 0.05</u>, the data provides convincing evidence that the average food intake is different for the patients in the treatment group.

A similar problem is given at brainly.com/question/25600813

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