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2 years ago

8

Triangle MPT with NR || MT is shown below the dimensons are in centimeters which measurements is closest to the length of RT in

centimeters

1 answer:

svlad2 [7]2 years ago

7 0

Step-by-step explanation:

do u have like a photo or sum to explain it better

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D. The length is 32 inches and the width is 40 inches

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A sample of 1700 computer chips revealed that 51% of the chips do not fail in the first 1000 hours of their use. The company's p

hichkok12 [17]

Answer:

The answer is -1.2

Step-by-step explanation:

Recalling the standard deviation formula,

we have : σ = sqrt[ P * ( 1 - P ) / n ]

Where sqrt = square root

n = Number of derivative

Therefore,:

if, H0: p is not 48% vs H1: p1 = 48%

standard deviation is:

σ = sqrt[ P * ( 1 - P ) / n ] = sqrt[ .48*.49/900 ] = .01666333+ z -score is (p-P)/σ

= (.51-.53)/.01666333

The answer therefore = -1.2

ICONCLUSION:

It can therefore be concluded that there is statistical evidence that the proportions are different. H0 accepted in the hypothesis.

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3 years ago

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Equivalent factored form for 10x^2+15x-9=2x^2+x-14

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3 years ago

(a) Find the size of each of two samples (assume that they are of equal size) needed to estimate the difference between the prop

zalisa [80]

Answer:

(a) The sample sizes are 6787.

(b) The sample sizes are 6666.

Step-by-step explanation:

(a)

The information provided is:

Confidence level = 98%

MOE = 0.02

n₁ = n₂ = n

$\hat p_{1} = \hat p_{2} = \hat p = 0.50\ (\text{Assume})$

Compute the sample sizes as follows:

$MOE=z_{\alpha/2}\times\sqrt{\frac{2\times\hat p(1-\hat p)}{n}$

$n=\frac{2\times\hat p(1-\hat p)\times (z_{\alpha/2})^{2}}{MOE^{2}}$

$=\frac{2\times0.50(1-0.50)\times (2.33)^{2}}{0.02^{2}}\\\\=6786.125\\\\\approx 6787$

Thus, the sample sizes are 6787.

(b)

Now it is provided that:

$\hat p_{1}=0.45\\\hat p_{2}=0.58$

Compute the sample size as follows:

$MOE=z_{\alpha/2}\times\sqrt{\frac{\hat p_{1}(1-\hat p_{1})+\hat p_{2}(1-\hat p_{2})}{n}$

$n=\frac{(z_{\alpha/2})^{2}\times [\hat p_{1}(1-\hat p_{1})+\hat p_{2}(1-\hat p_{2})]}{MOE^{2}}$

$=\frac{2.33^{2}\times [0.45(1-0.45)+0.58(1-0.58)]}{0.02^{2}}\\\\=6665.331975\\\\\approx 6666$

Thus, the sample sizes are 6666.

7 0

2 years ago