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3 years ago

Help plz I really need it

Mathematics

1 answer:

Lena [83]3 years ago

3 0

Answer:

Area = .19625

Step-by-step explanation:

The formula for area of a circle is A=πr²

So, 3.14 x .25² gives you the answer.

=.19625 Hope this helps : )

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Mr. Maze is taking a nap in Pride Park. He wakes up suddenly and wonders how tall the water tower is. The angle of elevation wit

Lisa [10]

Answer:

Step-by-step explanation:

125

6 0

3 years ago

I need 11 and 12 thanks!

adelina 88 [10]

Answer:

11. c > 5

12. option 1

Step-by-step explanation:

5 0

3 years ago

A scale drawing of a square object has a scale of 1 in. : 5 mm. The scale drawing has a length of 2.5 inches. Find the perimeter

luda_lava [24]

Answer:

Perimeter and area of drawing

Perimeter= 10 In

Area = 6.25 In²

Perimeter and area of object

Perimeter = 51.836 mm

Area = 167.936 mm²

Step-by-step explanation:

We are dealing with a square object, so let's talk about the drawing first.

The length of one side = 2.5 In

Perimeter of square= 4L

Perimeter = 4*2.5

Perimeter= 10 In

Area = L*L

Area = 2.5*2.5

Area = 6.25 In²

Letd note that 1 inch =

25.4 millimetres

So 2.5 Inch = 2.5 * 25.4

2.5 In = 63.5 mm.

25.4 mm is to 5mm

63.5 mm is to (63.5*5)/24.5

63.5mm is to 12.959 mm

So the length of actual object is 12.959mm

The perimeter = 4*12.959

Perimeter = 51.836 mm

Area = 12.959²

Area = 167.936 mm²

7 0

3 years ago

Read 2 more answers

Eduardo has purchased a home with an assessed value of $179,000. The property tax rate in his area is 2.3%. What is his monthly

ad-work [718]

Answer:

C. 343.08

Step-by-step explanation:

Convert the tax rate into a decimal and multiply by the house price:

.023(179,000) = 4,117

Divide by 12 (to get monthly rate):

4,117/12 ≈ 343.08

Option C should be the correct answer.

7 0

3 years ago

Motorola used the normal distribution to determine the probability of defects and the number of defects expected in a production

Ghella [55]

Answer:

a) 0.3174 = 31.74% probability of a defect. The number of defects for a 1,000-unit production run is 317.

b) 0.0026 = 0.26% probability of a defect. The expected number of defects for a 1,000-unit production run is 26.

c) Less variation means that the values are closer to the mean, and farther from the limits, which means that more pieces will be within specifications.

Step-by-step explanation:

Normal Probability Distribution:

Problems of normal distributions 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.

Assume a production process produces items with a mean weight of 10 ounces.

This means that $\mu = 10$ .

Question a:

Process standard deviation of 0.15 means that $\sigma = 0.15$

Calculate the probability of a defect.

Less than 9.85 or more than 10.15. Since they are the same distance from the mean, these probabilities is the same, which means that we find 1 and multiply the result by 2.

Probability of less than 9.85.

pvalue of Z when X = 9.85. So

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

$Z = \frac{9.85 - 10}{0.15}$

$Z = -1$

$Z = -1$ has a pvalue of 0.1587

2*0.1587 = 0.3174

0.3174 = 31.74% probability of a defect.

Calculate the expected number of defects for a 1,000-unit production run.

Multiplication of 1000 by the probability of a defect.

1000*0.3174 = 317.4

Rounding to the nearest integer,

The number of defects for a 1,000-unit production run is 317.

Question b:

Now we have that $\sigma = 0.05$

Probability of a defect:

Same logic as question a.

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

$Z = \frac{9.85 - 10}{0.05}$

$Z = -3$

$Z = -3$ has a pvalue of 0.0013

2*0.0013 = 0.0026

0.0026 = 0.26% probability of a defect.

Expected number of defects:

1000*0.0026 = 26

The expected number of defects for a 1,000-unit production run is 26.

(c) What is the advantage of reducing process variation, thereby causing process control limits to be at a greater number of standard deviations from the mean?

Less variation means that the values are closer to the mean, and farther from the limits, which means that more pieces will be within specifications.

3 0

3 years ago