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Svet_ta [14]
2 years ago
12

How do you solve this step-by-step?

Mathematics
1 answer:
gtnhenbr [62]2 years ago
4 0

Answer:

  • 7 1/20

<u>OAmalOHopeO</u>

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Will mark Brainlest!! <br>Help Please!<br>(1). find the domain and range of the following <br>​
miss Akunina [59]

Answer:

Step-by-step explanation:

You have the domain. It is given as -1≤x≤1

Now all you have to do is figure out the range which is the y value. At first glance I think it might be 3, but that does not look very logical. I'll post this much of it now and be back in under an hour with a more complete answer.

Of course! How silly of me. There is a minimum of y = 1 in the range which comes from x = 0

I've included a graph so you can see how this all works.

So the range = 1 ≤ y ≤ 3

5 0
3 years ago
A firecracker shoots up from a hill 160 feet high, with an initial speed of 90 feet per second. Using the formula H(t) = −16t2 +
Masja [62]

When the firecracker hits the ground, H(t) = 0.0 = -16t^2 + 90t + 160 Using the quadratic formula,t = (-90 +- sqrt(90^2 - 4(-16)(160))) / 2(-16)t = -1.42 ort = 7.04

The negative time is extraneous. Therefore,t = 7.04 s.

Seven Seconds is your answer!

3 0
3 years ago
Read 2 more answers
Multiplying mixed numbers and whole numbers 1 1/2 x 2/1 =
Nezavi [6.7K]

Answer: 3

Step-by-step explanation:

1. Convert the mixed number to fraction:

- Multiply the denominator of the fraction by the whole number.

- Add the product obtained and the numerator of the fraction.

- Write the sum obtained as the numerator and rewrite the original denominator of the fraction.

Then:

1\ 1/2=\frac{(1)(2)+1}{2}=\frac{3}{2}

2. Multiply the numerators.

3. Multiply the denominator.

4. Reduce the fraction.

Then:

(\frac{3}{2})(\frac{2}{1})=\frac{6}{2}=3

5 0
3 years ago
The process standard deviation is 0.27, and the process control is set at plus or minus one standard deviation. Units with weigh
mr_godi [17]

Answer:

a) P(X

And for the other case:

tex] P(X>10.15)[/tex]

P(X>10.15)= P(Z > \frac{10.15-10}{0.15}) = P(Z>1)=1-P(Z

So then the probability of being defective P(D) is given by:

P(D) = 0.159+0.159 = 0.318

And the expected number of defective in a sample of 1000 units are:

X= 0.318*1000= 318

b) P(X

And for the other case:

tex] P(X>10.15)[/tex]

P(X>10.15)= P(Z > \frac{10.15-10}{0.05}) = P(Z>3)=1-P(Z

So then the probability of being defective P(D) is given by:

P(D) = 0.00135+0.00135 = 0.0027

And the expected number of defective in a sample of 1000 units are:

X= 0.0027*1000= 2.7

c) For this case the advantage is that we have less items that will be classified as defective

Step-by-step explanation:

Assuming this complete question: "Motorola used the normal distribution to determine the probability of defects and the number  of defects expected in a production process. Assume a production process produces  items with a mean weight of 10 ounces. Calculate the probability of a defect and the expected  number of defects for a 1000-unit production run in the following situation.

Part a

The process standard deviation is .15, and the process control is set at plus or minus  one standard deviation. Units with weights less than 9.85 or greater than 10.15 ounces  will be classified as defects."

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".  

Solution to the problem

Let X the random variable that represent the weights of a population, and for this case we know the distribution for X is given by:

X \sim N(10,0.15)  

Where \mu=10 and \sigma=0.15

We can calculate the probability of being defective like this:

P(X

And we can use the z score formula given by:

z=\frac{x-\mu}{\sigma}

And if we replace we got:

P(X

And for the other case:

tex] P(X>10.15)[/tex]

P(X>10.15)= P(Z > \frac{10.15-10}{0.15}) = P(Z>1)=1-P(Z

So then the probability of being defective P(D) is given by:

P(D) = 0.159+0.159 = 0.318

And the expected number of defective in a sample of 1000 units are:

X= 0.318*1000= 318

Part b

Through process design improvements, the process standard deviation can be reduced to .05. Assume the process control remains the same, with weights less than 9.85 or  greater than 10.15 ounces being classified as defects.

P(X

And for the other case:

tex] P(X>10.15)[/tex]

P(X>10.15)= P(Z > \frac{10.15-10}{0.05}) = P(Z>3)=1-P(Z

So then the probability of being defective P(D) is given by:

P(D) = 0.00135+0.00135 = 0.0027

And the expected number of defective in a sample of 1000 units are:

X= 0.0027*1000= 2.7

Part 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?

For this case the advantage is that we have less items that will be classified as defective

5 0
3 years ago
Write the product as a mixed number 5x1 7/8​
Naddik [55]

Answer:

10 5/8

Step-by-step explanation:

4 0
3 years ago
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