Which angle pairs in the diagram are supplementary angles? Check all that apply.

nancy spent 7/8 hour working out at the gym she spent 5/7 of that time lifting weights what fraction of an hour did sje spentld

Juli2301 [7.4K]

$\frac{9}{56}\\$ Fraction of hour was spent by NAncy in lifting weights

Step-by-step explanation:

Time spent at the gym by Nancy = $\frac{7}{8}\,hour$

Time spent at lifting weights = $\frac{5}{7}\,hour$

What fraction of hour she spent in lifting weights?

Solving:

Fraction of hour she spent in lifting weights= Time spend at the gym-Time spent at lifting weights

Fraction of hour she spent in lifting weights= $\frac{7}{8}\,hour-\frac{5}{7}\,hour$

$=\frac{49}{56}-\frac{40}{56}\\=\frac{49-40}{56}\\=\frac{9}{56}\\$

So, $\frac{9}{56}\\$ Fraction of hour was spent by Nancy in lifting weights

Keywords: Word Problems involving Fractions

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7 0

2 years ago

What is the volume of a gift box in the shape of a rectangular prism that is 3.5 inches high, 7 inches long, and 6 inches wide

ankoles [38]

Answer:

147 cubic inches

Step-by-step explanation:

volume of the prism is length x breadth x height

3.5 x 7 x 6=147

8 0

3 years ago

Evaluate the expression for h=-54.<br><br> |h| + 30=

insens350 [35]

1. Observe problem

2. Plug in values

|-54|+30=

54+30=

84

3 0

2 years ago

Which is equivalent to 3/8x?

g100num [7]

Answer:

b.

Step-by-step explanation:

hope this helps, could i get brainliest?

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

2 years ago