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Ymorist [56]
2 years ago
12

Eric can type 80 words in 5 minutes how many words can he type in 1 minute

Mathematics
2 answers:
Gnoma [55]2 years ago
8 0

Answer: 15+1

Step-by-step explanation:

80/5

Hope it works

Ann [662]2 years ago
3 0

Answer:16

Step-by-step explanation:divide 80 by 5 to get 16. Check by multiplying 16*5=80 or

add 16+16+16+16+16=80

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The vertex would be at (-2,7)
7 0
3 years ago
Colin pays £721.45 a year on his car insurance.
Burka [1]

Answer:

655.08

Step-by-step explanation:

<h3>721.45 ×0.092=66.3734</h3><h3>721.45-66.3734=655.0766</h3>
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3 years ago
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nirvana33 [79]
I’d say 21 and -1 :)
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2 years ago
Read 2 more answers
Thompson and Thompson is a steel bolts manufacturing company. Their current steel bolts have a mean diameter of 139 millimeters,
FinnZ [79.3K]

Answer:

0.4010 = 40.10% probability that the sample mean would differ from the population mean by more than 0.8 millimeters

Step-by-step explanation:

To solve this question, we need to understan the normal probability distribution and the central limit theorem.

Normal probability distribution:

Problems of normally distributed samples are 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.

Central limit theorem:

The Central Limit Theorem estabilishes that, for a random variable X, with mean \mu and standard deviation \sigma, the sample means with size n of at least 30 can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}

In this problem, we have that:

\mu = 139, \sigma = 6, n = 40, s = \frac{6}{\sqrt{40}} = 0.9487

Either the sample mean differs by 0.8 mm or less from the population mean, or it differs by more. The sum of these probabilities is decimal 1.

Probability it differs by less than 0.8mm

pvalue of Z when X = 139 + 0.8 = 139.8mm subtracted by the pvalue of Z when X = 139 - 0.8 = 138.2 mm

X = 139.8

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

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{139.8 - 139}{0.9487}

Z = 0.84

Z = 0.84 has a pvalue of 0.7995

X = 138.2

Z = \frac{X - \mu}{s}

Z = \frac{138.2 - 139}{0.9487}

Z = -0.84

Z = -0.84 has a pvalue of 0.2005

0.7995 - 0.2005 = 0.5990

What is the probability that the sample mean would differ from the population mean by more than 0.8 millimeters?

p + 0.5990 = 0.4010

0.4010 = 40.10% probability that the sample mean would differ from the population mean by more than 0.8 millimeters

7 0
3 years ago
A seamstress uses 3/4 yards of material to make a uniform top for band members. If there are 6 3/4 yards of material left on the
dalvyx [7]

Answer:

She can make 9 uniform tops from the material left.

Step-by-step explanation:

Given:

Yards of material left on the bolt = 6\frac{3}{4}

Material requirement for a uniform top for band members = \frac{3}{4}\ yd

To find the number of tops that can be made from the material left.

Solution:

In order to find the number of tops that can be made from 6\frac{3}{4} yards of material, we need to divide 6\frac{3}{4}\ yd  by  \frac{3}{4}\ yd.

Number of uniform tops that can be made is given as:

⇒ 6\frac{3}{4}\div \frac{3}{4}

We first convert mixed number to fractions.

⇒  \frac{27}{4}\div \frac{3}{4}

To divide fractions the fractions are multiplied after flipping the divisor.

⇒ \frac{27}{4}\times \frac{4}{3}

⇒ \frac{27}{3}

⇒ 9

Thus, she can make 9 uniform tops from the material left.

4 0
2 years ago
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