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quester [9]
3 years ago
14

A merchant bought some shirts for $120. The next day the price charged for each shirt was reduced by $1. The merchant calculated

that, at the sale price, he could have bought 10 more shirts for $120. How many shirts did he buy originally?
Mathematics
1 answer:
Goshia [24]3 years ago
6 0

Answer:

The merchant buys 30 shirts originally.

Step-by-step explanation:

Let us assume that the merchant bought x numbers of shirts in $120.

So, the cost for each shirt is $\frac{120}{x}.

Now, if the cost for each shirt is reduced by 1$, then he would have bought 10 shirts more i.e. (x + 10) shirts in $120.

So, we can write the following equation as  

(\frac{120}{x} - 1)(x + 10) = 120

⇒(120 - x)(x + 10) = 120x

⇒ 120x - 10x + 1200 - x² = 120x

⇒ x² +10x - 1200 = 0

⇒ x² + 40x - 30x - 1200 = 0

⇒(x + 40)(x - 30) = 0

⇒ x = - 40 or x = 30

But x can not be negative.

Hence, the merchant buys 30 shirts originally. (Answer)

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Answer:

The pvalue of the test is 0.0124 < 0.1, which means that there is sufficient evidence at the 0.1 level to support the testing firm's claim.

Step-by-step explanation:

An automobile manufacturer has given its van a 59.5 miles/gallon (MPG) rating. An independent testing firm has been contracted to test the actual MPG for this van since it is believed that the van has an incorrect manufacturer's MPG rating:

At the null hypothesis, we test if the mean is the same, that is:

H_0: \mu = 59.5

At the alternate hypothesis, we test that it is different, that is:

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The test statistic is:

z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}

In which X is the sample mean, \mu is the value tested at the null hypothesis, \sigma is the standard deviation and n is the size of the sample.

59.5 is tested at the null hypothesis:

This means that \mu = 59.5

After testing 250 vans, they found a mean MPG of 59.2. Assume the population standard deviation is known to be 1.9.

This means that n = 250, X = 59.2, \sigma = 1.9

Value of the test statistic:

z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}

z = \frac{59.2 - 59.5}{\frac{1.9}{\sqrt{250}}}

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The pvalue of the test is the probability of finding a mean that differs from 59.5 by at least 0.3, which is P(|Z|>-2.5), which is 2 multiplied by the pvalue of Z = -2.5.

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The pvalue of the test is 0.0124 < 0.1, which means that there is sufficient evidence at the 0.1 level to support the testing firm's claim.

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