Josh takes a load of dress shirts to the dry cleaners. The amount he pays for dry cleaning (

Mathematics

2 answers:

Rina8888 [55]3 years ago

8 0

Answer:

$\frac{3}{2}$ is the constant of proportionality in terms of the cost per shirt .

Step-by-step explanation:

Constant of proportionality

Let us assume that x is proportional to y .

$x \propto y$

x = ky

Where k is the constant of proportionality .

As given

Josh takes a load of dress shirts to the dry cleaners.

The amount he pays for dry cleaning f is given by the equation $f = \frac{3}{2} s$

where s is the number of shirts.

Compare $f = \frac{3}{2} s$ with x = ky .

Thus

$Cost\ per\ shirt =\frac{f}{s}= \frac{3}{2}$

$k = \frac{3}{2}$

Therefore $\frac{3}{2}$ is the constant of proportionality in terms of the cost per shirt .

saw5 [17]3 years ago

7 0

The amount he pays is the number of shirts times 3 / 2 (or 1.5)
unless the equation is f=3/(2s) where the cost would then be the number of shirts times two, divided by three

You might be interested in

A manufacturer knows that their items have a normally distributed length, with a mean of 15.4 inches, and standard deviation of

Masteriza [31]

Answer:

0.9452 = 94.52% probability that their mean length is less than 16.8 inches.

Step-by-step explanation:

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

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 z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .