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Mathematics

2 answers:

Norma-Jean [14]3 years ago

8 0

Answer:

It is A

Step-by-step explanation:

prohojiy [21]3 years ago

6 0

Answer: I think it is train b

Step-by-step explanation: it is going faster

You might be interested in

The markup of an MP3 player is $207.20. The markup rate is 60%. What is the cost?

ch4aika [34]

If markup is 60% of the cost, you have

... markup = 0.60×cost

... markup/0.60 = cost

... $207.20/0.60 = cost = $345.33

_____

If markup is 60% of the selling price, the other 40% is the cost. So, the cost is 40/60 = 2/3 of the markup.

... cost = (2/3)×markup = (2/3)×$207.20

... cost = $138.13

_____

Markup can be specified in terms of either selling price or cost. Those are the usual reference values; there could be others. When only the percentage is given, you don't have enough information to work the problem. You need to know what it is a percentage of. (Example problems in your text may tell you the expected interpretation.)

5 0

4 years ago

A door delivery florist wishes to estimate the proportion of people in his city that will purchase his flowers. Suppose the true

kvv77 [185]

Answer:

99.74% probability that the sample proportion will be less than 0.1

Step-by-step explanation:

I am going to use the binomial approximation to the normal to solve this question.

Binomial probability distribution

Probability of exactly x sucesses on n repeated trials, with p probability.

Can be approximated to a normal distribution, using the expected value and the standard deviation.

The expected value of the binomial distribution is:

$E(X) = np$

The standard deviation of the binomial distribution is:

$\sqrt{V(X)} = \sqrt{np(1-p)}$

Normal probability distribution

Problems of normally distributed samples can be 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.

When we are approximating a binomial distribution to a normal one, we have that $\mu = E(X)$ , $\sigma = \sqrt{V(X)}$ .