What is 1.584x10 to the seventh power is in standard notation.

Mathematics

1 answer:

Eva8 [605]1 year ago

3 0

Answer:

None

Step-by-step explanation:

Thats not how math works

The proper answer would be 15,840,000,000

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The cafeteria staff made sandwiches. Each sandwich had either rye or white bread, either ham or turkey, and either cheese or no

Anit [1.1K]

Answer:

1/2 (1 half)

Step-by-step explanation:

The number of different sandwiches is calculated multiplying all of the possibilities for each material used:

rye or white bread: 2 options

ham or turkey: 2 options

cheese or no cheese: 2 options

So the number of different sandwiches that can be made is 2*2*2 = 8.

From these 8 different sandwiches, 4 have cheese and 4 have no cheese, as the staff made a equal number of each type of sandwich.

So, if from 8 different type of sandwiches, 4 have cheese, the chances of Mary getting a sandwich with cheese is 4/8 = 1/2 (1 half).

8 0

2 years ago

An Epson inkjet printer ad advertises that the black ink cartridge will provide enough ink for an average of 245 pages. Assume t

Neko [114]

Answer:

35.2% probability that the sample mean will be 246 pages or more

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 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 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 $s = \frac{\sigma}{\sqrt{n}}$ .