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Mathematics

1 answer:

lara31 [8.8K]2 years ago

4 0

Answer:

am not 100% sure but i think it is A

hope this help

Step-by-step explanation:

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e-lub [12.9K]

Answer:

B is an answer.

6 0

3 years ago

There are 18 stores in California and 12 stores in Nevada the shops opens 10 new stores some are in California, and some are in

Rzqust [24]

That means there would be 28 in California and still 12 in Nevada or if California takes from Nevada it would be California 28 and Nevada 2

4 0

3 years ago

Write the ratio 42 inches to 9 feet as a fraction in simplest form. Explain why you get the same result whether you convert feet

V125BC [204]

Let's convert one of these numbers:
1 foot = 12 inches

9 feet were 9*12=108 inches

so the ratio would be 42: 108

or we can simplify it: divide by 2:
21:54

divide by 3:
7:18

A ratio always stays the same no matter the unit or currency, so it doesn't matter which unit we convert it to.

4 0

2 years ago

A researcher reports survey results by stating that the standard error of the mean is 25 the population standard deviation is 40

bezimeni [28]

Answer:

a) A sample of 256 was used in this survey.

b) 45.14% probability that the point estimate was within ±15 of the population mean

Step-by-step explanation:

This question is solved using the normal probability distribution and the central limit theorem.

Normal probability distribution

When the distribution is normal, we use 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 $s = \frac{\sigma}{\sqrt{n}}$ .