Marci purchased the rug shown. To the nearest hundredth, what area of floor space will the rug cover? (π = 3.14)

slamgirl [31]

0.1875 you must divide 3 by 16 the numerator by the denominator

3 0

3 years ago

A produce supplier ships boxes of produce to individual customers. The distribution of weights of shipped boxes is approximately

schepotkina [342]

Answer:

$0.67(4)+ 36$

Option D

Step-by-step explanation:

Given that a producer ships boxes of produce to individual customers.(say x)

X is a normal random variable with mean = 36 and std dev =4 lbs

By definition of std normal variate we know that

$\frac{x-36}{4}$ is N(0,1)

75th percentile of std normal distribution is

0.675

Hence corresponding x would be

$36+0.675(4)$

Option D matches with this value

Hence answer is option D

$0.67(4)+ 36$

5 0

3 years ago

Read 2 more answers

A study of the amount of time it takes a mechanic to rebuild the transmission for a 1992 Chevrolet Cavalier shows that the mean

Dovator [93]

Answer:

B) 0.0069

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