a cactus casts a shadow that is 15ft long. a gate nearby cast a shadow that is 5 ft long. find the height of the cactus

Lucy cuts 4 squares with side length x in. from the corners of a 6 in. by 9 in. cardboard rectangle. She folds the remaining car

Cloud [144]

Answer:

Lucy cuts 4 squares with side length x in. from the corners of a 6 in. by 9 in. cardboard rectangle. She folds the remaining cardboard to make a tray that is x in. high. Write and simplify a polynomial function for the volume V

Step-by-step explanation:

9v

8 0

2 years ago

What is the next number on the sequence, 4, 7, 5, 8, 6, 9,___

Alchen [17]

Answer:

7

Step-by-step explanation:

as you can see, there is a pattern in the numbers. first, you add the term with 3, then subtract the following with 2. therefore, when we get to the sixth term 9, we will subtract 2 from it, thus getting 7. then, we add 7 and 3, to get the next term, which is 10, and so on and so forth.

8 0

3 years ago

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The head of maintenance at XYZ Rent-A-Car believes that the mean number of miles between services is 3639 3639 miles, with a var

Andrei [34K]

Answer:

96.6% probability that the mean of a sample would differ from the population mean by less than 126 miles

Step-by-step explanation:

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

A reminder is that the standard deviation is the square root of the variance.

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