Hi! Kan someone fill in the blanks with either addition, subtraction, multiplication, or division?

Please help out with this geometry homework I was assigned. And what formula do I use?

lozanna [386]

Answer:

$V_{cone} \approx 1,178.97 \: {in}^{3}$

Step-by-step explanation:

Diameter of cone = 16 in

Radius of cone r = 16/2 = 8 in

Height of cone h = 17.6 in

$V_{cone} = \frac{1}{3} \pi {r}^{2}h \\ \\ V_{cone} = \frac{1}{3} \times 3.14 \times {(8)}^{2} \times 17.6 \\ \\ V_{cone} = \frac{1}{3} \times 3,536.896 \\ \\ V_{cone} = 1,178.96533 \\ \\ V_{cone} \approx 1,178.97 \: {in}^{3} \\$

5 0

3 years ago

A population has a mean of 200 and a standard deviation of 50. Suppose a sample of size 100 is selected and x is used to estimat

zmey [24]

Answer:

a) 0.6426 = 64.26% probability that the sample mean will be within +/- 5 of the population mean.

b) 0.9544 = 95.44% probability that the sample mean will be within +/- 10 of the population mean.

Step-by-step explanation:

To solve this question, we need to understand 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 $s = \frac{\sigma}{\sqrt{n}}$ .