Graph y=2x-7 (please show where to put the points) <br>

disa [49]

It means multiply if your looking in a math problem

6 0

3 years ago

Suppose that x and y vary inversely and x = 1 when y =5. Write a function that models the inverse variation Graph the function a

Vaselesa [24]

The answer is a function

3 0

2 years ago

Simplify: -1 | 2/3 - 4 | / 5/6

stealth61 [152]

Answer:

Step-by-step explanation:

$- 1 | \frac{2}{3} - 4| \div \frac{5}{6} \\ - 1 | \frac{2}{3} - \frac{4 \times 3}{1 \times 3} | \div \frac{5}{6} \\ - 1 | \frac{2 - 12}{3} | \div \frac{5}{6} \\ - 1 | \frac{ - 10}{3} | \div \frac{5}{6} \\ \frac{10}{3} \div \frac{5}{6} \\ \frac{10}{3} \times \frac{6}{5} \\ \frac{2}{1} \times \frac{2}{1} = 4$

3 0

2 years ago

A distribution of values is normal with a mean of 220 and a standard deviation of 13. From this distribution, you are drawing sa

Paraphin [41]

Answer:

The interval containing the middle-most 48% of sample means is between 218.59 to 221.41.

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 distributied random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$