What is 4,130,304 rounded to the nearest thousandth?

Cristina describes her shape.she says it has 3 equal sides that are 4 centimeters in lengths.it has no right angels.do your best

Snowcat [4.5K]

Three equal sides and no right angels is a triangle, since the sides are all the same it is an equilateral triangle.

5 0

3 years ago

There 6 pounds of apples in one bag. Make a table to show how many pound of apples are in 6 bags. How do you your answer is easo

kherson [118]

Answer:

36 Pounds of Apples.

Step-by-step explanation:

Pounds: 0 6 12 18 24 30 36

0 1 2 3 4 5 6

Bags

This answer is reasonable because each bag has 6 pounds of apples in them, and there are 6 bags. 6 x 6 = 36 Pounds.

Oh yeah, and the picture is what I was going to send you (as the graph) but then I realized it wasn't accurate for your question

I really tried hard on this, hope this helps you out!

4 0

3 years ago

it is known that the population proton of utha residnet that are members of the church of jesus christ 0l6 suppose a random samp

Lady_Fox [76]

Answer:

0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

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 normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes 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 $s = \frac{\sigma}{\sqrt{n}}$ .

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean $\mu = p$ and standard deviation $s = \sqrt{\frac{p(1-p)}{n}}$