Find the sum of the digits 10^15−1.

GEOMETRY. Identify the angles that each have a measure of 117º

sergiy2304 [10]

Answer:

It should be 4, 6, and 8

Step-by-step explanation:

Hope This Helped

6 0

2 years ago

Simplify : (a+b)² Thanks

Eddi Din [679]

Answer:

(a+b)(a+b)

=a^2 + ab + ab + b^2

=a^2 + 2ab + b^2

6 0

2 years ago

Read 2 more answers

How many even 3-digit positive integers can be written using the digits 1,2,4,7, and 8?

N76 [4]

Looking at the question, we can see we have 5 different numbers that can go in any order.

5 * 4 * 3

The above is basically saying there are 5 different numbers that can be multiplied by 4 other numbers. Those 2 can be multiplied by 3 different ones, leaving us with a 3 digit positive integer.

5 * 4* 3 = 60

4 0

2 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 $s = \sqrt{\frac{p(1-p)}{n}}$