Round to the nearest tenth what is 6.58 divided by 0.9 long division

If there were a total of 195 campers, how many boy campers were there?

kozerog [31]

Well it depends if its all boys at the camp then the answer is 195 boy campers
but if its girls and boys then you would have to spilt it up upon what the question is asking

4 0

3 years ago

Hyperbola question looks easy hurry

Finger [1]

Answer:

Step-by-step explanation:

Center: (F,E)

right vertex: (F+W,E)

slopes of asymptotes: ±J/W

foci: (F±C), C² = W² + J²

C = √(W²+J²)

3 0

3 years ago

Amy and Roger both wash windows at a large downtown building. Any can wash 25 windows in 4 hours. Roger can was 32 windows in 6

Radda [10]

Amy can wash 6.25 windows per hours

rodger can wash 5.3 windows per hour

multiply those numbers by 12 and add them

138.6 windows

7 0

4 years ago

Read 2 more answers

What is the equation for X+3y=16;(-3,-4)

Galina-37 [17]

I hope this helps you

-3+3. (-4)=16

-3-12=16

-15=16 false

7 0

4 years ago

Suppose 46% of politicians are lawyers. If a random sample of size 662 is selected, what is the probability that the proportion

Svet_ta [14]

Answer:

0.9606 = 96.06% probability that the proportion of politicians who are lawyers will differ from the total politicians proportion by less than 4%

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