There are approximately 39.37 iches in 1 meter. how many meters are in 170 inches

Find the value of x to the nearest tenth.

Anton [14]

44+44+21+21+50+3×-2 =181

4 0

3 years ago

I need help FAST!!!!

Pavel [41]

Answer:

Step-by-step explanation:

okay, so here are a few methods you can use.

___

1) +

x + 7 = 10 so because this is addition we use the reverse, subtraction. So

x - 7 = 10 - 7 and that equals to 3.

2) -

x - 2 = 10 so because this is subtraction we do the reverse, addition.

x + 2 = 10 + 2 and that equals to 12.

3) x

x times 3 = 21 because this is multiplying we divide.

x/3 = 21/3 and that would be 7.

4) /

x/2 = 10 same as above, reverse is multiplying.

x times 2 = 10 times 2 and thats 20.

GL!

6 0

3 years ago

PLEASE HELP ASAP

muminat

Last month, she had 100.....this month, 30 new members joined and 10 cancelled....thats basically the same as saying 20 new members joined...so there are now 120 members...at 10 per subscription = 120(10) = $ 1200 <=

7 0

3 years ago

The average score of all golfers for a particular course has a mean of 75 and a standard deviation of 4. Suppose 64 golfers play

Vilka [71]

Answer:

0.0228 = 2.28% probability that the average score of the 64 golfers exceeded 76.

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 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 $s = \frac{\sigma}{\sqrt{n}}$ .