What is 1342.5414 rounded to the nearest thousandth?

Mathematics

1 answer:

Ainat [17]3 years ago

8 0

Answer:

Note the placement of each digit:

1342.5414 =

1: thousands place

3: hundreds place

4: tens place

2: ones place

5: tenths place

4: hundredths place

1: thousandths place

4: ten thousandths place

You are rounding to the nearest thousandths. Look at the number in the ten thousandths place (4). Note that:

- If the number is 5 or greater, round up

- If the number is 4 or less, round down

In this case, the number is a 4, so you round down.

1342.5414 rounded to the nearest thousandths is 1342.541

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PLZ EXPLAIN HOW TO DO THIS

Hitman42 [59]

Hello! Sorry, this took a minute.

All of the x's are on the same line, and they are equal to 180 degrees.

2x+x+3x+x=180

A straight line equals 180.

Combine like terms.

7x=180

Divide both sides by 7.

25.714... round to 25.7

Now, plug that into the angle that is vertical to 2y. (3x)

3(25.7)= 77.1

So, 3x= 77.1 and 2y= 77.1

Plug that in.

2y=77.1

Divide both sides by 2.

y=38.55

Round that up to 38.6.

"D" is the answer.

I hope this helps!
~kaikers

3 0

4 years ago

A population has mean 187 and standard deviation 32. If a random sample of 64 observations is selected at random from this popul

Zina [86]

Answer:

11.51% probability that the sample average will be less than 182

Step-by-step explanation:

To solve this question, we have 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 random variable X, with mean $\mu$ and standard deviation $\sigma$ , a large sample size can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$