What is the mathmatical definition of decomposing

Mathematics

2 answers:

Vinil7 [7]4 years ago

7 0

Answer:

Decomposing is an important mathematical concept because it helps students develop an understanding of how addition and subtraction works. Students can put together (compose) or take apart (decompose) objects to represent addition and subtraction.

Idk what else to say

jarptica [38.1K]4 years ago

4 0

It's when we break a number apart. Like as if we take 3,864. And we take each number and put it in a area of the number.

3=Thousands.
8=Hundreds.
6=Tens.
4=Ones.

Fairly simple and easy.

You might be interested in

Does this table represent a function? Why or why not?

baherus [9]

Answer:

its c

Step-by-step explanation:

because on the x value it has the same numbers so its repeating

3 0

3 years ago

Read 2 more answers

Anyone ?<br>Math hw...........

Dovator [93]

Both of these are linear functions:
Distance A is d=2.5h and is proportional to time because the distance is just 2.5 times however many hours you have traveled.
Distance B is d= 2(h-1) + 4. This is not proportional to time but it is a linear function.

7 0

3 years ago

A process is producing a particular part where the thickness of the part is following a normal distribution with a µ = 50 mm and

Hitman42 [59]

Answer:

0.13% probability that this selected sample has an average thickness greater than 53

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