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2 years ago

The round off value of 38.23 to the nearest whole number is?

Mathematics

2 answers:

defon2 years ago

8 0

Answer:

Rounding 38.23 to the nearest whole number would be 38

Step-by-step explanation:

Rounding 38.23 to the nearest whole number would be 38 because the extra 0.23 is less than 0.50

If it was 0.50, the answer would go up to 39.

boyakko [2]2 years ago

5 0

Answer:

Step-by-step explanation:

38.23 is closest to 28, because neither the .2 or .03 is high enough to round the last whole number up

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Susan Marciano invested part of her $37,000 bonus in a fund that paid a 12% profit and invested the rest in stock that suffered

Bad White [126]

Answer:

The amount invested in funds that earned 12% profit is 31000

The amount invested in stock that suffered 5% loss is 6,000.

Step-by-step explanation:

Let x be the amount invested in funds that earned 12%.

Then the amount invested in stock that suffered 5% loss is 37000 - x.

The profit from investment in funds is = 12% of x

= 0.12·x

The loss from investment in stock is = 5% of (37000 - x)

= 0.05 × (37000 - x)

The net profit is 0.12·x - 0.05 × (37000 - x)

As the net profit given in the problem is $3420, we have :

0.12·x - 0.05 × (37000 - x) = 3420

Solving for x, we have :

12·x - 5 × (37000 - x) = 342000 (multiply both sides by 100)

12·x - 185000 + 5·x = 342000

17·x = 527000

⇒ x = 31000

Hence, The amount invested in funds that earned 12% profit is 31000 and the amount invested in stock that suffered 5% loss is 37000 - x = 37000 - 31000 = 6,000.

6 0

3 years ago

If 60% of people said that they like

PilotLPTM [1.2K]

Answer:

40%

Step-by-step explanation:

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3 years ago

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According to the Central Limit Theorem, which statements(s) are TRUE. There may be more than 1 correct answer. A. An increase in

Liono4ka [1.6K]

Answer:

Options A, B and C are correct.

Step-by-step explanation:

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

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