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3 years ago

What is the circumference of a circle whose diameter is 20?

Mathematics

1 answer:

Sergeeva-Olga [200]3 years ago

3 0

Answer:

C≈62.83

Step-by-step explanation:

Use the formula C=πd

Plug in 20 for d and simplify

You then get 62.8318530718 in a calculator

So, you round it to 62.83

You might be interested in

Simplify the answer -12 ÷ 3 × (-8 + (-4)² - 6) + 2 I need the steps for the equation on how to solve it.

aleksley [76]

Answer:

-6

Step-by-step explanation:

We use PEMDAS to solve this,

so P stands for parentheses, so that's where we start.

We first, square the innermost parentheses with the exponent which is the E in PEMDAS, then then the outer parentheses

-12/3*(-8+16-6)+2

-12/3*(2)+2

Now we divide as in Division in PEMDAS.

-4*2+2

Now we multiply as in Multiplication in PEMDAS.

-8+2

Now we add as in A for Addition

-6

In PEMDAS, Multiplication doesn't always come before division, and same for addition and subtraction.

7 0

3 years ago

How do you find the answer so confused

alexandr402 [8]

For which question, I can help,
<span>for number 9, parallel lines have the same exact slope, so divide each of them by to isolate the y and then see if the slope (number multiplied by x) is the same as the other problems . PS: focus on the slope, rest of equation doesn't matter in this question</span>

5 0

3 years ago

John has 12 pounds of dog food and is going to separate it into 3/4 pound portions. How many portions of dog food will he have?

NNADVOKAT [17]

16 portions

explanation:

12 / 3/4

= 12 * 4/3

= 4*4 = 16

7 0

3 years ago

PLEASE HELP ME 30POINTS +BRAINLY

Serga [27]

1) If a die is rolled once, determine the probability of rolling a 4: Rolling a 4 is an event with 1 favorable outcome (a roll of 4) and the total number of possible outcomes is 6 (a roll of 1, 2, 3, 4, 5, or 6). Thus, the probability of rolling a 4 is $\frac{1}{6}$ .

If a die is rolled once, determine the probability of rolling at least a 4: Rolling at least 4 is an event with 3 favorable outcomes (a roll of 4, 5, or 6) and the total number of possible outcomes is again 6. Thus, the probability of rolling at least a 4 is $\frac{3}{6}$ = $\frac{1}{2}$ .

<h3>Here are two more examples: </h3>

If a coin is flipped twice, determine the probability that it will land heads both times:

Favorable outcomes: 1 -- HH

Possible outcomes: 4 -- HH, HT, TH, TT

Thus, the probability that the coin will land heads both times is $\frac{1}{4}$ .

If Dan grabs one sock from a drawer containing 3 white socks, 4 blue socks, and 5 yellow socks, what is the probability that he will grab a white sock?

Favorable outcomes: 3 (3 white socks)

Possible outcomes: 12 (3 white socks + 4 blue socks + 5 yellow socks)

Thus, the probability that Dan will grab a white sock is $\frac{3}{12}$ = $\frac{1}{4}$ .

6 0

3 years ago

An article written for a magazine claims that 78% of the magazine's subscribers report eating healthily the previous day. Suppos

Schach [20]

Answer:

89.44% probability that less than 80% of the sample would report eating healthily the previous day

Step-by-step explanation:

We use the binomial approximation to the normal to solve this question.

Binomial probability distribution

Probability of exactly x sucesses on n repeated trials, with p probability.

Can be approximated to a normal distribution, using the expected value and the standard deviation.

The expected value of the binomial distribution is:

$E(X) = np$

The standard deviation of the binomial distribution is:

$\sqrt{V(X)} = \sqrt{np(1-p)}$

Normal probability distribution

Problems of normally distributed samples can be 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.

When we are approximating a binomial distribution to a normal one, we have that $\mu = E(X)$ , $\sigma = \sqrt{V(X)}$ .