Please help!!will get brainliest!! Use a graphing calculator to draw a graph of f for 0

What fraction is equivalent to 15/24?

Novay_Z [31]

ANSWER:30/48
multiply both numerator and denominator by 2

5 0

4 years ago

Read 2 more answers

I'm looking for perimeter

amid [387]

Answer:

what?

Step-by-step explanation:

7 0

3 years ago

Read 2 more answers

Solve for a.

tamaranim1 [39]

The correct answer is: [C]: " a = ± $\sqrt{25 - b^2}$ " .
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Explanation:
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We are given:
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 → " 25 = a² + b² " ; Solve for "a" ;
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→ To solve for "a" ; we want to isolate "a" on one side of the equation.
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We can rewrite: " 25 = a² + b² " ;

as: " a² + b² = 25 " .

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Then, we can subtract " b² " from each side of the equation; as follows:
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→ " a² + b² − b² = 25 − b² " ;

to get:
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→ " a² = 25 − b² " ;
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→ Now, take the square root of EACH SIDE of the equation ;
to isolate "a" on one side of the equation; & to solve for the value(s) of "a" ;
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→ √(a²) = $\sqrt{25 - b^2}$ ;

to get:
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 → a = | $\sqrt{25 - b^2}$ | ;

→ a = ± $\sqrt{25 - b^2}$ ;

 → which is: Answer choice: [C]: " a = ± $\sqrt{25 - b^2}$ " .
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8 0

3 years ago

Suppose 46% of politicians are lawyers. If a random sample of size 662 is selected, what is the probability that the proportion

Svet_ta [14]

Answer:

0.9606 = 96.06% probability that the proportion of politicians who are lawyers will differ from the total politicians proportion by less than 4%

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 normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes 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 $s = \sqrt{\frac{p(1-p)}{n}}$