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2 years ago

Please help me I will give you brain list

Mathematics

1 answer:

xz_007 [3.2K]2 years ago

8 0

1 triangle=3 sticks.

2 triangle= 6 sticks.

3 triangle= 9 sticks.

4 triangles=12 sticks.

5triangles=15sticks.

10 triangles= 30 sticks.

30 triangles=90 sticks

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$4.30 makeup; 40% discount; 6% tax

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2 years ago

Quadrilateral ABCD is a square with diagonals that intersect at point E. Find angle ADB. A. 11.25º B. 22.5º C. 45º D. 90º

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IT WOULD BE "D" I BELIEVE

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3 years ago

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If you are driving on the freeway at 55 mph and you travel for 5 hours, how far will you have gone?

alina1380 [7]

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2 years ago

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The area of a square tile is 49 square inches. Write the length of one side of the square time as a square root and standard fro

aleksandr82 [10.1K]

A square area=legnth times width
but for a square, legnth=width so
area=legnth^2

are=49
49=legnth^2

square root of both sides
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2 years ago

Ricardo and Tammy practice putting golf balls. Ricardo makes 47% of his putts and Tammy makes 51% of her putts. Suppose that Ric

yaroslaw [1]

Answer:

0.3821 = 38.21% probability that Ricardo makes a higher proportion of putts than Tammy.

Step-by-step explanation:

To solve this question, we need to understand the normal distribution, the central limit theorem, and subtraction of normal variables.

Normal probability distribution

When the distribution is normal, we use 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 $s = \frac{\sigma}{\sqrt{n}}$ .

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

Subtraction of normal variables:

When we subtract normal variables, the mean of the subtraction will be the subtraction of the means, while the standard deviation will be the square root of the sum of the variances.

Ricardo makes 47% of his putts, and attempts 25 putts.

By the Central Limit Theorem, we have that:

$\mu_R = 0.47, s_R = \sqrt{\frac{0.47*0.53}{25}} = 0.0998$