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3 years ago

Find the perimeter. Use 3.14 for pi.

Mathematics

1 answer:

balu736 [363]3 years ago

3 0

Step-by-step explanation:

perimeter=4×6-1/4 ×2π×3=24-1/2×3.14×3=24-4.71=19.29cm

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Solve for g.<br> 2g - 3 <1

Olegator [25]

Answer:

g < 2

Step-by-step explanation:

2g - 3 < 1 , add 3 to both sides

2g -3 + 3 < 1+3 , combine like terms

2g < 4, divide both sides by 2

g < 2

6 0

3 years ago

Help me plz.................

Brrunno [24]

Answer:

3/5

Step-by-step explanation:

hope i helped :)

6 0

3 years ago

Read 2 more answers

In a fruit punch drink, the 3 ingredients are apple juice, orange juice and cranberry juice. If 3/4 of the drink is apple juice

SVETLANKA909090 [29]

Step-by-step explanation:

Let's assume x = apple juice, y = orange juice, and z = cranberry juice

$1 = x + y + z \\ 1 = \frac{3}{4} + \frac{1}{10} + z \\ z = 1 - \frac{3}{4} - \frac{1}{10} \\ z = \frac{3}{20}$

<u>x : y : z = 3/4 : 1/10 : 3/20</u>

If we were to make them all in the same denominator, x : y : z = 15/20 : 2/20 : 3/20.

$\frac{3}{4} \times \frac{5}{5} = \frac{15}{20} \\ \\ \frac{1}{10} \times \frac{2}{2} = \frac{2}{20}$

5 0

3 years ago

A researcher reports survey results by stating that the standard error of the mean is 25 the population standard deviation is 40

bezimeni [28]

Answer:

a) A sample of 256 was used in this survey.

b) 45.14% probability that the point estimate was within ±15 of the population mean

Step-by-step explanation:

This question is solved using the normal probability distribution and the central limit theorem.

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