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3 years ago

How do you find the area of parralelogram

Mathematics

1 answer:

Nezavi [6.7K]3 years ago

3 0

Answer:

See below.

Step-by-step explanation:

The area = length of the base * perpendicular height.

If you are given the lengths of the side and the measure of one of the angles you can use the trigonometry to find the perpendicular height.

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1. A certain ceiling is made up of tiles. Every square meter of ceiling requires 10.75 tiles. Fill in the table with the missing

dybincka [34]

Given:

Consider the below table attached with this question.

Every square meter of ceiling requires 10.75 tiles.

To find:

The missing values in the table.

Solution:

We have,

1 square meter of ceiling = 10.75 tiles. ...(i)

Therefore, the number of tiles corresponding to 1 square meter of ceiling is 10.75.

Multiply both sides by 10.

10×1 square meter of ceiling = 10×10.75 tiles.

10 square meter of ceiling = 107.5 tiles.

Therefore, the number of tiles corresponding to 10 square meter of ceiling is 107.5.

Divide both sides by 10.75 in (i).

$\dfrac{1}{10.75}$ square meter of ceiling = 1 tile.

Multiply both sides by 100.

$\dfrac{100}{10.75}$ square meter of ceiling = 100 tiles.

On approximating the value, we get

9.3 square meter of ceiling = 100 tiles.

Therefore, the square meters of ceiling corresponding to 100 tiles is about 9.3.

Multiply both sides by a in (i).

a×1 square meter of ceiling = a×10.75 tiles.

a square meter of ceiling = 10.75a tiles.

Therefore, the number of tiles corresponding to a square meter of ceiling is 10.75a.

8 0

2 years ago

Read 2 more answers

Suppose the weights of Farmer Carl's potatoes are normally distributed with a mean of 8.0 ounces and a standard deviation of 1.1

svet-max [94.6K]

Answer:

a) 0.9959 = 99.59% probability that the mean weight is less than 9.3 ounces

b) 0.0129 = 1.29% probability that the mean weight is more than 9.0 ounces

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