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3 years ago

Which of the following describes the image of a figure after a dilation that has a scale factor between zero and one?

Mathematics

1 answer:

g100num [7]3 years ago

7 0

Answer:

When a figure or shape is dilated , the size of the figure either enlarges or shrank without changing its actual shape.

Shape or figure after dilation is similar to original figure.

If the scale or factor of dilation is greater than 1 the shape enlarges.

If the scale factor lies between 0<scale factor<1, then the figure shrinks i.e size reduces.

So out of all the options given →It has the same shape as the original and is smaller than the original figure.

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shutvik [7]

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3 years ago

If this cube of edge 20 cm long is cut into smaller cubes of edge 10 cm, how many of these smaller cubes do I get?

BartSMP [9]

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3 years ago

A flagpole broke in a storm. It was originally 81 8181 feet tall. 28 2828 feet are still sticking straight out of the ground, wh

solong [7]

Answer:

The end of the flagpole is 50.79 ft away from the base of the pole.

Step-by-step explanation:

The problem is represented by the diagram below.

The broken flagpole forms the shape of a right angled triangle. We need to find one of the sides of the triangle, the adjacent (x).

The hypotenuse is the broken part of the flagpole (53 ft), while the opposite is the part of the flagpole that is still stuck to the ground (28 ft).

Using Pythagoras theorem, we have that:

$hyp^2 = adj^2 + opp^2$

=> $53^2 = x^2 + 28^2$

$3364 = x^2 + 784\\\\=> x^2 = 3364 - 784\\\\x^2 = 2580\\\\x = \sqrt{2580}\\ \\x = 50.79 ft$

The end of the flagpole is 50.79 ft away from the base of the pole.

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3 years ago

The amounts of nicotine in a certain brand of cigarette are normally distributed with a mean of 0.895 g and a standard deviation

kvasek [131]

Answer:

3.67% probability of randomly seleting 37 cigarettes with a mean of 0.809 g or less.

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 normally distributed samples are 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 $s = \frac{\sigma}{\sqrt{n}}$ .