Which shape could be thought of as being created by stacking congruent polygons?

Mathematics

2 answers:

Amiraneli [1.4K]4 years ago

7 0

You would form a prism.

yarga [219]4 years ago

3 0

A polygon is composed of three or more line segments ,and when you join these segments ,a closed geometrical shape should form.For example, Equilateral triangle, Square, Regular Pentagon, Regular Hexagon,....

There are many possibilities.

→If you join an Equilateral and three other equilateral triangles having same base length,you will obtain, Equilateral Pyramid.

→If you join Square and four equilateral triangles having same base length,you will obtain, Equilateral Square Pyramid.

→Similarly, if you join Regualr Pentagon and five equilateral triangles having same base length,you will obtain, Equilateral Regular pentagonal Pyramid

→Regular Hexagon+Six equilateral triangles=Equilateral Regular Hexagonal Pyramid

→If you join six congruent squares ,you will get the shape of Prism called Cube.

These are known that is defined shape in mathematics which i have written.You can create many more shapes by yourself.

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The body temperatures of adults are normally distributed with a mean of 98.6degrees° F and a standard deviation of 0.60degrees°

Schach [20]

Answer:

97.72% probability that their mean body temperature is greater than 98.4degrees° F.

Step-by-step explanation:

To solve this question, we have to understand the normal probability distribution and the central limit theorem.

Normal probability distribution:

Problems of normally distributed samples 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 random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sample means with size n of at least 30 can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$