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2 years ago

Which shapes make up this composite figure?

Mathematics

2 answers:

lisov135 [29]2 years ago

7 0

Answer:

A cylinder and 2 half spheres

Step-by-step explanation:

Using my eyes, I see 1 cylinder and 2 half spheres at the ends of the cylinder.

tamaranim1 [39]2 years ago

5 0

Answer:

A cylinder and 2 half spheres

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Rewrite ^3 sqrt 27x-81-5 to make it easy to graph a translation. Describe the graph.

iragen [17]

First we have to rewrite this function.Instead of :^3 sqrt (...) we can write : (...)^(1/3).
f ( x ) = ( 27 x - 81 ) ^(1/3) - 5 = ( 27 * ( x - 3 ) ) ^(1/3) - 5 = = 3 * ( x - 3 )^(1/3) - 5 And this makes it easy to graph a translation.The parent function is y = x^(1/3). The zero of this function is ( 0, 0 ).
Then it will be stretched vertically by a factor of 2 and translated: 3 units right and 5 units down.

8 0

3 years ago

Enter the sum of numbers as a product of their GCF. 75 + 30 The sum of the numbers as a product of their GCF is

Oksanka [162]

Answer: 75+30 = 15 x 7

Step-by-step explanation:

The given expression is 75+30 (=105) which defines the sum of 75 and 30.

Prime factorization of 75 and 30 are as below:

75 = 5 x 5 x 3

30 = 5 x 3 x 2

GCD (75,30) = 5x 3 = 15 [Note: GCD = Greatest common divisor]

Consider 75+30 = (15 x 5) + (15 x 2) [75 = 15 x 5 and 30= 15 x 2]

= 15 (5+2) [taking 15 as common ]

= 15 x (7)

(=105)

So, 75+30 which is sum of the numbers and it is expressed as 15 x 7 which a product of their GCF.

7 0

3 years ago

The variance can never bea.zerob.larger than the standard deviationc.negatived.smaller than the standard deviation

MrRa [10]

Answer:

c.negative

True, by definition since the variance take in count the differences around the mean squared, then we can't have a negative value for a sum of square values.

Step-by-step explanation:

We need to remember that the variance is a measure of dispersion for a dataset respect to a measure of central tendency called the mean. The mean is defined as:

$\bar x = \frac{\sum_{i=1}^n X_i}{n}$

And the population mean is given by:

$\mu= \frac{\sum_{i=1}^n X_i}{N}$

The population variance is defined by:

$\sigma^2 = \frac{\sum_{i=1}^n (X_i -\mu)^2}{N}$

And the sample variance is defined as:

$s^2= \frac{\sum_{i=1}^n (X_i -\bar x)}{n-1}$

Now if we analyze the options we have this:

a.zero

False, if all the values are the same then the mean would be the same to the values and the difference between each value and the mean would 0 and indeed the variance would be 0.

b.larger than the standard deviation

False, if the population standard deviation is $\sigma=2$ then the variance would be $\sigma^2 = 4$ and as we can see is higher than the standard deviation

c.negative

True, by definition since the variance take in count the differences around the mean squared, then we can't have a negative value for a sum of square values.

d.smaller than the standard deviation

False, if the population standard deviation is $\sigma=0.1$ then the variance would be $\sigma^2 = 0.01$ and as we can see is lower than the standard deviation