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3 years ago

Which hexagons only have pairs of parallel sides?

Mathematics

1 answer:

nalin [4]3 years ago

3 0

regular hexagon

which means a hexagon with equal sides and equal interior angle which have three parallel sides

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Your mother gave you $25 dollars with which to buy a present. This covered 5/6 of the cost. How much did the present cost???????

miv72 [106K]

I don't know, but I think we/you have to divide 5 into 6. If it is wrong, then i'm so sorry.

7 0

3 years ago

Read 2 more answers

Veda solves the following system of linear equations by eliminations. What is the vale of x in the solution of the system of equ

kupik [55]

Answer:

x = -1, y = 1

Step-by-step explanation:

6 + 4x - 2y = 0 (1)

-3 - 7y = 10x (2)

From (1)

6 + 4x - 2y = 0 (1)

4x - 2y = -6 (3)

From (2)

-3 - 7y = 10x (2)

10x + 7y = -3 (4)

4x - 2y = -6 (3)

10x + 7y = -3 (4)

Using elimination method

Multiply (3) by 10 and (4) by 4 to eliminate x

40x - 20y = -60

40x + 28y = -12

28y - (-20y) = -12 - (-60)

28y + 20y = -12 + 60

48y = 48

y = 48/48

y = 1

Substitute y = 1 into (3)

4x - 2y = -6 (3)

4x - 2(1) = -6

4x - 2 = -6

4x = -6 + 2

4x = -4

x = -4/4

x = -1

x = -1, y = 1

The value of x in the equation is -1

4 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