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3 years ago

PLEASE HELP QUICKLY!!

Mathematics

2 answers:

morpeh [17]3 years ago

8 0

On the graph you just go down by two every time once you find one point like (1,-2). Basically (x,-2(x)) is what it would look like on an order pair.

dimulka [17.4K]3 years ago

4 0

Y=-2x so the -2x turns to -2/1 so you go down 2 and right 1. That's your answer

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PLEASE SOMEONE HELP ASAP AND SHOW THE WORK

julsineya [31]

The sum of two sides of a triangle must be greater than the third side
1) 5+9>14
14>14 It is incorrect so you can’t have these lengths

2)7+7>15
14>15 X

3)1+2>4
3>4 X

4)3+6>8
9>8

6+8>3
14>3

8+3>6
11>6

So the fourth choice is correct

8 0

3 years ago

Triangle MNP is dilated according to the rule DO,1.5 (x,y)Right arrow(1.5x, 1.5y) to create the image triangle M'N'P, which is n

Vikki [24]

Given:

The vertex of a triangle MNP are M(-4, 6), N(2, 6), and P(-1, 1).

The rule of dilation is:

$(x,y)\to (1.5x,1.5y)$

The image of triangle MNP after dilation is M'N'P'.

To find:

The coordinates of the endpoints of segment M'N'.

Solution:

The end points of MN are M(-4, 6) and N(2, 6).

The rule of dilation is:

$(x,y)\to (1.5x,1.5y)$

Using this rule, we get

$M(-4,6)\to M'(1.5(-4),1.5(6))$

$M(-4,6)\to M'(-6,9)$

And,

$N(2,6)\to N'(1.5(2),1.5(6))$

$N(2,6)\to N'(3,9)$

The endpoints of M'N' are M'(-6, 9) and N'(3, 9).

Therefore, the correct option is B.

8 0

3 years ago

HELP ASAP Calculate the residuals for your scatterplot in step 2d.

postnew [5]

The residuals of the linear regression equation are -1.36, 0.32, 0.01, 0.62, 0.17, -0.24, 0.89, 0.09, 0.18 and -0.7

<h3>How to determine the residuals?</h3>

The regression equation is given as:

y = 0.141x + 0.842

Next, we calculate the predicted values (y) at the corresponding x values.

So, we have:

y = 0.141 * 13.8 + 0.842 = 2.78

y = 0.141 * 18 + 0.842 = 3.38

y = 0.141 * 16.7 + 0.842 = 3.20

y = 0.141 * 18 + 0.842 = 3.38

y = 0.141 * 0.7 + 0.842 = 0.94

y = 0.141 * 21.9 + 0.842 = 3.93

y = 0.141 * 15.5 + 0.842 = 3.03

y = 0.141 * 9.2 + 0.842 = 2.14

y = 0.141 * 19.5 + 0.842 = 3.59

y = 0.141 * 16.7 + 0.842 = 3.20

The residuals are then calculated using:

Residual = Actual value - Predicted value

So, we have:

Residual = 1.42 - 2.78 = -1.36

Residual = 3.7 - 3.38 = 0.32

Residual = 3.21 - 3.20 = 0.01

Residual = 4 - 3.38 = 0.62

Residual = 1.11 - 0.94 = 0.17

Residual = 3.69 - 3.93 = -0.24

Residual = 3.92 - 3.03 = 0.89

Residual = 2.23 - 2.14 = 0.09

Residual = 3.77 - 3.59 = 0.18

Residual = 2.5 - 3.20 = -0.7

Hence, the residuals of the linear regression equation are -1.36, 0.32, 0.01, 0.62, 0.17, -0.24, 0.89, 0.09, 0.18 and -0.7