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3 years ago

MATCH THE CONSTANT WITH ITS EXPRESSION! HELP PLEASE I SUCK AT MATH❤️❤️❤️❤️

Mathematics

1 answer:

malfutka [58]3 years ago

4 0

Answer:

Step-by-step explanation:

The constant of an equation is simply the number at the end without a variable. It is called a constant because it is the only term that doesn't change if you plug in a random x value. So:

The constant of 5a^2 + a - 1 is -1

The constant of 3x^2-x +1 is +1

The constant of y^2-2y+2 is +2

and the constant of m^2 +2m -2 is -2.

Hope this helped!

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In a big garden 60% of the trees are coconut trees,25%of the number of coconut trees are mango trees and 20% of the number ofof

AVprozaik [17]

the number of trees in the garden = x
coconut trees = 0.6x
mango trees = 0.6x * 0.25 = 0.15x
apple trees = 0.15x * 0.2 = 0.03x

0.03x = 1500
x = 1500/0.03
x = 50 000 ← the number of trees in the garden.

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3 years ago

I NEED DESPERATE HELP BADDIES VAWYCVYAGVFYGQEVEF

gtnhenbr [62]

Answer:

BCD is the outer angle of triangle ABC

=> BCD = ABC + BAC

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3 years ago

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A side of the triangle below has been extended to form an exterior angle of 128° . Find the value of x.

AfilCa [17]

Answer:

Step-by-step explanation:

The sum of adjacent angles traversed by a line must equal 180 degrees.

128+x=180

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3 years ago

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Please help!!! I need it fast! PLEASE!!!

Dovator [93]

Answer:

The points you need to graph are (-4,0) (-1,9) (2,0)

Step-by-step explanation:

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3 years ago

A researcher collected data on the hours of TV watched per day from a sample of five people of different ages. Here are the resu

frozen [14]

Answer:

1. The least squares regression is y = -0.1015·x + 6.51

2. The independent variable is b) age

Please see attached table

Step-by-step explanation:

The least squares regression formula is given as follows;

$\dfrac{\sum_{i = 1}^{n}(x_{i} - \bar{x})\times \left (y_{i} - \bar{y} \right ) }{\sum_{i = 1}^{n}(x_{i} - \bar{x})^{2}}$

We have;

$\bar x$ = 24

$\bar y$ = 4

$\Sigma (x_i - \bar x) (y_i - \bar y)$ = -79

$\Sigma (x_i - \bar x)^2$ = 778

$\therefore \hat \beta =\dfrac{\sum_{i = 1}^{n}(x_{i} - \bar{x})\times \left (y_{i} - \bar{y} \right ) }{\sum_{i = 1}^{n}(x_{i} - \bar{x})^{2}} = \frac{-79}{778} = -0.1015$