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Temka [501]
2 years ago
10

What is the slope of a line that passes through points (-1, 4) and (4, 9)?

Mathematics
1 answer:
Elena L [17]2 years ago
3 0
1 the slope is 5/5 which is equal to 1
You might be interested in
Round 86,124 to the nearest ten thousand
kirill [66]
When rounding any number anything greater than 5 you go up and anything less than 5 you round down.
nearest ones 86120 ( 4 is lower than 5 you go down)
nearest tens 86100 ( 2 islower than 5 you go down)
nearest hundreds 86000( 1 is lower than 5 you go down)
nearest thousands 87000 ( 6 higher than 5 you go up)
nearest ten thousands 90000 ( 8 is higher than 5 you go up)
7 0
3 years ago
The perimeter of a semicircle is 46.26 meters. What is the semicircle's diameter?
barxatty [35]

Answer:

diameter = 18 m

Step-by-step explanation:

46.26 = 1/2π(diameter) + diameter

46.26 = 1.57d + d = 2.57d

d = 46.26/2.57 = 18 m

8 0
2 years ago
Can you make 24 from the numbers 3, 6, 8 and 2, using all the numbers? You can only use the operations: + - x / ( )
Andre45 [30]

Answer:

you can do 3 x 6 + 8 - 2

Step-by-step explanation:

3 × 6= 18

+8= 26

-2= 24

hope this Helps :)

4 0
3 years ago
Evaluate the expression 50g^2 if g = 1/5.
andrezito [222]

Answer:

2

Step-by-step explanation:

50*1/5 squared

1/5 = 0.2.

0.2 squared = 0.04

0.04 * 50 = 2

3 0
3 years ago
The difference between the observed value of the dependent variable and the value predicted using the estimated regression equat
Elenna [48]

Answer:

For this case we define the dependent variable as Y and the independent variable X. We assume that we have n observations and that means the following pairs:

(x_1, y_1) ,....,(x_n,y_n)

For this case we assume that we want to find a linear regression model given by:

\hat y = \hat m x +\hat b

Where:

\hat m represent the estimated slope for the model

\hat b represent the estimated intercept for the model

And for any estimation of the dependent variable \hat y_i , i=1,...,n is given by this model.

The difference between the observed value of the dependnet variable and the value predicted using the estimated regression equation is known as residual, and the residual is given by this formula:

e_i = y_i -\hat y_i , i=1,...,n

So the best option for this case is:

d. residual

Step-by-step explanation:

For this case we define the dependent variable as Y and the independent variable X. We assume that we have n observations and that means the following pairs:

(x_1, y_1) ,....,(x_n,y_n)

For this case we assume that we want to find a linear regression model given by:

\hat y = \hat m x +\hat b

Where:

\hat m represent the estimated slope for the model

\hat b represent the estimated intercept for the model

And for any estimation of the dependent variable \hat y_i , i=1,...,n is given by this model.

The difference between the observed value of the dependnet variable and the value predicted using the estimated regression equation is known as residual, and the residual is given by this formula:

e_i = y_i -\hat y_i , i=1,...,n

So the best option for this case is:

d. residual

7 0
3 years ago
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