66% of what number is 33

Nataliya [291]

<h3>Answer: C) 4/8 = 0.5</h3>

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Explanation:

The sine ratio involves the opposite over hypotenuse.

With respect to reference angle J, the opposite leg is the one furthest from the angle. So the opposite side is KL = 4. The hypotenuse is always the longest side, always opposite the 90 degree angle, so LJ = 8 is the hypotenuse.

sin(angle) = opposite/hypotenuse

sin(J) = KL/LJ

sin(J) = 4/8

sin(J) = 0.5

3 0

3 years ago

1.) The deer population increases at a rate of 3% per year. There are 1253 deer this year. Write a function that models the deer

BabaBlast [244]

Exponential growth/decay rule with general function is:<span>y=y(0)<span>e<span>kt</span></span></span> with k is growth/decay constant rate.
(1) Deer population function y(0) = 1253 k
= 3% t
= 6 (years)
y(6) = 1253 . e^(0.03*6) ~ 1500 (deers)
: 1496 deers
(2) Growth factor b is rate 9.5% = 0.095

4 0

3 years ago

Read 2 more answers

Find the solution of this system of equations 2x-4y=32 <br> 2x-8y=48

EleoNora [17]

2x - 4y = 32
2x - 8y = 48

2x - 2x - 4x - (-8y) = 32 - 48

4x = -16
x = -16 ÷ 4
x = -4

substitute your value of x into any of the original equations

2 (-4) - 4y = 32

-8 - 4y = 32

-4y = 32 + 8

-4y = 40
y = 40 ÷ -4
y = -10

6 0

3 years ago

Standard form: 10X - 7y = -8<br> slope-intercept form: ___________________

Eva8 [605]

Hope this help’s you out.

4 0

2 years ago

Read 2 more answers

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