Answer:
Step-by-step explanation:
Step 2, she factored the trinomial incorrectly. She did, "what two numbers multiply to equal 6 but add to equal 5?" She should have asked multiply to equal -6 and add to equal 5. The correct factorization would be:
(x+6)(x-1) = 0
x = -6 and 1
Answer:

Step-by-step explanation:
Given
Function; 
Required
Find an equation perpendicular to the given function if it passes through (-3,9)
First, we need to determine the slope of: 
The slope intercept of an equation is in form;

<em>Where m represent the slope</em>
Comparing
to
;
We'll have that

Going from there; we need to calculate the slope of the parallel line
The condition for parallel line is;

Substitute 

Divide both sides by -2


The point slope form of a line is;

Where
and 
becomes

Open the inner bracket

<em>Hence, the point slope form of the perpendicular line is: </em>
<em />
<em />
Answer:
Okay so, we know that Theta =10°
Opposite=7ft
Adjacent= x ft
And then
Tan 10=7/x
Hence
X=7/tan10
X=39.69897 39. 7 ft
I Hope that i explained well
Answer:
Figure obtained - Cylinder
Surface area of the cylinder is 1224π square units.
Step-by-step explanation:
When a rectangle is rotated about an axis along its largest side, a cylinder having radius 17 and height 19 units is formed.
Surface area of the cylinder = 2πr(h + r)
By substituting r = 17 units and h = 19 units in the given formula,
Surface area = 2π(17)(17 + 19)
= (34)(36)π
= 1224π
Therefore, surface area of the cylinder is 1224π square units.
Minimizing the sum of the squared deviations around the line is called Least square estimation.
It is given that the sum of squares is around the line.
Least squares estimations minimize the sum of squared deviations around the estimated regression function. It is between observed data, on the one hand, and their expected values on the other. This is called least squares estimation because it gives the least value for the sum of squared errors. Finding the best estimates of the coefficients is often called “fitting” the model to the data, or sometimes “learning” or “training” the model.
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