Answer:
The distance from point A to the top of the building is 5
feet
The Height of the Skyscraper is 5 feet
Step-by-step explanation:
Given
Let Top point of building be point C
Also Let Base of the building be point B
Distance from point A to base B of the building AB= 5 feet
∴ It makes a Right angle triangle
Also ∠ACB = 45°
Also tan 45° = 1
Now tan ∠ACB =
∴ AB= BC =5 feet
The Height of the Skyscraper is 5 feet
Now Triangle ABC is right angle triangle with right angled at B
So by Pythagoras theorem
AC= 
The distance from point A to the top of the building is 5
feet
Answer:
arc AB = 70°
arc BC = 110°
arc ABC = 180°
arc CDB = 250°
Step-by-step explanation:
Since these angles are not inscribed, but are at the center point of the circle, the angles and arcs will be the same measure.
Solve for arc AB:
Angle AFB = 70°, so arc AB = 70°
Solve for arc BC:
Angle AFC is a straight angle so it is 180°. To find angle BFC, subtract angle AFB from 180°.
180 - 70 = 110°
Since angle BFC = 110°, arc BC = 110°
Solve for arc ABC:
Add arc AB and arc BC together.
70° + 110° = 180°
arc ABC = 180°
Solve for arc CDB:
There are 360° in a cirlce. To find arc CDB, subtract arc BC from 360°.
360° - 110° = 250°
arc CDB = 250°
The <em>directional</em> derivative of
at the given point in the direction indicated is
.
<h3>How to calculate the directional derivative of a multivariate function</h3>
The <em>directional</em> derivative is represented by the following formula:
(1)
Where:
- Gradient evaluated at the point
.
- Directional vector.
The gradient of
is calculated below:
(2)
Where
and
are the <em>partial</em> derivatives with respect to
and
, respectively.
If we know that
, then the gradient is:
![\nabla f(r_{o}, s_{o}) = \left[\begin{array}{cc}\frac{s}{1+r^{2}\cdot s^{2}} \\\frac{r}{1+r^{2}\cdot s^{2}}\end{array}\right]](https://tex.z-dn.net/?f=%5Cnabla%20f%28r_%7Bo%7D%2C%20s_%7Bo%7D%29%20%3D%20%5Cleft%5B%5Cbegin%7Barray%7D%7Bcc%7D%5Cfrac%7Bs%7D%7B1%2Br%5E%7B2%7D%5Ccdot%20s%5E%7B2%7D%7D%20%5C%5C%5Cfrac%7Br%7D%7B1%2Br%5E%7B2%7D%5Ccdot%20s%5E%7B2%7D%7D%5Cend%7Barray%7D%5Cright%5D)
![\nabla f (r_{o}, s_{o}) = \left[\begin{array}{cc}\frac{3}{1+1^{2}\cdot 3^{2}} \\\frac{1}{1+1^{2}\cdot 3^{2}} \end{array}\right]](https://tex.z-dn.net/?f=%5Cnabla%20f%20%28r_%7Bo%7D%2C%20s_%7Bo%7D%29%20%3D%20%5Cleft%5B%5Cbegin%7Barray%7D%7Bcc%7D%5Cfrac%7B3%7D%7B1%2B1%5E%7B2%7D%5Ccdot%203%5E%7B2%7D%7D%20%5C%5C%5Cfrac%7B1%7D%7B1%2B1%5E%7B2%7D%5Ccdot%203%5E%7B2%7D%7D%20%5Cend%7Barray%7D%5Cright%5D)
![\nabla f (r_{o}, s_{o}) = \left[\begin{array}{cc}\frac{3}{10} \\\frac{1}{10} \end{array}\right]](https://tex.z-dn.net/?f=%5Cnabla%20f%20%28r_%7Bo%7D%2C%20s_%7Bo%7D%29%20%3D%20%5Cleft%5B%5Cbegin%7Barray%7D%7Bcc%7D%5Cfrac%7B3%7D%7B10%7D%20%5C%5C%5Cfrac%7B1%7D%7B10%7D%20%5Cend%7Barray%7D%5Cright%5D)
If we know that
, then the directional derivative is:
![\nabla_{\vec v} f = \left[\begin{array}{cc}\frac{3}{10} \\\frac{1}{10} \end{array}\right] \cdot \left[\begin{array}{cc}5\\10\end{array}\right]](https://tex.z-dn.net/?f=%5Cnabla_%7B%5Cvec%20v%7D%20f%20%3D%20%5Cleft%5B%5Cbegin%7Barray%7D%7Bcc%7D%5Cfrac%7B3%7D%7B10%7D%20%5C%5C%5Cfrac%7B1%7D%7B10%7D%20%5Cend%7Barray%7D%5Cright%5D%20%5Ccdot%20%5Cleft%5B%5Cbegin%7Barray%7D%7Bcc%7D5%5C%5C10%5Cend%7Barray%7D%5Cright%5D)

The <em>directional</em> derivative of
at the given point in the direction indicated is
. 
To learn more on directional derivative, we kindly invite to check this verified question: brainly.com/question/9964491
Answer: Ok so go back to the lesson and read the directions for this page then look at the signs and fill in the page easy
Step-by-step explanation:
Answer:
You forgot to put the data
Step-by-step explanation:
Because we don't have the data that represent the responses to the survery I can show you how to can get the mean and the standard deviation.
The mean is the average of the numbers. We use it to get a representative value of the data we are manipulating. We calculate it by adding all the numbers and then divide the sum by how many numbers we have.
The formule we use is:
∑
The standard deviation is a measure of the variation or dispersion of the data we have. A low deviation indicates that our data is few disperced, on the other hand a big deviation indicates a high dispersion of the data.
The formule we use is:
