Answer:
1863 ![meters^{2}](https://tex.z-dn.net/?f=meters%5E%7B2%7D)
Step-by-step explanation:
( 23 x 12 ) x 2 = 552
69 x 19 = 1311
1311
<u>+552</u>
= 1863 ![meters^{2}](https://tex.z-dn.net/?f=meters%5E%7B2%7D)
Answer:
![\sin Y= \frac{XZ}{XY}](https://tex.z-dn.net/?f=%5Csin%20Y%3D%20%5Cfrac%7BXZ%7D%7BXY%7D)
![\cos Y= \frac{YZ}{XY}](https://tex.z-dn.net/?f=%5Ccos%20Y%3D%20%5Cfrac%7BYZ%7D%7BXY%7D)
![\tan Y= \frac{XZ}{YZ}](https://tex.z-dn.net/?f=%5Ctan%20Y%3D%20%5Cfrac%7BXZ%7D%7BYZ%7D)
Step-by-step explanation:
Given
See attachment for triangle
Required
Find
and
of angle Y
For angle Y:
![Opposite = XZ](https://tex.z-dn.net/?f=Opposite%20%3D%20XZ)
![Adjacent = YZ](https://tex.z-dn.net/?f=Adjacent%20%3D%20YZ)
The
of an angle is calculated as:
![\sin\theta = \frac{Opposite}{Hypotenuse}](https://tex.z-dn.net/?f=%5Csin%5Ctheta%20%3D%20%5Cfrac%7BOpposite%7D%7BHypotenuse%7D)
So:
![\sin Y= \frac{XZ}{XY}](https://tex.z-dn.net/?f=%5Csin%20Y%3D%20%5Cfrac%7BXZ%7D%7BXY%7D)
The
of an angle is calculated as:
![\cos\theta = \frac{Adjacent}{Hypotenuse}](https://tex.z-dn.net/?f=%5Ccos%5Ctheta%20%3D%20%5Cfrac%7BAdjacent%7D%7BHypotenuse%7D)
So:
![\cos Y= \frac{YZ}{XY}](https://tex.z-dn.net/?f=%5Ccos%20Y%3D%20%5Cfrac%7BYZ%7D%7BXY%7D)
The
of an angle is calculated as:
![\tan\theta = \frac{Opposite}{Adjacent}](https://tex.z-dn.net/?f=%5Ctan%5Ctheta%20%3D%20%5Cfrac%7BOpposite%7D%7BAdjacent%7D)
So:
![\tan Y= \frac{XZ}{YZ}](https://tex.z-dn.net/?f=%5Ctan%20Y%3D%20%5Cfrac%7BXZ%7D%7BYZ%7D)
The keyword is Rise over run
So the 2nd y value minus the first y value and divide it by the 2nd x value minus
(y2-y1)/(x2-x1)
(10-7)/5-3)
the first x value and you should get 3/2
*dont worry the if the number is under and not above it’s not a saying to do to the power of but it’s just saying the order in which to put the x and y values.
Answer:
C. 30
Step-by-step explanation:
-It is a statistical rule of thumb that the size of a sample must be
.
-This size is deemed adequate for the Central Limit Theorem to hold.
-At this size or greater, the shape of the resultant distribution is normal.
#It should however be noted, that for a normal distribution the CLT holds even for smaller sample sizes.