Check the picture below.
so, to get the area of the triangles, we can simply run a perpendicular line from the top to the base, and end up with a right-triangle with a base of 22 and a hypotenuse of 34, let's find the altitude.
so then the surface area of the triangular prism is,
![\bf \stackrel{\textit{left and right}}{2(34\cdot 76)}~~+~~\stackrel{\textit{bottom}}{(44\cdot 76)}~~+~~\stackrel{\textit{front and back}}{2\left[\cfrac{1}{2}(44)(\sqrt{672}) \right]} \\\\\\ 8512~~+~~(44)(\sqrt{672})\qquad \approx\qquad 9652.61036291978](https://tex.z-dn.net/?f=%5Cbf%20%5Cstackrel%7B%5Ctextit%7Bleft%20and%20right%7D%7D%7B2%2834%5Ccdot%2076%29%7D~~%2B~~%5Cstackrel%7B%5Ctextit%7Bbottom%7D%7D%7B%2844%5Ccdot%2076%29%7D~~%2B~~%5Cstackrel%7B%5Ctextit%7Bfront%20and%20back%7D%7D%7B2%5Cleft%5B%5Ccfrac%7B1%7D%7B2%7D%2844%29%28%5Csqrt%7B672%7D%29%20%20%5Cright%5D%7D%0A%5C%5C%5C%5C%5C%5C%0A8512~~%2B~~%2844%29%28%5Csqrt%7B672%7D%29%5Cqquad%20%5Capprox%5Cqquad%209652.61036291978)
Answer:
(16/3,-4)
Step-by-step explanation:
- y=-3/4x & y=3/2x -12
- then solve for x which means -3/4x=3/2x-12
- then the value of x=16/3
- put the x value in one equation
- y=3/4(16/3)
- then the value of y=-4
Answer:
maybe do the math
Step-by-step explanation:
Look up this site called calculator soup it gives great answers
Answer:
d) Squared differences between actual and predicted Y values.
Step-by-step explanation:
Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable.Such formula could be used to generate values of given value X.
For example,
suppose a = 10 and b = 7. If X is 10, then predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.
It minimize the squared difference between actual and predicted value.
