Answer: 1300 cm^3, 850 cm^2
Step-by-step explanation:
With a triangular prism, you need to calculate the surface area of the triangle first and then the length. Here's how to solve for the volume (A):
Triangles is b*h/2, plug in the equation
10*13/2
130/2
65
Now use the length of the rest of the shape, which is 20 cm.
65*20=1300
the volume is 1300 cm^3.
for B, finding the surface area requires you to analyze each individual part of the shape.
Each part colored in represents a different shape:
Red:
10*20=200
Blue:
13*10/2=65
65*2=130 (Two triangles)
Green:
13*20=260
260*2=520 (Two rectangles)
Add them all up together.
200+130+520= 850 cm^2
Let the width be W, then the length is 4W (since the width is 1/4 the length)
The area of the original deck is
The dimensions of the new deck are :
length = 4W+6
width=W+2
so the area of the new deck is :
"<span>the area of the new rectangular deck is 68 ft2 larger than the area of the original deck</span>" means that we write the equation:
the length is
ft
Answer: width: 4, length: 16
Answer:
I am sorry. To answer a test is against the Honor Code, I cannot help you.
Step-by-step explanation:
Remember PEMDAS :)
Answer:
B) The sum of the squared residuals
Step-by-step explanation:
Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).
Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.
Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.
Therefore, option B gives the correct answer.
Answer:
1/5 divided by 4 equals to 0.05