(x + 3)^2 + (x + 4)^2
= x^2 + 6x + 9 + x^2 + 8x + 16
= 2x^2 + 14x + 25
= 2(x^2 + 7x) + 25
= 2[(x + 7/2)^2 - 49/4] + 25
= 2(x + 7/2)^2 - 98/4 + 25
= 2(x + 7/2)^2 + 1/2
Its B
Answer:
Step-by-step explanation:
Note that there are two scale models with each of ratio of 1/2 and 1/16 respectively.
For the first model, the dimension will be as follows:
Length/2 by width/2
94/2 by 50/2 = 47 feet by 25 feet.
For the second model, the dimension will be as follows:
Length/16 by width/16
The dimensions of the second model is 94/16 by 50/16 = 5.875 feet by 3.125 feet.
Since we are to solve for the area of the smallest scale model which is
5.875 feet by 3.125 feet.
Hence, area (A) = L× W
=5.875 × 3.125 feet.
= 18.359ft^2
Answer:
a) 90 stamps
b) 108 stamps
c) 333 stamps
Step-by-step explanation:
Whenever you have ratios, just treat them like you would a fraction! For example, a ratio of 1:2 can also look like 1/2!
In this context, you have a ratio of 1:1.5 that represents the ratio of Canadian stamps to stamps from the rest of the world. You can set up two fractions and set them equal to each other in order to solve for the unknown number of Canadian stamps. 1/1.5 is representative of Canada/rest of world. So is x/135, because you are solving for the actual number of Canadian stamps and you already know how many stamps you have from the rest of the world. Set 1/1.5 equal to x/135, and solve for x by cross multiplying. You'll end up with 90.
Solve using the same method for the US! This will look like 1.2/1.5 = x/135. Solve for x, and get 108!
Now, simply add all your stamps together: 90 + 108 + 135. This gets you a total of 333 stamps!
The most likely answer is B.
Highlighting the corresponding parts from the original in the copy is just coloring in the parts of the copy that have the same scale, for example, if the center of the scaled copy is one unit, the original could be 3 units.
Answer:
The Null hypothesis is a claim the researcher is trying to disprove. (I think)
Step-by-step explanation:
The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). It states results are due to chance and are not significant in terms of supporting the idea being investigated.
