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bogdanovich [222]
3 years ago
10

What is the area of this figure?​

Mathematics
1 answer:
iVinArrow [24]3 years ago
6 0

Answer: To find the area of a rectangle, multiply its height by its width. For a square you only need to find the length of one of the sides (as each side is the same length) and then multiply this by itself to find the area.

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Evaluate this function h(x) = 7x + 1 find h(3)
Lesechka [4]
The answer is 22 , you’re welcome!
3 0
3 years ago
A cylinder has a volume of 198cm, and its base is 22cm. What is the height of the cylinder?
MrRa [10]
69 babyyyyyyyyyyyyyyyyyyyyyy
7 0
3 years ago
The sum of the measures of the angles of a triangle is 180. The sum of the measures of the second and third angles is five times
Art [367]

Answer: 30°, 67° e 83°.

Step-by-step explanation: The angles are (2x + 16)/5 , x e x + 16°

Adding all the angles, comes:

(2x + 16)/5 + x + x + 16° = 180° ;

2x + 16° + 5x + 5x + 80° = 900° ;

12x = 900° - 16° - 80° ;

12x = 804°° ;

x = 804°/12 ;

x = 67°

So, the first angle is (2,67 + 16)/5 = 30°, the second is 67° and the third is 83°.

7 0
3 years ago
Read 2 more answers
<img src="https://tex.z-dn.net/?f=%20%5Cfrac%7Bx%7D%7B10.1%7D%20%20%3D%203.5" id="TexFormula1" title=" \frac{x}{10.1} = 3.5" al
dsp73

Answer:

x=35.35

Step-by-step explanation:

If you multiply \frac{x}{10.1} .1 by 10.1 that will cancel out the 10.1s, then since you multiplies one side of the equation by something you have to multiply the other too.  So 3.5*10.1 = 35.35.  Then since the 10.1s canceled out this leaves us with x=35.35.  Let me know if you need any more help.  

5 0
3 years ago
If two linear regression models have the same number of explanatory variables, a model with an R2 value of 0.45 is a better pred
icang [17]

Answer:

False

Step-by-step explanation:

Given that two linear  regression models have the same number of explanatory variables

First model has coefficient of determination as 0.45 while other

0.65

we know that R^2 is the proportion showing the variation of dependent because of variation in independent variables.

Hence a higher R square is always better because it ensures more linearity and hence more accuracy in the regression equation.

Hence 0.65 model is better than 0.45 model.

The given statement is false.

5 0
3 years ago
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