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Mariana [72]
3 years ago
14

If you are adding 2 fractions that are both greater than 1/2, what must be true about the sum? Give three examples to support yo

ur thinking.
Mathematics
2 answers:
tensa zangetsu [6.8K]3 years ago
8 0
It would be greater than 1, 3/4+3/4= 6/4
5/6+5/6=10/6
4/5+4/5=8/5
alukav5142 [94]3 years ago
7 0
It would be at least a whole number
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Help pls..................
Triss [41]

Answer:

Area = 152ft²

Step-by-step explanation:

Simpifly:

A = 13(13-6) + 9(13-6)

A = 13(7) + 9(7)

A = 22(7)

A = 22(7)

A = 20(7)+2(7)

A = 140 + 14

A = 152

5 0
3 years ago
Please help me with this<br> thanks<br> please let me know if my answer is incorrect
sergey [27]
I just graphed the table next to the graph of g and compared the two.

The y-intercept of f is (0,8) and the y-intercept of g is (0, -2).  So f is GREATER THAN the y-intercept of g.

Now to do the average rate of change, I see from the interval [-6,-3] that f is decreasing and for g it is increasing.  Thus f is LESS THAN the average rate of change of g.
5 0
3 years ago
Write the equation of the line that passes through the given point and is parallel to the given line.
Mekhanik [1.2K]

Answer:

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Step-by-step explanation:

5 0
3 years ago
These both go together
Airida [17]

a) We know that the probability Jane will win is 0.2, and draws is 0.3, which leaves the probability of her losing to be 0.5 (1 - 0.2 - 0.3 = 0.5).

I'll begin by filling in for the first game:

win = 0.2, draw = 0.3, lose = 0.5

Next, we'll fill in for if she wins, draws, or loses the second game. The probabilities would be the same as the first game for the second game.

Win (0.2): win = 0.2, draw = 0.3, lose = 0.5

Draw (0.3): win = 0.2, draw = 0.3, lose = 0.5

Lose (0.5): win = 0.2, draw = 0.3, lose = 0.5

b) To find the probability that Jane will win both games, we need to multiply the probability of Jane winning the first game by the probability of her winning the second game.

0.2 x 0.2 = 0.04

Hope this helps! :)

6 0
4 years ago
Wat is linear regression
Leno4ka [110]
Linear regression - the relation between variables when the regression equation is linear: e.g., y = ax + b
5 0
4 years ago
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