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Step2247 [10]
3 years ago
7

Hhhhhhhhhhhheeeeeeeellllllpppppp

Mathematics
2 answers:
AfilCa [17]3 years ago
7 0

Answer:

1.5

Step-by-step explanation:

Jlenok [28]3 years ago
5 0

Don't know the answer sorry

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fist bubble i am pretty sure

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What’s the slope for RS
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Answer: 4/2

Step-by-step explanation: Rise over run. From point S to R it goes up 4 and over 2.

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What is the square root of 144??
Maksim231197 [3]
12, or -12
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Read 2 more answers
Law of Sines or Law of Cosines....Help!!
iren2701 [21]

Answer:

Law of cosines to find missing measures

c^2= a^2+ b^2 - 2ab*cos(C)

Used because it is a SAS triangle

Step-by-step explanation:

This is known as a side, angle, side, or SAS triangle. We can find the missing measures by using the Law of cosines

c^2= a^2+b^2-2ab*cos(C)

7 0
3 years ago
You play two games against the same opponent. The probability you win the first game is 0.4. If you win the first game, the prob
Ilia_Sergeevich [38]

Answer:

a) No

b) 42%

c) 8%

d) X               0                 1                2

   P(X)           42%            50%         8%

e) 0.62

Step-by-step explanation:

a) No, the two games are not independent because the the probability you win the second game is dependent on the probability that you win or lose the second game.

b) P(lose first game) = 1 - P(win first game) = 1 - 0.4 = 0.6

P(lose second game) = 1 - P(win second game) = 1 - 0.3 = 0.7

P(lose both games) = P(lose first game)  × P(lose second game) = 0.6 × 0.7 = 0.42 = 42%

c)   P(win first game)  = 0.4

P(win second game) = 0.2

P(win both games) = P(win first game)  × P(win second game) = 0.4 × 0.2 = 0.08 = 8%

d) X               0                 1                2

   P(X)           42%            50%         8%

P(X = 0)  =  P(lose both games) = P(lose first game)  × P(lose second game) = 0.6 × 0.7 = 0.42 = 42%

P(X = 1) = [ P(lose first game)  × P(win second game)] + [ P(win first game)  × P(lose second game)] = ( 0.6 × 0.3) + (0.4 × 0.8) = 0.18 + 0.32 = 0.5 = 50%

e) The expected value  \mu=\Sigma}xP(x)= (0*0.42)+(1*0.5)+(2*0.08)=0.66

f) Variance \sigma^2=\Sigma(x-\mu^2)p(x)= (0-0.66)^2*0.42+ (1-0.66)^2*0.5+ (2-0.66)^2*0.08=0.3844

Standard deviation \sigma=\sqrt{variance} = \sqrt{0.3844}=0.62

8 0
3 years ago
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