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RoseWind [281]
3 years ago
15

HELPPPPPPPPPPPPPPPPPPPPP

Mathematics
1 answer:
Juliette [100K]3 years ago
3 0

Step-by-step explanation:

  • x^2+10x=0

(x+10)(x-0)=0

x=-10 and x=0

  • x=-10 in x^2+10x+h=h

(-10)^2+10(-10)+h=h

100-100+h=h

0=0

  • x=0 inx^2+10x+h =h

0^2+10(0)+h=h

0+h=h

0=0

I think,you miss some numbers

if you don't miss some numbers, this solution is no solution

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Please show work thank you math experts
AlekseyPX

Answer:

1. 35 - 176 = -141

2. 0.55 × 1.79 = 0.9845

3. 11.38 × 5.46 = 62.1348

Hope it helps and have a great day! =D

~sunshine~

6 0
2 years ago
The sunrise café gets tea bags of 1,000. If the café charges $1.75 for each cup of tea, and each cup of tea gets 1 tea bag, how
Setler [38]

Answer:

You would do $1.75 x 1,000. That would be 1$,750. Then, you would take away that $95 because they spent buying the box of tea bags, and their total would be $1,750 - $95 = $1,655.

5 0
3 years ago
Sophia is 6 years older than her brother Eric. The sum of their ages is 28
Step2247 [10]

Answer:

Sophia is 17 years old. Sophia's brother is 11 years old.

Step-by-step explanation:

7 0
3 years ago
Tyler is playing a game in which he needs to guess an integer. Which of the following is a number that tyler can gues
Ksenya-84 [330]
Well a interger is any negative number so -1 or -2 or -3 or -4......
4 0
3 years ago
Suppose that in a casino game the payout is a random variable X . If X is positive, you gain money, if negative, you lose.
Vanyuwa [196]

Answer:

the conditional probability that X = 1 , X = 2 and X = 3 is  0.7333 (73.33%) , 0.25 (25%) and 0.0167 (1.67%) respectively

Step-by-step explanation:

a player wins money when i>0 then defining event W= gain money , then

P(W) = p(i>0) = p(1)+p(2)+p(3)

then the conditional probability can be calculated through the theorem of Bayes

P(X=1/W)= P(X=1 ∩ W)/P(W)

where

P(X=1 ∩ W)= probability that the payout is 1 and earns money

P(X=1 / W)= probability that the payout is 1 given money was earned

then

P(X=1/W)= P(X=1 ∩ W)/P(W) = P(X=1) / P(W) = p(1) /[p(1)+p(2)+p(3)] = 11/40 /(11/40+3/32+1/160 ) = 0.7333 (73.33%)

similarly

P(X=2/W)=p(2) /[p(1)+p(2)+p(3)] = 3/32 /(11/40+3/32+1/160 ) = 0.25 (25%)

P(X=3/W)=p(2) /[p(1)+p(2)+p(3)] = 1/160 /(11/40+3/32+1/160 ) = 0.0167 (1.67%)

8 0
3 years ago
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