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2 years ago

How do you solve 3b(9b-27)=0

Mathematics

2 answers:

insens350 [35]2 years ago

3 0

Answer:0

Step-by-step explanation:3b(-27 + 9b) = 0

(-27 * 3b + 9b * 3b) = 0

(-81b + 27b2) = 0

pickupchik [31]2 years ago

3 0

Answer:

$b=3$ , or $0$

Step-by-step explanation:

$\mathrm{Using\:the\:Zero\:Factor\:Principle:\quad \:If}\:ab=0\:\mathrm{then}\:a=0\:\mathrm{or}\:b=0$

$b=0\quad \mathrm{or}\quad \:9b-27=0$

$9b-27=0$ , $\mathrm{Add\:}27\mathrm{\:to\:both\:sides}.$

$9b-27+27=0+27$

$9b=27$

$\mathrm{Divide\:both\:sides\:by\:}9$

$\frac{9b}{9}=\frac{27}{9}$

$b=3$

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Americans receive an average of 20 Christmas cards each year. Suppose the number of Christmas cards is normally distributed with

soldier1979 [14.2K]

The distribution of X is X ~ N (20 , 6) and the probability that this American will receive no more than 24 Christmas cards this year is 0.7486.

<h3>Probability</h3>

a. Distribution

X ~ N (20 , 6)

b. P(x ≤24)

= P[(x - μ ) / σ (24 - 20) / 6]

= P(z ≤0.67)

= 0.74857

=0.7486

Hence:

Probability = 0.7486

c. P(21 < x < 26)

= P[(21 - 26)/ 6) < (x - μ ) / σ < (24 - 20) / 6) ]

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= P(z < 0.67) - P(z < -0.)

= 0.74857- 0.2033

= 0.54527

Hence:

Probability =0.54527

d. Using standard normal table ,

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P(Z < 0.50) = 0.66

z = 0.50

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x = z× σ + μ

x = 0.50 × 6 + 20 = 23

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2 years ago

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Answer:

Step-by-step explanation:

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Two stores sell CDs in packages, as shown in the table below.

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3 years ago

Read 2 more answers

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

koban [17]

Answer:

a) No, because the probabilities of winning the 2nd game are dependant on the result of the 1st game. The probabilities are different if you win or lose the first game.

b) P=0.42

c) P=0.08

X | P(X)

------------------

0 | 0.42

1 | 0.50

2 | 0.08

e) E(x)=0.66

s.d.=0.62

Step-by-step explanation:

a) No, because the probabilities of winning the 2nd game are dependant on the result of the 1st game. The probabilities are different if you win or lose the first game.

b) The probability of losing the first game is

$P(G_1=L)=1-P(G_1=W)=1-0.4=0.6$

The probability of losing the second game, given that the first game was lost is:

$P(G_2=L|G_1=L)=1-P(G_2=W|G_1=L)=1-0.3=0.7$

So the probability of losing both games is:

$P(G_2=L\&G_1=L)=P(G_1=L)*P(G_2=L|G_1=L)=0.6*0.7=0.42$

c) The probability of winning both games is:

$P(G_2=W\&G_1=W)=P(G_1=W)*P(G_2=W|G_1=W)=0.4*0.2=0.08$

d) The variable X can take values 0, 1 and 2.

X=0 is when both games are lost. This happens with probability P=0.42.

X=2 is when both games are won. This happens with probability P=0.08.

X=1 is when one game is won and the other is lost. This happens with probability P=1-0.42-0.08=0.50.

Then the table of probabilities become:

X | P(X)

------------------

0 | 0.42

1 | 0.50

2 | 0.08

e) The expected value is:

$E(x)=\sum p_ix_i=0.42*0+0.50*1+0.08*2=0.00+0.50+0.16=0.66$

The variance and standard deviation of x are:

$V(x)=\sum p_i(x_i-E(x))^2\\\\V(x)=0.42(0-0.66)^2+0.50(1-0.66)^2+0.08(2-0.66)^2\\\\V(x)=0.42*0.4356+0.50*0.1156+0.08*1.7956\\\\V(x)=0.183+0.058+0.144=0.385\\\\\\\sigma=\sqrt{V(x)}=\sqrt{0.385}=0.62$

The standard deviation can

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