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sergij07 [2.7K]
3 years ago
12

I need the last part of it pls help!

Mathematics
1 answer:
Klio2033 [76]3 years ago
8 0

Answer:

  • Commutative

Step-by-step explanation:

Multiplication is Commutative. the term can be multiplied in either order. the product will be the same as the previous product.

<u>OAmalOHopeO</u>

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1. m+ 10&lt; 16
sleet_krkn [62]

Answer:

1.\ m

Step-by-step explanation:

1. Subtract 10 from both sides. Then:

m+ 10< 16\\ m+ 10-10< 16-10\\m

2. Divide both sides by -2. Notice that the direction of the symbol of the inequality will change. Then:

-2g\geq8\\\\\frac{-2g}{-2}\geq\frac{8}{-2}\\\\g\leq-4

3. Add 22 to both sides. Then:

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4. Divide both sides by -7. Notice that the direction of the symbol of the inequality will change. Then:

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5. Subtract 30 from both sides. Then:

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6. Divide both sides by -5. Notice that the direction of the symbol of the inequality will change. Then:

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7. Subtract 13 from both sides. Then:

t+13>22\\ t+13-13>22-13\\t>9

8. Add 12 to both sides. Then:

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Answer:

1. 18.84 in

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3. 4.71 ft

4. 25.12 m

5. 37.68 ft

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7 0
3 years ago
Three businesswomen are trying to convene in Northwest Arkansas for a business meeting. The first (Woman 1) is arriving on a fli
boyakko [2]

Answer:

a) The probability mass function of X is then presented in the table below.

X | probability P(X=x) or p

0 | 0.001

1 | 0.032

2 | 0.283

3 | 0.684

b) The cumulative distribution function of X

Cdf = Σ pdf = P(X=0) + P(X=1) + P(X=2) + P(X=3)

= 1.000

c) The probability that at least two businesswomen arrive on time

P(X ≥ 2) = P(X=2) + P(X=3) = 0.967

d) Expected value of X = E(X) = 2.65

e) Standard deviation = 0.545

Step-by-step explanation:

The probability that the woman coming from Atlanta arrives on time = P(A) = 0.90

The probability that the woman coming from Atlanta DOES NOT arrive on time = P(A') = 1 - 0.90 = 0.10

The probability that the woman coming from Dallas arrives on time = P(B) = 0.95

The probability that the woman coming from Dallas DOES NOT arrive on time = P(B') = 1 - 0.95 = 0.05

The probability that the woman coming from Chicago arrives on time = P(C) = 0.80

The probability that the woman coming from Chicago DOES NOT arrive on time = P(C') = 1 - 0.80 = 0.20

Since X is the random variable that represents how many women arrive on time,

To evaluate the probability function, we will first obtain the probability that the number of women that arrive in time = 0, 1, 2, and 3.

First probability; that no woman arrives on time. X = 0

P(X=0) = P(A') × P(B') × P(C')

= 0.10 × 0.05 × 0.20

P(X=0) = 0.001

Second probability; that only one of the women arrive on time. X = 1

P(X=1) = [P(A) × P(B') × P(C')] + [P(A') × P(B) × P(C')] + [P(A') × P(B') × P(C)]

= [0.90 × 0.05 × 0.20] + [0.10 × 0.95 × 0.20] + [0.10 × 0.05 × 0.80]

= 0.009 + 0.019 + 0.004

P(X=1) = 0.032

Third probability; that only two women arrive on time. X = 2

P(X=2) = [P(A) × P(B) × P(C')] + [P(A) × P(B') × P(C)] + [P(A') × P(B) × P(C)]

= [0.90 × 0.95 × 0.20] + [0.90 × 0.05 × 0.80] + [0.10 × 0.95 × 0.80]

= 0.171 + 0.036 + 0.076

P(X=2) = 0.283

Fourth probability; that all 3 women arrive on time. X = 3

P(X=3) = P(A) × P(B) × P(C)

= 0.90 × 0.95 × 0.8

P(X=3) = 0.684

The probability mass function of X is then presented in the table below.

X | probability P(X=x) or p

0 | 0.001

1 | 0.032

2 | 0.283

3 | 0.684

b) The cumulative distribution function of X

Cdf = Σ pdf = P(X=0) + P(X=1) + P(X=2) + P(X=3)

= 0.001 + 0.032 + 0.283 + 0.684 = 1.000

c) The probability that at least two businesswomen arrive on time

P(X ≥ 2) = P(X=2) + P(X=3) = 0.283 + 0.684 = 0.967

d) Expected value of X

Expected value is given as

E(X) = Σ xᵢpᵢ

E(X) = (0)(0.001) + (1)(0.032) + (2)(0.283) + (3)(0.684) = 0 + 0.032 + 0.566 + 2.052 = 2.65

e) What is the standard deviation of X?

Standard deviation = √(variance)

Variance = Var(X) = Σx²p − μ²

μ = E(X) = 2.65

Σx²p = (0²)(0.001) + (1²)(0.032) + (2²)(0.283) + (3²)(0.684)

= (0)(0.001) + (1)(0.032) + (4)(0.283) + (9)(0.684)

= 0 + 0.032 + 1.132 + 6.156

= 7.32

Variance = Var(X) = 7.32 - 2.65² = 7.32 - 7.0225

Var(X) = 0.2975

Standard deviation = √(variance) = √0.2975

Standard deviation = 0.545

Hope this Helps!!!

8 0
3 years ago
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