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MrMuchimi
2 years ago
12

What makes this undefined.

Mathematics
2 answers:
nexus9112 [7]2 years ago
7 0
<h3>Answer:</h3>

The expression is undefined when x=3 or -3

<h3>Solution:</h3>
  • An expression is undefined when it's divided by 0.
  • In order to make the denominator equal 0, we need to subtract 9-9.
  • Notice that x is squared.
  • So x times itself should equal 9. What number is it?
  • That's right, 3.
  • So we have
  • 9-(3)^2=9-9=0
  • There's also another solution: x=-3 (-3•(-3)=9

Hope it helps.

Do comment if you have any query.

motikmotik2 years ago
5 0

Answer: The expression is undefined when x is 3 or -3

Step-by-step explanation:

The denominator of a fraction can never be 0 because you cannot divide by 0. So all we have to do is determine the value of x when the denominator is equal to 0.

9-x^{2} =0\\

We want x^2 to be positive so divide everything by -1

9-x^{2} =0\\\\\frac{9-x^{2}}{-1}=\frac{0}{-1}  \\-9+x^2=0\\x^2-9=0

Use difference of two squares (DOTS) to factor

x^2+9=0\\(x+3)(x-3)=0

So x cannot be 3 or -3

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A school club sold 300 shirts. 31% were sold to fifth-graders, 52% were sold to sixth graders, and the rest were sold to teacher
Valentin [98]

Answer:

Fifth graders= 93 shirts

Sixth graders= 156 shirts

Teachers= 51 shirts

Step-by-step explanation:

31% of 300 is 93

52% of 300 is 156

The total number of shirts sold to fifth graders and sixth graders is:

156+93= 249

The number of shirts sold to teachers is:

300-249= 51

6 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!!!

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