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musickatia [10]
2 years ago
14

Complete the square to make a perfect square trinomial. Then, write the result as a binomial squared. q2+11q

Mathematics
2 answers:
Sedbober [7]2 years ago
8 0

Answer:

(q+\frac{11}{2})^2-\frac{121}{4}

Step-by-step explanation:

We have been given an expression q^2+11q. We are asked to complete the square to make a perfect square trinomial. Then, write the result as a binomial squared.

We know that a perfect square trinomial is in form a^2+2ab+b^2.

To convert our given expression into perfect square trinomial, we need to add and subtract (\frac{b}{2})^2 from our given expression.

We can see that value of b is 11, so we need to add and subtract (\frac{11}{2})^2 to our expression as:

q^2+11q+(\frac{11}{2})^2-(\frac{11}{2})^2

Upon comparing our expression with (a+b)^2=a^2+2ab+b^2, we can see that a=q, 2ab=11q and b=\frac{11}{2}.

Upon simplifying our expression, we will get:

(q+\frac{11}{2})^2-\frac{11^2}{2^2}

(q+\frac{11}{2})^2-\frac{121}{4}

Therefore, our perfect square would be (q+\frac{11}{2})^2-\frac{121}{4}.

hram777 [196]2 years ago
7 0

Answer:

(q+\frac{11}{2})^2-\frac{121}{4}

Step-by-step explanation:

The given expression is

q^2+11q

We need to write the result as a binomial square.

If an expression is x^2+bx, then we need to add (\frac{b}{2})^2 in the expression to make it perfect square.

In the given expression b=11 and x=q.

(\frac{b}{2})^2=(\frac{11}{2})^2

Add and subtract (\frac{11}{2})^2 in the given expression.

q^2+11q+(\frac{11}{2})^2-(\frac{11}{2})^2

(q+\frac{11}{2})^2-(\frac{11}{2})^2         [\because (a+b)^2=a^2+2ab+b^2]

(q+\frac{11}{2})^2-\frac{121}{4}

Therefore, the result as a binomial square is (q+\frac{11}{2})^2-\frac{121}{4}.

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Suppose that we wish to expand (x + y + z)17. what is the coefficient of x 2 y5z10
Delvig [45]
We use the trinomial theorem to answer this question. Suppose we have a trinomial (a + b + c)ⁿ, we can determine any term to be:

[n!/(n-m)!(m-k)!k!] a^(n-m) b^(m-k) c^k

In this problem, the variables are: x=a, y=b and z=c. We already know the exponents of the variables. So, we equate this with the form of the trinomial theorem.

n - m = 2
m - k = 5
k = 10

Since we know k, we can determine m. Once we know m, we can determine n. Then, we can finally solve for the coefficient.

m - 10 = 5
m = 15

n - 15 = 2
n = 17

Therefore, the coefficient is equal to:

Coefficient = n!/(n-m)!(m-k)!k! = 17!/(17-5)!(15-10)!10! = 408,408


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2 years ago
Lisa earns $8.10 an hour and worked 40 hours. Jamie earns $10.80 an hour. How many hours would Jamie need to work to equal Lisa’
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Henry is asked to find the exact value of cos 10pi/3. His steps are shown below.
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Answer:

<em>A) The reference angle should be </em><u><em>\frac{\pi }{3},</em></u><em> and the sign of the value should be </em><u><em>negative.</em></u>

Step-by-step explanation:

cos(\frac{10\pi }{3})

Remove full rotations of 2π until the angle is between 0 and 2\pi.

cos(\frac{4\pi }{3})

Apply the reference angle by finding the angle with equivalent trig values in the first quadrant. Make the expression negative because cosine is negative in the third quadrant.

 -cos(\frac{\pi }{3})

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The heat evolved in calories per gram of a cement mixture is approximately normally distributed. The mean is thought to be 100,
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Answer:

A.the type 1 error probability is \mathbf{\alpha = 0.0244 }

B. β  = 0.0122

C. β  = 0.0000

Step-by-step explanation:

Given that:

Mean = 100

standard deviation = 2

sample size = 9

The null and the alternative hypothesis can be computed as follows:

\mathtt{H_o: \mu = 100}

\mathtt{H_1: \mu \neq 100}

A. If the acceptance region is defined as 98.5 <  \overline x >  101.5 , find the type I error probability \alpha .

Assuming the critical region lies within \overline x < 98.5 or \overline x > 101.5, for a type 1 error to take place, then the sample average x will be within the critical region when the true mean heat evolved is \mu = 100

∴

\mathtt{\alpha = P( type  \ 1  \ error ) = P( reject \  H_o)}

\mathtt{\alpha = P( \overline x < 98.5 ) + P( \overline x > 101.5  )}

when  \mu = 100

\mathtt{\alpha = P \begin {pmatrix} \dfrac{\overline X - \mu}{\dfrac{\sigma}{\sqrt{n}}} < \dfrac{\overline 98.5 - 100}{\dfrac{2}{\sqrt{9}}} \end {pmatrix} + \begin {pmatrix}P(\dfrac{\overline X - \mu}{\dfrac{\sigma}{\sqrt{n}}}  > \dfrac{101.5 - 100}{\dfrac{2}{\sqrt{9}}} \end {pmatrix} }

\mathtt{\alpha = P ( Z < \dfrac{-1.5}{\dfrac{2}{3}} ) + P(Z  > \dfrac{1.5}{\dfrac{2}{3}}) }

\mathtt{\alpha = P ( Z  2.25) }

\mathtt{\alpha = P ( Z

From the standard normal distribution tables

\mathtt{\alpha = 0.0122+( 1-  0.9878) })

\mathtt{\alpha = 0.0122+( 0.0122) })

\mathbf{\alpha = 0.0244 }

Thus, the type 1 error probability is \mathbf{\alpha = 0.0244 }

B. Find beta for the case where the true mean heat evolved is 103.

The probability of type II error is represented by β. Type II error implies that we fail to reject null hypothesis \mathtt{H_o}

Thus;

β = P( type II error) - P( fail to reject \mathtt{H_o} )

∴

\mathtt{\beta = P(98.5 \leq \overline x \leq  101.5)           }

Given that \mu = 103

\mathtt{\beta = P( \dfrac{98.5 -103}{\dfrac{2}{\sqrt{9}}} \leq \dfrac{\overline X - \mu}{\dfrac{\sigma}{n}} \leq \dfrac{101.5-103}{\dfrac{2}{\sqrt{9}}}) }

\mathtt{\beta = P( \dfrac{-4.5}{\dfrac{2}{3}} \leq Z \leq \dfrac{-1.5}{\dfrac{2}{3}}) }

\mathtt{\beta = P(-6.75 \leq Z \leq -2.25) }

\mathtt{\beta = P(z< -2.25) - P(z < -6.75 )}

From standard normal distribution table

β  = 0.0122 - 0.0000

β  = 0.0122

C. Find beta for the case where the true mean heat evolved is 105. This value of beta is smaller than the one found in part (b) above. Why?

\mathtt{\beta = P(98.5 \leq \overline x \leq  101.5)           }

Given that \mu = 105

\mathtt{\beta = P( \dfrac{98.5 -105}{\dfrac{2}{\sqrt{9}}} \leq \dfrac{\overline X - \mu}{\dfrac{\sigma}{n}} \leq \dfrac{101.5-105}{\dfrac{2}{\sqrt{9}}}) }

\mathtt{\beta = P( \dfrac{-6.5}{\dfrac{2}{3}} \leq Z \leq \dfrac{-3.5}{\dfrac{2}{3}}) }

\mathtt{\beta = P(-9.75 \leq Z \leq -5.25) }

\mathtt{\beta = P(z< -5.25) - P(z < -9.75 )}

From standard normal distribution table

β  = 0.0000 - 0.0000

β  = 0.0000

The reason why the value of beta is smaller here is that since the difference between the value for the true mean and the hypothesized value increases, the probability of type II error decreases.

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