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Can someone help me and can y’all show me how you got the answer

Answers

Answer 1

Answer:

Answer:

x = -8

Step-by-step explanation:

Step 1: Write equation

1/2x + 13 = 9

Step 2: Solve for x

Subtract 13 on both sides: 1/2x = -4
Multiply both sides by 2: x = -8

Step 3: Check

Plug in x to verify it's a solution.

1/2(-8) + 13 = 9

-4 + 13 = 9

9 = 9

Answer 2

Answer:

Answer:

-8

Step-by-step explanation:

you use inverse operation

meaning opposite signs

subtract -13 from 13 cross it out

subtract 13 from 9

you get 1/2x=-4

divide 1/2 on both sides

-4 divided by 1/2 =-8

Related Questions

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For the set data below, determine the 5 number summaries. Construct the resulting box and whisker plot for 5, 3,8, 6, 1, -10,3,9,7,4,2

Answers

The 5 number summaries Median: 4, Minimum: -10, Maximum: 9, First quartile: 2, Third quartile: 7

What are numbers?

Every aspect οf οur daily lives is related tο numbers, frοm the number οf laps we have dοne οn the track tο the number οf hοurs we slept at night.

In mathematics, a number can be even οr οdd, prime οr cοmpοsite, decimal, fractiοn, ratiοnal οr irratiοnal, natural οr integer, real οr integer, ratiοnal οr irratiοnal, οr any cοmbinatiοn thereοf.

Put the values in the data set into increasing order:

−10, 1, 2, 3, 3, 4, 5, 6, 7, 8, 9

The minimum (the smallest number) is −10

and the maximum (the largest number) is 9.

Find the median,

The median is the middle number

The median is = 4.

Find Q1 and Q3.

Q1 = 2, Q3 = 7

Range = max−min = 9−(−1) = 19

Interquartile range = Q3−Q1 = 7−2 = 5.

Thus,

The 5 number summaries Median: 4, Minimum: -10, Maximum: 9, First quartile: 2, Third quartile: 7

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A can of paint covers 400 square feet how many cans will needed to cover 3,000 square feet

Answers

Answer:

7 and a half cans of Paint ( 7.5 cans of paint )

Step-by-step explanation:

If one can of paint covers 400 square feet then this should be easy. Since you need 3,000 square feet covered with paint (and one paint can cover 400 sq) then just divide 3,000 by 400.

This will show you how many times 400 can go into 3,000 which is the same as saying "how many cans of paint can i used to cover 3,000 square feet?"

Hope this helped!

-Greg

A die is rolled twice.What is the probability that the first roll was a 6 and the second roll was an odd number?

Answers

Answer:

1/12

Step-by-step explanation:

1/6 * 3/6 (First roll has a one in 6 chance, second 3 in 6)

1*3 / 36 =

3 / 36 =

1/12

Of all postsecondary degrees awarded in the United States, including master's and doctorate degrees, 21% are associate's degrees, 58% are earned by people whose race is White, and 12% are associate's degrees earned by Whites. What is the conditional probability that a degree is earned by a person whose race is White, given that it is an associate's degree

Answers

The conditional probability that a degree is earned by a person whose race is White, given that it is an associate's degree, is 57.14%.

Given that of all postsecondary degrees awarded in the United States, including master's and doctorate degrees, 21% are associate's degrees, 58% are earned by people whose race is White, and 12% are associate's degrees earned by Whites, to determine what is the conditional probability that a degree is earned by a person whose race is White, given that it is an associate's degree, the following calculation should be performed:

A cross multiplication must be applied to obtain the percentage of whites who have obtained an associate degree.
21 = 100
12 = X
12 x 100/21 = X
1200/21 = X
57.14 = X

Therefore, the conditional probability that a degree is earned by a person whose race is White, given that it is an associate's degree, is 57.14%.

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Final answer:

The conditional probability that a degree is earned by a person whose race is White, given that it is an associate's degree, is 57 %.

Explanation:

The question asks us to compute the conditional probability that a degree is earned by a person whose race is White, given that it is an associate's degree. The definition of conditional probability P(A|B) is the probability of event A given that event B has happened. In your case, event A is 'degree is earned by a person whose race is White' and event B is 'the degree is an associate's degree'.

The probability P(A|B) can be calculated using the formula: P(A|B) = P(A ∩ B) / P(B).

Applying this information to your question, P(A ∩ B) is the probability that the degree is an associate's degree earned by a person whose race is white, which is 12 %. P(B) is the probability that the degree is an associate's degree, which is 21 %. Therefore, P(A|B) = 12/21 = 0.57 or 57 %.

So the conditional probability that a degree is earned by a person whose race is White, given that it is an associate's degree is 57 %.

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Use the Gram-Schmidt process to find an orthonormal basis for the subspace of R4 spanned by the vectors (1, 0, 1, 1), (1, 0, 1, 0), (0, 0, 1, 1).

Answers

Answer:

$$ e_1 = \begin{pmatrix}\frac{\sqrt{\textbf{3}}}{\textbf{2}}\n\n\textbf{0} \n\n\frac{\sqrt{\textbf{3}}}{\textbf{3}}\n\n\frac{\sqrt{\textbf{3}}}{\textbf{3}}\end{pmatrix}$ $$ e_2 = \begin{pmatrix}\frac{\sqrt{\textbf{6}}}{\textbf{6}}\n\n\textbf{0} \n\n\frac{\sqrt{\textbf{6}}}{\textbf{6}}\n\n\frac{\sqrt{\textbf{-6}}}{\textbf{3}}\end{pmatrix}$ $$ e_3 = \begin{pmatrix}\frac{\sqrt{\textbf{-2}}}{\textbf{2}}\n\n\textbf{0} \n\n\frac{\sqrt{\textbf{2}}}{\textbf{2}}\n\n0\end{pmatrix}$

Step-by-step explanation:

we have to orthonormalize the vectors:

$v_1 = \begin{pmatrix} 1 \n 0 \n 1 \n 1 \end{pmatrix}$ $v_2 = \begin{pmatrix} 1 \n 0 \n 1 \n 0 \end{pmatrix}$ $$ v_3 = \begin{pmatrix} 0 \n 0 \n 1 \n 1 \end{pmatrix}$

According to Gram - Schmidt process, we have:

$u_k = v_k - \sum_(j = 1) ^ {k - 1} proj_(uj) (v_k)$ where, $$ proj_u (v) = (u . v)/(u . u)u$

The normalized vector is: $e_k = (u_k)/(√(u_k.u_k))$

Now, the first step.

$v_1 = \begin{pmatrix} 1 \n 0 \n 1 \n 1 \end{pmatrix}$ = u₁

Therefore, e₁ = $(u_1)/(√(u_1.u_1))$

$$ = \begin{pmatrix}\frac{\sqrt{\textbf{3}}}{\textbf{2}}\n\n\textbf{0} \n\n\frac{\sqrt{\textbf{3}}}{\textbf{3}}\n\n\frac{\sqrt{\textbf{3}}}{\textbf{3}}\end{pmatrix}$

Now, we find e₂.

$u_2 = v_2 - (u_1.v_2)/(u_1.u_1)u_1$

$$ = \begin{pmatrix} (1)/(3)\n \n 0 \n\n (1)/(3)\n\n (-2)/(3) \end{pmatrix}$

Therefore, $e_2 = (u_2)/(√(u_2.u_2))$

$$ e_2 = \begin{pmatrix}\frac{\sqrt{\textbf{6}}}{\textbf{6}}\n\n\textbf{0} \n\n\frac{\sqrt{\textbf{6}}}{\textbf{6}}\n\n\frac{\sqrt{\textbf{-6}}}{\textbf{3}}\end{pmatrix}$

To find e₃:

$u_3 = v_3 - (u_1. v_3)/(u_1.u_1)u_1 - (u_2. v_3)/(u_2.u_2) u_2$

$$ = \begin{pmatrix} (-1)/(2) \n\n 0\n \n (1)/(2) \n\n 0 \n\end{pmatrix}$

$e_3 = (u_3)/(√(u_3.u_3))$

$$ e_3 = \begin{pmatrix}\frac{\sqrt{\textbf{-2}}}{\textbf{2}}\n\n\textbf{0} \n\n\frac{\sqrt{\textbf{2}}}{\textbf{2}}\n\n0\end{pmatrix}$

So, we have the orthonormalized vectors $e_1, e_2, e_3$ .

Hence, the answer.

Final answer:

To find the orthonormal basis using the Gram-Schmidt process, we calculate the first vector by dividing the first given vector by its magnitude and normalize it. Then, we subtract the projection of each subsequent vector onto the previously found orthonormal vectors and normalize the resulting vector.

Explanation:

To find an orthonormal basis for the subspace of R4 spanned by the given vectors using the Gram-Schmidt process, we will start by finding the first vector of the orthonormal basis. Let's call the given vectors v1, v2, and v3, respectively. The first vector of the orthonormal basis, u1, is equal to v1 divided by its magnitude, which is ||v1||. So, u1 = v1 / ||v1||. We can calculate ||v1|| as √(1^2 + 0^2 + 1^2 + 1^2) = √3.

Therefore, u1 = (1/√3, 0/√3, 1/√3, 1/√3).

Now, we need to find u2, the second vector of the orthonormal basis. To do this, we subtract the projection of v2 onto u1 from v2, then divide the result by its magnitude. We calculate the projection of v2 onto u1 as proj_u1(v2) = u1 * dot(u1, v2), where dot(u1, v2) represents the dot product of u1 and v2.

Finally, we subtract proj_u1(v2) from v2 to get v2' = v2 - proj_u1(v2), and then normalize v2' to get u2 = v2' / ||v2'||.

We can repeat this process to find u3, the third vector of the orthonormal basis. Subtract proj_u1(v3) and proj_u2(v3) from v3, then normalize the result to get u3 = v3' / ||v3'||.

Therefore, the orthonormal basis for the subspace spanned by the given vectors is (1/√3, 0/√3, 1/√3, 1/√3), (0, 0, 0, 1), and (-1/√3, 0/√3, 1/√3, 1/√3).