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Over [174]
3 years ago
7

Math work pls help :)​

Mathematics
1 answer:
Gwar [14]3 years ago
7 0

Answer:

C ASA

Angle side Angle.....

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What is the number of solutions in this system?
Nana76 [90]

Answer:

No solution.

Step-by-step explanation:

Since the solution lies on three distinct planes.

Regards: Umer

6 0
4 years ago
A while back, either james borrowed $12 from his friend rita or she borrowed $12 from him, but he can’t quite remember which. ei
LenaWriter [7]
To answer the question let x be the amount of money James will have after he sees Rita. If it was him who owed Rita $12 and he intent to pay, he will have $42.80 - $12 after seeing her. However, if it was Rita who owed him the amount, the money he will have after seeing her will be $42.80 + $12. Thus, the equations are,
                      x = 42.80 - 12          and           x = 42.80 + 12
8 0
4 years ago
Read 2 more answers
: Your experience indicates that offering a discount in your emails increases responses by 80%. Your last email, without a disco
timofeeve [1]
For this case we can make the following rule of three:
 23000 -------> 100%
 x --------------> 180%
 Clearing the value of x we have:
 x = (180/100) * (23000)
 x = 41400
 Answer:
 
you would expect about:
 
a) 41,400 responses
8 0
3 years ago
A sales person earns 300.50 per week plus 7% of her weekly sales. Which of the following describes the sales necessary for the s
vodomira [7]
300.50 + 0.07x > = 900.85
0.07x > = 900.85 - 300.50
0.07x > = 600.35
x > = 600.35 / 0.07
x > = 8576.43 <=== she would have to sell at least 8576.43
3 0
4 years ago
Find a formula for the least squares solution of ax=b when the columns of a are orthonormal1 .
neonofarm [45]
Let \mathbf A be a rectangular m\times n matrix with column vectors \mathbf a_1,\ldots,\mathbf a_n, i.e.

\mathbf A=\begin{bmatrix}\mathbf a_1&\cdots&\mathbf a_n\end{bmatrix}

Then we have

\mathbf A^\top=\begin{bmatrix}\mathbf a_1&\cdots&\mathbf a_n\end{bmatrix}^\top

and the product of the two is

\mathbf A^\top\mathbf A=\begin{bmatrix}\mathbf a_1\cdot\mathbf a_1&\mathbf a_1\cdot\mathbf a_2&\cdots&\mathbf a_1\cdot\mathbf a_n\\\mathbf a_2\cdot\mathbf a_1&\mathbf a_2\cdot\mathbf a_2&\cdots&\mathbf a_2\cdot\mathbf a_n\\\vdots&\vdots&\ddots&\vdots\\\mathbf a_n\cdot\mathbf a_1&\mathbf a_n\cdot\mathbf a_2&\cdots&\mathbf a_n\cdot\mathbf a_n\end{bmatrix}

Because the columns of \mathbf A are orthonormal, we have

\mathbf a_i\cdot\mathbf a_j=\begin{cases}1&\text{for }i=j\\0&\text{for }i\neq j\end{cases}

which means \mathbf A^\top\mathbf A reduces to an n\times n matrix with ones along the diagonal and zero everywhere else, i.e.

\mathbf A^\top\mathbf A=\begin{bmatrix}1&0&\cdots&0\\0&1&\cdots&0\\\vdots&\vdots&\ddots&\vdots\\0&0&\cdots&1\end{bmatrix}=\mathbf I_n

where \mathbf I denotes the identity matrix. This means the solution to \mathbf{Ax}=\mathbf b is given by

\mathbf A^\top(\mathbf{Ax})=\mathbf A^\top\mathbf b\implies(\underbrace{\mathbf A^\top\mathbf A}_{\mathbf I})\mathbf x=\mathbf A^\top\mathbf b\implies\mathbf x=\mathbf A^\top\mathbf b
6 0
3 years ago
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