In linear algebra, the rank of a matrix
A
A is the dimension of the vector space generated (or spanned) by its columns.[1] This corresponds to the maximal number of linearly independent columns of
A
A. This, in turn, is identical to the dimension of the vector space spanned by its rows.[2] Rank is thus a measure of the "nondegenerateness" of the system of linear equations and linear transformation encoded by
A
A. There are multiple equivalent definitions of rank. A matrix's rank is one of its most fundamental characteristics.
The rank is commonly denoted by
rank
(
A
)
{\displaystyle \operatorname {rank} (A)} or
rk
(
A
)
{\displaystyle \operatorname {rk} (A)}; sometimes the parentheses are not written, as in
rank
A
{\displaystyle \operatorname {rank} A}.
First, let's only the expression with x stay on the let side: let's add 9 to both sides of the equation.

which is

now, let's multiply everything by 2:


so the answer is -32!
Answer:
10.6%
Step-by-step explanation:
Normal curves are symmetrical. That means that on a standard normal distribution, the area less than -1.25 is the same as the area greater than +1.25. The total area under the curve is 1, so:
P = 1 - 0.894
P = 0.106
Approximately 10.6% of the area under the curve lies below -1.25.
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