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}.
The formula is
A=p (1+r)^t
A future value?
P current value 140
R rate of inflation 0.034
T time 2008-2005=3 years
A=140×(1+0.034)^(3)
A=154.77 round your answer to get
A=155
The answer is: "72" .
______________________________________________
(input value, or "x") / 10 = y = f(x);
When y = 7.2, what is the value of "x" ?
x / 10 = 7.2 ;
x = (7.2) * 10 = 72 .
_______________________
Answer:
1.30
Step-by-step explanation:
When you multiply 5.20x0.25 you get 1.30
