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}.
Answer:
4a + 3b
Step-by-step explanation:
3a + a + 5b - 2b
3a + s = 4a
5b - 2b = 3b
<em><u>4a + 3b</u></em>
Answer:
Step-by-step explanation:
A) Stem-and-leaf plot
stem leaf
5 1
7 6,7,8,9
8 1,2,4,6
9 9
B) Outlier
Could consider 51 an outlier.
Answer:
4.3
Step-by-step explanation:
first round
3.296 is 3.3
0.9785 is 1
3.3 + 1 = 4.3
