matrix algebra matrix algebra is a means of expressing large numbers of calculations made upon...
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Matrix Algebra Matrix algebra is a means of
expressing large numbers of calculations made upon ordered sets of numbers.
Often referred to as Linear Algebra
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Why use it? Matrix algebra is used primarily to
facilitate mathematical expression. Many equations would be
completely intractable if scalar mathematics had to be used. It is also important to note that the scalar algebra is under there somewhere.
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Definitions - scalar scalar - a number
denoted with regular type as is scalar algebra
[1] or [a]
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Definitions - vector vector - a single row or column of
numbers denoted with bold small letters row vector
column vector
54321
5
4
3
2
1
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Definitions - Matrix A matrix is a set of rows and
columns of numbers
Denoted with a bold Capital letter All matrices (and vectors) have an
order - that is the number of rows x the number of columns. Thus A is
654
321
32654
321
x
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Matrix Equality Thus two matrices are equal iff (if
and only if) all of their elements are identical
Note: your data set is a matrix.
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Matrix Operations Addition and Subtraction Multiplication Transposition Inversion
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Addition and Subtraction Two matrices may be added iff
they are the same order. Simply add the corresponding
elements
3231
2221
1211
3231
2221
1211
3231
2221
1211
cc
cc
cc
bb
bb
bb
aa
aa
aa
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Addition and Subtraction (cont.)
Where
Hence
129
107
85
64
64
64
65
43
21
323232
313131
222222
212121
121212
111111
cba
cba
cba
cba
cba
cba
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Matrix Multiplication To multiply a scalar times a matrix,
simply multiply each element of the matrix by the scalar quantity
2221
1211
2221
1211
22
222
aa
aa
aa
aa
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Matrix Multiplication (cont.) To multiply a matrix times a
matrix, we write A times B as AB
This is pre-multiplying B by A, or post-multiplying A by B.
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Matrix Multiplication (cont.) In order to multiply matrices, they
must be conformable (the number of columns in A must equal the number of rows in B.)
an (mxn) x (nxp) = (mxp) an (mxn) x (pxn) = cannot be done a (1xn) x (nx1) = a scalar (1x1)
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Matrix Multiplication (cont.) Thus
where
3231
2221
1211
3231
2221
1211
333231
232221
131211
cc
cc
cc
bb
bb
bb
x
aaa
aaa
aaa
32332232123132
31332132113131
32232222122122
31232122112121
32132212121112
31132112111111
bababac
bababac
bababac
bababac
bababac
bababac
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Matrix Multiplication- an example Thus
where
9642
8136
6630
63
52
41
963
852
741
3231
2221
1211
cc
cc
cc
x
96695643
42392613
81685542
36382512
66675441
30372411
32
31
22
21
12
11
***
***
***
***
***
***
c
c
c
c
c
c
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Matrix multiplication is not Commutative AB does not necessarily equal BA (BA may even be an impossible
operation)
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Yet matrix multiplication is Associative A(BC) = (AB)C
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Special matrices There are a number of special
matrices Diagonal Null Identity
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Diagonal Matrices A diagonal matrix is a square matrix
that has values on the diagonal with all off-diagonal entities being zero.
44
33
22
11
000
000
000
000
a
a
a
a
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Identity Matrix An identity matrix is a diagonal
matrix where the diagonal elements all equal one. It is used in a fashion analogous to multiplying through by "1" in scalar math.
1000
0100
0010
0001
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Null Matrix A square matrix where all elements
equal zero.
Not usually ‘used’ so much as sometimes the result of a calculation. Analogous to “a+b=0”
0000
0000
0000
0000
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The Transpose of a Matrix A' Taking the transpose is an
operation that creates a new matrix based on an existing one.
The rows of A = the columns of A' Hold upper left and lower right
corners and rotate 180 degrees.
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Example of a transpose
654
321
63
52
41
', AA
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The Transpose of a Matrix A' If A = A', then A is symmetric (i.e.
correlation matrix) If AA’ = A then A' is idempotent (and A' =
A) The transpose of a sum = sum of
transposes
The transpose of a product = the product of the transposes in reverse order
''')'( CBACBA
''')'( ABCABC
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An example: Suppose that you wish to obtain
the sum of squared errors from the vector e. Simply pre-multiply e by its transpose e'.
which, in matrices looks like
222
21 neeeee ..'
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An example - cont Since the matrix product is a scalar
found by summing the elements of the vector squared.
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The Determinant of a Matrix The determinant of a matrix A is
denoted by |A|. Determinants exist only for square
matrices. They are a matrix characteristic,
and they are also difficult to compute
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The Determinant for a 2x2 matrix
If A =
Then
That one is easy
21122211 aaaaA
2221
1211
aa
aa
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The Determinant for a 3x3 matrix If A =
Then
333231
232221
131211
aaa
aaa
aaa
312213322113332112312312322311332211 aaaaaaaaaaaaaaaaaaA
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Determinants For 4 x 4 and up don't try. For those
interested, expansion by minors and cofactors is the preferred method.
(However the spaghetti method works well! Simply duplicate all but the last column of the matrix next to the original and sum the products of the diagonals along the following pattern.)
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Spaghetti Method of |A|
3231
2221
1211
333231
232221
131211
aa
aa
aa
aaa
aaa
aaa
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Properties of Determinates Determinants have several
mathematical properties which are useful in matrix manipulations.
1 |A|=|A'|. 2. If a row of A = 0, then |A|= 0. 3. If every value in a row is multiplied by
k, then |A| = k|A|. 4. If two rows (or columns) are
interchanged the sign, but not value, of |A| changes.
5. If two rows are identical, |A| = 0.
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Properties of Determinates 6. |A| remains unchanged if each
element of a row or each element multiplied by a constant, is added to any other row.
7. Det of product = product of Det's |A| = |A| |B|
8. Det of a diagonal matrix = product of the diagonal elements
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The Inverse of a Matrix (A-
1) For an nxn matrix A, there may be a B
such that AB = I = BA. The inverse is analogous to a reciprocal) A matrix which has an inverse is
nonsingular. A matrix which does not have an
inverse is singular. An inverse exists only if
0A
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Inverse by Row or column operations Set up a tableau matrix A tableau for inversions consists of
the matrix to be inverted post multiplied by a conformable identity matrix.
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Matrix Inversion by Tableau Method
Rules: You may interchange rows. You may multiply a row by a scalar. You may replace a row with the sum of that
row and another row multiplied by a scalar. Every operation performed on A must
be performed on I When you are done; A = I & I = A-1
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The Tableau Method of Matrix Inversion: An Example Step 1: Set up Tableau
100
010
001
231
452
341
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Matrix Inversion – cont. Step 2: Add –2(Row 1) to Row 2
Step 3: Add –1(Row 1) to Row 3
100
012
001
231
230
341
101
012
001
570
230
341
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Matrix Inversion – cont. Step 4: Multiply Row 2 by –1/3
Step 5: Add –4 (Row 2) to Row 1
101
03132
001
570
3210
341
///
101
03132
03435
570
3210
3101
//
//
/
/
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Matrix Inversion – cont. Step 6: Add 7(Row 2) to Row 3
Step 7: Add Row 3 to Row 1
137311
03132
03435
3100
3210
3101
//
//
//
/
/
/
137311
03132
112
3100
3210
001
//
//
/
/
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Matrix Inversion – cont. Step 9: Add 2(Row 3) to Row 2
Step 9: Multiply Row 3 by -3
137311
258
112
3100
010
001
///
3711
258
112
100
010
001
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Checking the calculation Remember AA-1=I
Thus
100
010
001
3711
258
112
231
452
341
etc
0735411
11138421
***
***
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The Matrix Model The multiple regression model may
be easily represented in matrix terms.
Where the Y, X, B and e are all matrices of data, coefficients, or residuals
Y XB e
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The Matrix Model (cont.) The matrices in are
represented by
Note that we postmultiply X by B since this order makes them conformable.
Y
Y
Y
Yn
1
2
X
X X X
X X X
X X X
ik
k
n n nk
11 12
21 22 2
1 2
. . .
. . .
. . . . . . . . . . . .
. . .
B
B
B
B k
1
2 e
e
e
en
1
2
Y XB e
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The Assumptions of the ModelScalar Version
1. The ei's are normally distributed. 2. E(ei) = 0 3. E(ei
2) = 2
4. E(eiej) = 0 (ij) 5. X's are nonstochastic with values fixed in repeated
samples and (Xik-Xbark)2/n is a finite nonzero number. 6. The number of observations is greater than the
number of coefficients estimated. 7. No exact linear relationship exists between any of
the explanatory variables.
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The Assumptions of the Model: The Matrix Version These same assumptions
expressed in matrix format are:
1. e N(0,) 2. = 2I 3. The elements of X are fixed in
repeated samples and (1/ n)X'X is nonsingular and its elements are finite