Introduction to Vectors and Matrices
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Introduction to Vectors and Matrices

Matrices. Definition: A matrix is a rectangular array of numbers or symbolic elements. In many applications, the rows of a matrix will represent individuals cases (people, items, plants, animals,...) and columns will represent attributes or characteristics.

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Introduction to Vectors and Matrices




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Presentation on theme: "Introduction to Vectors and Matrices"— Presentation transcript:

Slide1

Introduction to Vectors and Matrices

Slide2

Matrices

Definition: A matrix is a rectangular array of numbers or symbolic elementsIn many applications, the rows of a matrix will represent individuals cases (people, items, plants, animals,...) and columns will represent attributes or characteristicsThe dimension of a matrix is its number of rows and columns, often denoted as r x c (r rows by c columns)Can be represented in full form or abbreviated form:

Slide3

Special Types of Matrices

Slide4

Regression Examples - Carpet Data

Slide5

Matrix Addition and Subtraction

Slide6

Matrix Multiplication

Slide7

Matrix Multiplication Examples - I

Slide8

Matrix Multiplication Examples - II

Slide9

Special Matrix Types

Slide10

Linear Dependence and Rank of a Matrix

Linear Dependence: When a linear function of the columns (rows) of a matrix produces a zero vector (one or more columns (rows) can be written as linear function of the other columns (rows))Rank of a matrix: Number of linearly independent columns (rows) of the matrix. Rank cannot exceed the minimum of the number of rows or columns of the matrix. rank(A) ≤ min(rA,ca) A matrix if full rank if rank(A) = min(rA,ca)

Slide11

Geometry of Vectors

A vector of order n is a point in n-dimensional space

The line running through the origin and the point represented by the vector defines a 1-dimensional subspace of the n-dim space

Any p linearly independent vectors of order n, p < n define a p-dimensional subspace of the n-dim space

Any p+1 vectors in a p-dim subspace must have a linear dependency

Two vectors

x

and

y

are orthogonal if

x

y

=

y

x

= 0 and form a 90

 angle at the origin

Two vectors

x

and

y

are linearly dependent if they form a 0

 or 18

0

 angle at the origin

Slide12

Geometry of Vectors - II

If two vectors each have mean 0 among their elements then

q

is the product moment correlation between the two vectors

Slide13

Slide14

Matrix Inverse

Note: For scalars (except 0), when we multiply a number, by its reciprocal, we get 1: 2(1/2)=1 x(1/x)=x(x-1)=1In matrix form if A is a square matrix and full rank (all rows and columns are linearly independent), then A has an inverse: A-1 such that: A-1 A = A A-1 = I

Slide15

Computing an Inverse of 2x2 Matrix

Slide16

Use of Inverse Matrix – Solving Simultaneous Equations

Slide17

Useful Matrix Results

Slide18

Orthogonal Matrices

Slide19

Eigenvalues and Eigenvectors

Slide20

Positive Definite Matrices / Spectral Decomposition

Slide21

Distance as a Quadratic Form

Slide22

Slide23

Square Root of a Positive Definite Square Matrix

Slide24

Mean, Variance, Covariance, Correlation

Slide25

Random Vectors and Matrices

Slide26

Mean and Variance of Linear Functions of X

Slide27

Standard Deviation and Correlation Matrices

LPGA “Population” Data:

Slide28

Partitioned Covariance Matrix

Slide29

Matrix Inequalities and Maximization

Slide30

Multivariate Normal Distribution