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Introduction to Matrices and Matrix Approach to Simple Linear Regression

Introduction to Matrices and Matrix Approach to Simple Linear Regression. Matrices. Definition: A matrix is a rectangular array of numbers or symbolic elements

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Introduction to Matrices and Matrix Approach to Simple Linear Regression

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  1. Introduction to Matrices and Matrix Approach to Simple Linear Regression

  2. 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 • The 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:

  3. Special Types of Matrices

  4. Regression Examples - Carpet Data

  5. Matrix Addition and Subtraction

  6. Matrix Multiplication

  7. Matrix Multiplication Examples - I

  8. Matrix Multiplication Examples - II

  9. Special Matrix Types

  10. 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)

  11. 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 u and v are orthogonal if u’v = v’u = 0 and form a 90 angle at the origin • Two vectors u and v are linearly dependent if they form a 0 or 180 angle at the origin

  12. Geometry of Vectors - II If two vectors each have mean 0 among their elements then cos(a) is the product moment correlation between the two vectors

  13. 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)=1 • In 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 = AA-1 = I

  14. Computing an Inverse of 2x2 Matrix

  15. Use of Inverse Matrix – Solving Simultaneous Equations

  16. Useful Matrix Results

  17. Random Vectors and Matrices

  18. Linear Regression Example (n=3)

  19. Mean and Variance of Linear Functions of Y

  20. Multivariate Normal Distribution

  21. Simple Linear Regression in Matrix Form

  22. Estimating Parameters by Least Squares

  23. Fitted Values and Residuals

  24. Analysis of Variance

  25. Inferences in Linear Regression

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