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Statistical Analysis Professor Lynne Stokes
Department of Statistical Science Lecture 6QF Multivariate Normal Distribution, Chi-square Distribution of Quadratic Forms, Testing the Significance of Factor Effectrs
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Quadratic Forms Distributional properties of q depend on both
the properties of the known matrix A and the distribution of the random vector x.
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Multivariate Normal Distribution
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Properties of the Covariance Matrix
Nonsingular Symmetric Positive Definite Positive (Semi-) Definite Matrices Similar Definitions: Negative (Semi-) Definite, Indefinite
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Distribution of Quadratic Forms in Normal Random Variables
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Trace of a Square Matrix
Definition Properties Cyclic Permutations li = eigenvalues of A Symmetric Idempotent Matrix li = 1 or 0
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Sample Variance Probability Distribution
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Total Sum of Squares Quadratic Form Show Degrees of Freedom
n -1 = ar - 1 = rank(AT) = tr(AT)
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Main Effect Sum of Squares
Quadratic Form Show Degrees of Freedom a -1 = rank(AA) = tr(AA)
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Main Effect Sum of Squares
Probability Distribution Show
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Main Effect Noncentrality Parameter
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Error Sum of Squares Quadratic Form Degrees of Freedom Show
n - a = rank(AE) = tr(AE)
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Error Sum of Squares Probability Distribution Show
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Pairwise Independence of Quadratic forms
Independence of the Main Effect and Error Sums of Squares
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Statistical Tests for (Fixed) Main Effects and Interactions : Balanced Complete Factorials
Single-Factor Experiment Response Distribution y ~ N(m1 + XAa , s2I)
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Statistical Tests for (Fixed) Main Effects and Interactions : Balanced Complete Factorials
Distributional Properties SSA & SSE are statistically independent
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Statistical Tests for (Fixed) Main Effects and Interactions : Balanced Complete Factorials
iff a1 = ... = aa = 0
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Testing Factor Effects
Single-Factor Model yij = m + ai + eij i = 1, ..., a; j = 1, ..., r Equivalent Simultaneous Test for Main Effects H0: a1 = a2 = ... = aa vs. Ha: ai aj for some (i,j)
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Test Statistic
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Assignment Verify the ‘Show’ Results
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