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A contrast is defined as the sum of each group mean multiplied by a coefficient for each group (i.e., a signed number, c j). [10] In equation form, = ¯ + ¯ + + ¯ ¯, where L is the weighted sum of group means, the c j coefficients represent the assigned weights of the means (these must sum to 0 for orthogonal contrasts), and ¯ j represents the group means. [8]
The method is based on the theory of orthogonal collocation where the collocation points (i.e., the points at which the optimal control problem is discretized) are the Legendre–Gauss (LG) points. The approach used in the GPM is to use a Lagrange polynomial approximation for the state that includes coefficients for the initial state plus the ...
Example of orthogonal factorial design Orthogonality concerns the forms of comparison (contrasts) that can be legitimately and efficiently carried out. Contrasts can be represented by vectors and sets of orthogonal contrasts are uncorrelated and independently distributed if the data are normal.
The coefficient values and the graphs suggest that the important factors are A, C, and D, and the interaction terms A:C and A:D. The coefficients for A, C, and D are all positive in the ANOVA, which would suggest running the process with all three variables set to the high value.
Orthogonal polynomials with matrices have either coefficients that are matrices or the indeterminate is a matrix. There are two popular examples: either the coefficients { a i } {\displaystyle \{a_{i}\}} are matrices or x {\displaystyle x} :
More generally, statisticians consider linear combinations of parameters, which are estimated via linear combinations of treatment-means in the design of experiments and in the analysis of variance; such linear combinations are called contrasts. Statisticians can use appropriate optimality-criteria for such parameters of interest and for ...
Geometrically, we can see this problem by the following simple case where is a one-dimensional subspace: We want to find the closest approximation to the vector x {\displaystyle x} by a vector x ^ {\displaystyle {\hat {x}}} in the space W {\displaystyle W} .
Dropping (useful) collinear predictors will generally worsen the accuracy of the model and coefficient estimates. Similarly, trying many different models or estimation procedures (e.g. ordinary least squares, ridge regression, etc.) until finding one that can "deal with" the collinearity creates a forking paths problem.