You have a dataset of observations given by 58.42) 1 10 | 48.41 1 12 -4...
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You have a dataset of observations given by 58.42) 1 10 | 48.41 1 12 -4 y = 70.02 and X = = 1 8 -2 31.36 1 11 -5 48.89 1 -7 where y is the response variable and X the design matrix. Given these values, calculate the residual variance, the leverages for each observation, and the vector of standardized residuals r (r1, r2,..., rn) %3D without using R's built-in commands (you might use R as a calculator, though). The variance of the standardized residuals is expected to be close to 1. Is that the case here? Hint: Compute Var(r). R code: y <- c(58.42, 48.41, 70.02, 31.36, х1 <- с (10, 12, 8, 11, 9) х2 <- с(-1, -4, -2, -5, -7) X <- cbind (1, x1, x2) 48.89) Check your results with R using R 's built-in functions at the end of the exercise (1m, hatvalues, rstandard, ...). You have a dataset of observations given by 58.42) 1 10 | 48.41 1 12 -4 y = 70.02 and X = = 1 8 -2 31.36 1 11 -5 48.89 1 -7 where y is the response variable and X the design matrix. Given these values, calculate the residual variance, the leverages for each observation, and the vector of standardized residuals r (r1, r2,..., rn) %3D without using R's built-in commands (you might use R as a calculator, though). The variance of the standardized residuals is expected to be close to 1. Is that the case here? Hint: Compute Var(r). R code: y <- c(58.42, 48.41, 70.02, 31.36, х1 <- с (10, 12, 8, 11, 9) х2 <- с(-1, -4, -2, -5, -7) X <- cbind (1, x1, x2) 48.89) Check your results with R using R 's built-in functions at the end of the exercise (1m, hatvalues, rstandard, ...).
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Related Book For
Probability & Statistics for Engineers & Scientists
ISBN: 978-0130415295
7th Edition
Authors: Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying
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