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 9 wh

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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 9 Wh 1 (550.54 KiB) Viewed 35 times

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 9 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 = = (₁, 2,..., Tn) 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, 48.89) x1 <-c(10, 12, 8, 11, 9) x2 < c(-1, -4, -2, -5, -7) X <- cbind (1, x1, x2) Check your results with R using R 's built-in functions at the end of the exercise (1m, hatvalues, rstandard, ...). "