For the data of Table 12 4 1, we could also use the nonparametric bootstrap to assess the standard error from the LAD and M estimator fit (a) Fit the line y Icirc plusmn Icirc sup2 x to get estimates and (b) Generate new residuals by resampling from the fitted residuals and re estimate Icirc plusmn and Icirc sup2 (c) Do part (c) B times and calculate the standard deviation of and Table 12 4 1 Values of CO2 and 0 in the pouches of 23 potoroos (McPherson 1990) Anim 7 Os T 102 T55 104 16 1 7 19 2 4 18 CO2 1 1 2 1 1 1 4 2 3 1 7 1 7 2 4 Animal 18 2 186 1912 18 2 T8TT14, 18 TTA O2 2 2 1 2 1 1 2 2 3 1 9 2 4 2 6 2 9 Animal 02 16 5 17 2 17 3 17 8 17 3 18 4 16 9 CO2 4 0 3 3 3 0 3 4 2 9 1 9 3 9 18 6 19 2 18 7 1 17 18 19 20 21 2 23

Question: For the data of Table 12.4.1, we could also use the nonparametric bootstrap to assess the standard error from the LAD and M-estimator fit. (a)

For the data of Table 12.4.1, we could also use the nonparametric bootstrap to assess the standard error from the LAD and M-estimator fit.
(a) Fit the line y = Î± + Î²x to get estimates and .
(b) Generate new residuals by resampling from the fitted residuals and re-estimate Î± and Î².
(c) Do part (c) B times and calculate the standard deviation of and .

Table 12.4.1. Values of CO2 and 0 in the pouches of 23 potoroos (McPherson 1990) Anim 7 %Os T-102 T55-104-16 -1.7-19 -2.4 - 18. %CO2 1 1.2 1.1 1.4 2.3 1.7 1.7 2.4 Animal 18.2 186 1912 18.2-T8TT14, 18-TTA %O2 %2 2.1 2.1 1.2 2.3 1.9 2.4 2.6 2.9 Animal %02 16.5 17.2 17.3 17.8 17.3 18.4 16.9 %CO2 4.0 3.3 3.0 3.4 2.9 1.9 3.9 18.6 19.2 18.7 1 17 18 19 20 21 2 23

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