Xt yt t Xt yt 1 5.00 0.318 10 6.16 0.650 5.06 0.330 11 6.22 0.655...

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Xt yt t Xt yt 1 5.00 0.318 10 6.16 0.650 5.06 0.330 11 6.22 0.655 3 5.12 0.356 12 6.31 0.713 5.10 5.35 4 0.334 13 6.38 0.724 0.386 14 6.54 0.775 5.57 0.455 15 6.68 0.78 7 5.61 0.460 16 6.73 0.796 8 5.80 0.527 17 6.89 0.859 9. 6.04 0.598 18 6.97 0.88 Do the following. Fit a simple linear regression model to these data. 2. 1. Plot the residuals against time. State whether there is any indication of autocorrelation based on the plot. Use the Durbin-Watson test to determine if there is positive autocorrelation in the errors. 3. 4. Xt yt t Xt yt 1 5.00 0.318 10 6.16 0.650 5.06 0.330 11 6.22 0.655 3 5.12 0.356 12 6.31 0.713 5.10 5.35 4 0.334 13 6.38 0.724 0.386 14 6.54 0.775 5.57 0.455 15 6.68 0.78 7 5.61 0.460 16 6.73 0.796 8 5.80 0.527 17 6.89 0.859 9. 6.04 0.598 18 6.97 0.88 Do the following. Fit a simple linear regression model to these data. 2. 1. Plot the residuals against time. State whether there is any indication of autocorrelation based on the plot. Use the Durbin-Watson test to determine if there is positive autocorrelation in the errors. 3. 4.

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call 1mformula t xt Yt data Cosmeticsales Residuals in Median 106979 0 32516 002493 03... View the full answer