3. A chemist studied the concentration of a solution over time. Fifteen identical solutions were prepared. The 15 soluti
Posted: Wed May 11, 2022 6:19 am
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3. A chemist studied the concentration of a solution over time. Fifteen identical solutions were prepared. The 15 solutions were randomly divided into five sets of three, and the five sets were measured, respectively, after 1, 3, 5, 7, and 9 hours. A dataset named “Solution data” is available at the GaochoSpace. In this problem, time is the response variables and concentration is the independent variable. (a) (3 pt) Plot the data and fit a linear regression model. (b) (3 pt) Construct diagnostic plots and comment on your findings. (c) (3 pt) Use the Box-Cox procedure to find an appropriate power transformation. (d) (3 pt) Use the transformation Y = log10Y and obtain the estimated linear regression function for the transformed data. (e) (3 pt) Plot transfermed data and the estimated regression line in (d). Does the regression line appear to be a good fit to the transformed data? (h) (3 pt) Construct diagnostic plots for the fit in (d) and comments on your findings. (i) (3 pt) Express the estimated regression function in (d) in the original units.
0.07 9.0 0.09 9.0 0.08 9.0 0.16 7.0 0.17 7.0 0.21 7.0 0.49 5.0 0.58 5.0 0.53 5.0 1.22 3.0 1.15 3.0 1.07 3.0 2.84 1.0 2.57 1.0 3.10 1.0
show code and outcome thank uuu
3. A chemist studied the concentration of a solution over time. Fifteen identical solutions were prepared. The 15 solutions were randomly divided into five sets of three, and the five sets were measured, respectively, after 1, 3, 5, 7, and 9 hours. A dataset named “Solution data” is available at the GaochoSpace. In this problem, time is the response variables and concentration is the independent variable. (a) (3 pt) Plot the data and fit a linear regression model. (b) (3 pt) Construct diagnostic plots and comment on your findings. (c) (3 pt) Use the Box-Cox procedure to find an appropriate power transformation. (d) (3 pt) Use the transformation Y = log10Y and obtain the estimated linear regression function for the transformed data. (e) (3 pt) Plot transfermed data and the estimated regression line in (d). Does the regression line appear to be a good fit to the transformed data? (h) (3 pt) Construct diagnostic plots for the fit in (d) and comments on your findings. (i) (3 pt) Express the estimated regression function in (d) in the original units.
0.07 9.0 0.09 9.0 0.08 9.0 0.16 7.0 0.17 7.0 0.21 7.0 0.49 5.0 0.58 5.0 0.53 5.0 1.22 3.0 1.15 3.0 1.07 3.0 2.84 1.0 2.57 1.0 3.10 1.0