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Doctors Studying How the Human Body Assimilates Medication Inject Some =90.8%= 90.8 \% \quad

Question 57

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Doctors studying how the human body assimilates medication inject some patients with penicillin,and then monitor the concentration of the drug (in units/cc)in the patients' blood for seven hours.First they tried to fit a linear model.The regression analysis and residuals plot are shown.Is that estimate likely to be accurate,too low,or too high? Explain. Dependent variable is:                  Concentration
No Selector
R squared =90.8%= 90.8 \% \quad R squared (adjusted) =90.6%= 90.6 \%
s=3.472s = 3.472 with 432=4143 - 2 = 41 degrees of freedom
 Source  Sum of Squares  df  Mean Square  F-ratio  Regression 4900.5514900.55407 Residual 494.1994112.0536\begin{array} { l l r r r } \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4900.55 & 1 & 4900.55 & 407 \\ \text { Residual } & 494.199 & 41 & 12.0536 & \end{array}

 Variable  Coefficient  s.e. of Coeff  t-ratio  prob  Constant 40.32661.29531.1 S 0.0001 Time 5.959560.295620.2 S 0.0001\begin{array} { l l l r l } \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 40.3266 & 1.295 & 31.1 & \text { S } 0.0001 \\ \text { Time } & - 5.95956 & 0.2956 & - 20.2 & \text { S } 0.0001 \end{array}  Doctors studying how the human body assimilates medication inject some patients with penicillin,and then monitor the concentration of the drug (in units/cc)in the patients' blood for seven hours.First they tried to fit a linear model.The regression analysis and residuals plot are shown.Is that estimate likely to be accurate,too low,or too high? Explain. Dependent variable is:                  Concentration No Selector R squared  = 90.8 \% \quad  R squared (adjusted)  = 90.6 \%   s = 3.472  with  43 - 2 = 41  degrees of freedom  \begin{array} { l l r r r } \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4900.55 & 1 & 4900.55 & 407 \\ \text { Residual } & 494.199 & 41 & 12.0536 & \end{array}    \begin{array} { l l l r l } \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 40.3266 & 1.295 & 31.1 & \text { S } 0.0001 \\ \text { Time } & - 5.95956 & 0.2956 & - 20.2 & \text { S } 0.0001 \end{array}

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Too high; the residu...

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