Examlex

Solved

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

question 37

Essay

Penicillin 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. The data are shown in the scatterplot. First they tried to fit
a linear model. The regression analysis and residuals plot are shown. 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}{llrrr}\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 40.32661.29531.10.0001 Time 5.959560.295620.20.0001\begin{array}{lllrl}\text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\\text { Constant } & 40.3266 & 1.295 & 31.1 & \leq 0.0001 \\\text { Time } & -5.95956 & 0.2956 & -20.2 & \leq 0.0001\end{array}


 Penicillin 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. The data are shown in the scatterplot. First they tried to fit a linear model. The regression analysis and residuals plot are shown. 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}{llrrr} \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}{lllrl} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 40.3266 & 1.295 & 31.1 & \leq 0.0001 \\ \text { Time } & -5.95956 & 0.2956 & -20.2 & \leq 0.0001 \end{array}        a. Find the correlation between time and concentration. b. Using this model, estimate what the concentration of penicillin will be after 4 hours. c. Is that estimate likely to be accurate, too low, or too high? Explain. Now the researchers try a new model, using the re-expression log(Concentration). Examine the regression analysis and the residuals plot below. Dependent variable is: LogCnn No Selector R squared  = 98.0 \% \quad  R squared (adjusted)  = 98.0 \%   s = 0.0451  with  43 - 2 = 41  degrees of freedom    \begin{array}{llrrr} \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4.11395 & 1 & 4.11395 & 2022 \\ \text { Residual } & 0.083412 & 41 & 0.002034 & \end{array}    \begin{array}{llllc} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 1.80184 & 0.0168 & 107 & \leq 0.0001 \\ \text { Time } & -0.172672 & 0.0038 & -45.0 & \leq 0.0001 \end{array}       d. Explain why you think this model is better than the original linear model. e. Using this new model, estimate the concentration of penicillin after 4 hours.

a. Find the correlation between time and concentration.
b. Using this model, estimate what the concentration of penicillin will be after 4 hours.
c. Is that estimate likely to be accurate, too low, or too high? Explain.
Now the researchers try a new model, using the re-expression log(Concentration). Examine
the regression analysis and the residuals plot below. Dependent variable is: LogCnn
No Selector
R squared =98.0%= 98.0 \% \quad R squared (adjusted) =98.0%= 98.0 \%
s=0.0451s = 0.0451 with 432=4143 - 2 = 41 degrees of freedom


 Source  Sum of Squares  df  Mean Square  F-ratio  Regression 4.1139514.113952022 Residual 0.083412410.002034\begin{array}{llrrr}\text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\\text { Regression } & 4.11395 & 1 & 4.11395 & 2022 \\\text { Residual } & 0.083412 & 41 & 0.002034 &\end{array}

 Variable  Coefficient  s.e. of Coeff  t-ratio  prob  Constant 1.801840.01681070.0001 Time 0.1726720.003845.00.0001\begin{array}{llllc}\text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\\text { Constant } & 1.80184 & 0.0168 & 107 & \leq 0.0001 \\\text { Time } & -0.172672 & 0.0038 & -45.0 & \leq 0.0001\end{array}
 Penicillin 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. The data are shown in the scatterplot. First they tried to fit a linear model. The regression analysis and residuals plot are shown. 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}{llrrr} \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}{lllrl} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 40.3266 & 1.295 & 31.1 & \leq 0.0001 \\ \text { Time } & -5.95956 & 0.2956 & -20.2 & \leq 0.0001 \end{array}        a. Find the correlation between time and concentration. b. Using this model, estimate what the concentration of penicillin will be after 4 hours. c. Is that estimate likely to be accurate, too low, or too high? Explain. Now the researchers try a new model, using the re-expression log(Concentration). Examine the regression analysis and the residuals plot below. Dependent variable is: LogCnn No Selector R squared  = 98.0 \% \quad  R squared (adjusted)  = 98.0 \%   s = 0.0451  with  43 - 2 = 41  degrees of freedom    \begin{array}{llrrr} \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4.11395 & 1 & 4.11395 & 2022 \\ \text { Residual } & 0.083412 & 41 & 0.002034 & \end{array}    \begin{array}{llllc} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 1.80184 & 0.0168 & 107 & \leq 0.0001 \\ \text { Time } & -0.172672 & 0.0038 & -45.0 & \leq 0.0001 \end{array}       d. Explain why you think this model is better than the original linear model. e. Using this new model, estimate the concentration of penicillin after 4 hours.
d. Explain why you think this model is better than the original linear model.
e. Using this new model, estimate the concentration of penicillin after 4 hours.


Definitions:

Related Questions