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Saloni Manandhar
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SM
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If events A and B cannot occur at the same time,they are called mutually exclusive.
On Sep 25, 2024
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SM
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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
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R squared =90.8%= 90.8 \% \quad=90.8% R squared (adjusted) =90.6%= 90.6 \%=90.6%
s=3.472s = 3.472s=3.472 with 43−2=4143 - 2 = 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} Source Regression Residual Sum of Squares 4900.55494.199 df 141 Mean Square 4900.5512.0536 F-ratio 407
Variable Coefficient s.e. of Coeff t-ratio prob Constant 40.32661.29531.1 S 0.0001 Time −5.959560.2956−20.2 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} Variable Constant Time Coefficient 40.3266−5.95956 s.e. of Coeff 1.2950.2956 t-ratio 31.1−20.2 prob S 0.0001 S 0.0001
No Selector
R squared =90.8%= 90.8 \% \quad=90.8% R squared (adjusted) =90.6%= 90.6 \%=90.6%
s=3.472s = 3.472s=3.472 with 43−2=4143 - 2 = 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} Source Regression Residual Sum of Squares 4900.55494.199 df 141 Mean Square 4900.5512.0536 F-ratio 407
Variable Coefficient s.e. of Coeff t-ratio prob Constant 40.32661.29531.1 S 0.0001 Time −5.959560.2956−20.2 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} Variable Constant Time Coefficient 40.3266−5.95956 s.e. of Coeff 1.2950.2956 t-ratio 31.1−20.2 prob S 0.0001 S 0.0001
On Sep 22, 2024
Too high; the residuals are generally negative for times between 2 and 5 hours.