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TABLE 15-7
a Chemist Employed by a Pharmaceutical Firm Has

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TABLE 15-7
A chemist employed by a pharmaceutical firm has developed a muscle relaxant. She took a sample of 14 people suffering from extreme muscle constriction. She gave each a vial containing a dose (X) of the drug and recorded the time to relief (Y) measured in seconds for each. She fit a "centered" curvilinear model to this data. The results obtained by Microsoft Excel follow, where the dose (X) given has been "centered."
SUMMARY OUTPUT
 TABLE 15-7 A chemist employed by a pharmaceutical firm has developed a muscle relaxant. She took a sample of 14 people suffering from extreme muscle constriction. She gave each a vial containing a dose (X) of the drug and recorded the time to relief (Y) measured in seconds for each. She fit a  centered  curvilinear model to this data. The results obtained by Microsoft Excel follow, where the dose (X) given has been  centered.  SUMMARY OUTPUT     \begin{array}{l} \begin{array} { l r }  \begin{array} { l }   \end{array} \\ \hline\text { Regression  Statistics }\\ \hline \text { Multiple R } & 0.747 \\ \text { RSquare } & 0.558 \\ \text { Adjusted R Square } & 0.478 \\ \text { Standard Error } & 863.1 \\ \text { Observations } & 14 \\ \hline \end{array}\\ \text { ANOVA }\\\\ \begin{array} { l r r r l l }  \hline & d f & { \text { SS } } & \text { MS } & F & \text { Significance } F \\ \hline \text { Regression } & 2 & 10344797 & 5172399 & 6.94 & 0.0110 \\ \text { Residual } & 11 & 8193929 & 744903 & & \\ \text { Total } & 13 & 18538726 & & & \\ \hline \end{array}\\\\ \begin{array} { l c c c c }  \hline & \text { Coeff } & \text { Std Error } & t \text { Stut } & p \text {-value } \\ \hline \text { Intercept } & 1283.0 & 352.0 & 3.65 & 0.0040 \\ \text { CenDose } & 25.228 & 8.631 & 2.92 & 0.0140 \\ \text { CenDoseSq } & 0.8604 & 0.3722 & 2.31 & 0.0410 \\ \hline \end{array} \end{array}  -Referring to Table 15-7, suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose. If she chooses to use a level of significance of 0.01 she would decide that there is a significant curvilinear relationship.  Regression Statistics  Multiple R 0.747 RSquare 0.558 Adjusted R Square 0.478 Standard Error 863.1 Observations 14 ANOVA df SS  MS F Significance F Regression 21034479751723996.940.0110 Residual 118193929744903 Total 1318538726 Coeff  Std Error t Stut p-value  Intercept 1283.0352.03.650.0040 CenDose 25.2288.6312.920.0140 CenDoseSq 0.86040.37222.310.0410\begin{array}{l}\begin{array} { l r } \begin{array} { l } \end{array} \\\hline\text { Regression Statistics }\\\hline \text { Multiple R } & 0.747 \\\text { RSquare } & 0.558 \\\text { Adjusted R Square } & 0.478 \\\text { Standard Error } & 863.1 \\\text { Observations } & 14 \\\hline\end{array}\\\text { ANOVA }\\\\\begin{array} { l r r r l l } \hline & d f & { \text { SS } } & \text { MS } & F & \text { Significance } F \\\hline \text { Regression } & 2 & 10344797 & 5172399 & 6.94 & 0.0110 \\\text { Residual } & 11 & 8193929 & 744903 & & \\\text { Total } & 13 & 18538726 & & & \\\hline\end{array}\\\\\begin{array} { l c c c c } \hline & \text { Coeff } & \text { Std Error } & t \text { Stut } & p \text {-value } \\\hline \text { Intercept } & 1283.0 & 352.0 & 3.65 & 0.0040 \\\text { CenDose } & 25.228 & 8.631 & 2.92 & 0.0140 \\\text { CenDoseSq } & 0.8604 & 0.3722 & 2.31 & 0.0410 \\\hline\end{array}\end{array}
-Referring to Table 15-7, suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose. If she chooses to use a level of significance of 0.01 she would decide that there is a significant curvilinear relationship.


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Horizontal

Parallel to the ground or baseline, often used to describe layout, orientation, or relationships in various contexts.

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