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an Operations Manager Was =30.71.84= 30.7 - 1.84

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An operations manager was interested in determining if there is a relationship between the amount of training received by production line workers and the time it takes for them to troubleshoot a process problem. A sample of recently trained line workers was selected. The number of hours of training time received and the time it took (in minutes) for them to troubleshoot their last process problem were captured. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Trouble Shooting =30.71.84= 30.7 - 1.84 Training
 Predictor  Coef  SE Coef  T  P  Constant 30.7291.02330.030.000 Training 1.83600.137613.350.000\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 30.729 & 1.023 & 30.03 & 0.000 \\ \text { Training } & - 1.8360 & 0.1376 & - 13.35 & 0.000 \end{array}
S=1.43588RSq=93.2 % RSq(adj)=92.7 % S = 1.43588 \quad \mathrm { R } - \mathrm { Sq } = 93.2 \text { \% } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 92.7 \text { \% }
Analysis of Variance
 Source  DF  SS  MS  F  P  Regression 1367.20367.20178.100.000 Residual Error 1326.802.06 Total 14394.00\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \\ \text { Residual Error } & 13 & 26.80 & 2.06 & & \\ \text { Total } & 14 & 394.00 & & & \end{array}
 Use the following for questions  An operations manager was interested in determining if there is a relationship between the amount of training received by production line workers and the time it takes for them to troubleshoot a process problem. A sample of recently trained line workers was selected. The number of hours of training time received and the time it took (in minutes) for them to troubleshoot their last process problem were captured. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is Trouble Shooting  = 30.7 - 1.84  Training  \begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 30.729 & 1.023 & 30.03 & 0.000 \\ \text { Training } & - 1.8360 & 0.1376 & - 13.35 & 0.000 \end{array}   S = 1.43588 \quad \mathrm { R } - \mathrm { Sq } = 93.2 \text { \% } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 92.7 \text { \% }  Analysis of Variance  \begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \\ \text { Residual Error } & 13 & 26.80 & 2.06 & & \\ \text { Total } & 14 & 394.00 & & & \end{array}     -From the output, write the equation of the regression equation that can be used to predict troubleshooting time.

-From the output, write the equation of the regression equation that can be used to
predict troubleshooting time.


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Ménière

A disorder of the inner ear that causes episodes of vertigo, tinnitus, hearing loss, and a feeling of fullness or congestion in the ear.

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The information that is transmitted throughout the nervous system via neurons, using electrical and chemical signals.

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The conversion of one form of energy into another form, in psychology, the process by which sensory signals are transformed into neural impulses.

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