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SCENARIO 17-5
You Worked as an Intern at We Always

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SCENARIO 17-5
You worked as an intern at We Always Win Car Insurance Company last summer. You notice that
individual car insurance premiums depend very much on the age of the individual, the number of
traffic tickets received by the individual, and the population density of the city in which the individual
lives. You performed a regression analysis in EXCEL and obtained the following information:  Regression Statistics  Multiple R 0.63 R Square 0.40 Adjusted R Square 0.23 Standard Error 50.00 Observations 15.00 ANOVA df SS  MS  F  Significance F Regression 35994.242.400.12 Residual 1127496.82 Total 45479.54 Coefficients  Standard  Error  t Stat  P-value  Lower 99.0%  Upper 99.0%  Intercept 123.8048.712.540.0327.47275.07 AGE 0.820.870.950.363.511.87 TICKETS 21.2510.661.990.0711.8654.37 DENSITY 3.146.460.490.6423.1916.91\begin{array}{l}\begin{array}{lr}\hline {\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.63 \\\text { R Square } & 0.40 \\\text { Adjusted R Square } & 0.23 \\\text { Standard Error } & 50.00 \\\text { Observations } & 15.00 \\\hline\end{array}\\\\\text { ANOVA }\\\begin{array}{lrcccr}\hline & d f &{\text { SS }} & \text { MS } & \text { F }&{\text { Significance } F} \\\hline \text { Regression } & 3 & & 5994.24 & 2.40 & 0.12 \\\text { Residual } & 11 & 27496.82 & & & \\\text { Total } & & 45479.54 & & & \\\hline\end{array}\\\\\begin{array}{lrrrrrr}\hline & \text { Coefficients } & \begin{array}{c}\text { Standard } \\\text { Error }\end{array} & \text { t Stat } & \text { P-value } & \text { Lower 99.0\% } & \text { Upper 99.0\% } \\\hline \text { Intercept } & 123.80 & 48.71 & 2.54 & 0.03 & -27.47 & 275.07 \\\text { AGE } & -0.82 & 0.87 & -0.95 & 0.36 & -3.51 & 1.87 \\\text { TICKETS } & 21.25 & 10.66 & 1.99 & 0.07 & -11.86 & 54.37 \\\text { DENSITY } & -3.14 & 6.46 & -0.49 & 0.64 & -23.19 & 16.91 \\\hline\end{array}\end{array}
AGE -0.82 0.87 -0.95 0.36 -3.51 1.87
TICKETS 21.25 10.66 1.99 0.07 -11.86 54.37
DENSITY -3.14 6.46 -0.49 0.64 -23.19 16.91
-Referring to Scenario 17-5, the standard error of the estimate is _________.


Definitions:

Generalization Gradients

In learning theory, the range and degree to which a learned response is produced to stimuli that vary from the original conditioned stimulus.

Discrimination Training

The process in behavioral psychology of training an organism to respond differently to distinct stimuli, enhancing its ability to distinguish between them.

Inhibitory Generalization

The process by which an organism learns to inhibit responses to stimuli that are similar to a conditioned stimulus that signals an aversive event.

Training Stimulus

A specific condition or event presented during training to elicit and reinforce desired behaviors or responses.

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