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SCENARIO 13-10
You Worked as an Intern at We Always

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SCENARIO 13-10
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 and the number of traffic tickets received by the individual.You performed a regression analysis in EXCEL and obtained the following partial information:  Regression Statistics  Multiple R 0.8546 R Square 0.7303 Adjusted R Square 0.6853 Standard Error 226.7502 Observations 15 ANOVA df SS  MS  F  Significance F Regression 2 835284.6500 16.24570.0004 Residual 12616987.8200 Total 2287557.1200 Coefficients  Standard Error t Stat  P-value  Lower 99%  Upper 99%  Intercept 821.2617161.93915.07140.0003326.61241315.9111 Age 1.40612.59880.54110.59849.34446.5321 Tickets 243.440143.24705.62910.0001111.3406375.5396\begin{array}{l}\begin{array} { l r } \hline { \text { Regression Statistics } } \\\hline \text { Multiple R } & 0.8546 \\\text { R Square } & 0.7303 \\\text { Adjusted R Square } & 0.6853 \\\text { Standard Error } & 226.7502 \\\text { Observations } & 15 \\\hline\end{array}\\\\\text { ANOVA }\\\begin{array}{lrrrrrr} & d f & & \text { SS } & \text { MS } & \text { F } & \text { Significance } F \\\hline \text { Regression } & & 2 & & \text { 835284.6500 } & 16.2457 & 0.0004 \\\text { Residual } & & 12 & 616987.8200 & & & \\\text { Total } & & & 2287557.1200 & & & \\\hline\end{array}\\\\\begin{array} { l r r r r r r } \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & \text { P-value } & \text { Lower 99\% } & { \text { Upper 99\% } } \\\hline \text { Intercept } & 821.2617 & 161.9391 & 5.0714 & 0.0003 & 326.6124 & 1315.9111 \\\text { Age } & - 1.4061 & 2.5988 & - 0.5411 & 0.5984 & - 9.3444 & 6.5321 \\\text { Tickets } & 243.4401 & 43.2470 & 5.6291 & 0.0001 & 111.3406 & 375.5396 \\\hline\end{array}\end{array}
-Referring to SCENARIO 13-10, the proportion of the total variability in insurance premiums that can be explained by AGE and TICKETS is .


Definitions:

Control Chart

A tool used in quality control processes to monitor, control, and improve the process quality by plotting data points in time order and identifying any signals of unusual variation.

Type Error

Often refers to a Type I or Type II error in statistical hypothesis testing, misidentification of a true condition.

Null Hypothesis

In statistical hypothesis testing, it is the hypothesis that there is no effect or no difference, and any observed deviation is due to chance.

Control Chart

A graphical tool used in process control to display how a process varies over time, highlighting whether a process is in control or if there are signs of an out-of-control process.

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