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The Following Excel Printout Provides Information to Estimate Overhead Costs

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The following Excel printout provides information to estimate overhead costs using linear regression:
 Standard  Coefficients  Error t Stat P-value Lower 95 % Upper 95% Intercept 6035.9870271411.054644.2776420.0026962782.08719289.88697 DLH 4.5584826981.6096837312.8319120.0220850.8465438.27042244 # setups 771.102893854.9341831714.036856.44E07644.42436897.781429 # moves 29.94111242.87467534210.415486.26E0623.31209536.5701299\begin{array}{lrrrrrr}&&\text { Standard }\\&\text { Coefficients }&\text { Error }& t \text { Stat }&P \text {-value } &\text {Lower 95 \%}&\text { Upper } 95 \%\\\hline \text { Intercept } & 6035.987027 & 1411.05464 & 4.277642 & 0.002696 & 2782.0871 & 9289.88697 \\\text { DLH } & 4.558482698 & 1.609683731 & 2.831912 & 0.022085 & 0.846543 & 8.27042244 \\\text { \# setups } & 771.1028938 & 54.93418317 & 14.03685 & 6.44 \mathrm{E}-07 & 644.42436 & 897.781429 \\\text { \# moves } & 29.9411124 & 2.874675342 & 10.41548 & 6.26 \mathrm{E}-06 & 23.312095 & 36.5701299\end{array}  Regression Stidistics  Multiple R 0.996584412 R Square 0.99318049 Adjusted R Square 0.990623174 Standard Errar 347.9563597 Observations 12\begin{array} { l r } \quad\quad\quad\quad { \text { Regression Stidistics } } \\\hline \text { Multiple R } & 0.996584412 \\\text { R Square } & 0.99318049 \\\text { Adjusted R Square } & 0.990623174 \\\text { Standard Errar } & 347.9563597 \\\text { Observations } & 12 \\\hline\end{array}
a.Write the multiple regression model (round to nearest cent).
b.What does the 't Stat' measure?
c.What is the estimate of overhead if the department has 1,205 DLH,55 setups,and 125 moves?


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