For the second regression model, write the equation that predicts number of workers as a function

of the inputs to your model. $($e$.$g$.,$Demand $=5438*$Price$)$

D$)$ How many workers does Rebecca need on $5/10/2014$ if there are $1500$ packages to be stowed,

$20000$ items to be stowed, $40000$ items to be picked, and $6$ trucks scheduled?

E$)$ Create a new worksheet called $$Predictions$.$ For each of the $365$ data points, calculate the

predicted value of $$NumWorkers$($Based on your coefficients and model and the input data for

each day of the year$).$ Then, calculate the residuals $($Actual $$ Predicted$)$ for each data point.

$1.$ Make a histogram of the $365$ residuals. What does this look like?

$2.$ What is the standard deviation of the residuals $($STDEV$.$P$())?$ Is this close to your

standard error?

F$)$ Add $$TrucksScheduled$$ back to your second regression model. What are the old and new R$-$squared

values? What are the old and new adj$-$R$-$squared values? Can you explain why adding

TrucksScheduled would have this effect?