$\backslash$table$[[$Time$],[243],[199],[240],[125],[151],[158],[66],[350],[1141],[251],[385],[239],[139],[181],[111],[136],[250],[313],[154],[78],[264],[123],[314],[135],[99],[420],[112],[239],[208],[65],[133],[213],[229],[154],[377],[69],[170],[261],[230],[273],[288],[180],[296],[235],[243],[167],[227],[384],[331]]$

The file Call Center Call Duration.xlsx contains the monthly call volume for an existing product. Source: Data extracted from S$.$ Madadevan and J$.$ Overstreet, "Use of Warranty and Reliability Data to Inform Call Center Staffing," Quality Engineering $24(2012)$: $386-399.$

Construct the time$-$series plot.

Describe the monthly pattern in the data.

In general, would you say that the overall call volume is increasing or decreasing? Explain.

Develop an exponential trend forecasting equation with monthly components.

Interpret the monthly compound growth rate.

Interpret the January multiplier.

What is the predicted call volume for month $60?$

What is the predicted call volume for month $61?$

How can this type of time$-$series forecasting benefit the call center