Given actual sales data during the last three years (12 quarters, labeled as Periods 1-12), a regression analysis that considers seasonality with an additive form (i.e., defining quarters as dummy variables, Q1, Q2, and Q3) has been conducted to forecast the demand in this year with the following Excel output. Figure 3 What is the demand forecast for quarter 3 of this year?

10109

8152

7385

5658

Intercept Period Q1 Q2 Q3 Coefficients 3841.67 184.38 1913.54 131.25 -948.96 Standard Error 157.64 15.26 148.25 4.27 141.82 t Stat 24.37 -12.08 12.91 7.31 6.69 P-value 0.00 0.00 0.00 0.00 0.00