Question

Exhih a ctual sales data during the last three years (12 quarters, labeled as Periods 1-12), a Exhibit3 regression ssion analysis that considers seasonality with an additive form (i.e., defining quarters as y variables, Q1, Q2, and Q3) has been conducted to forecast the demand in this year with the following Excel output. Standard Coefficients Error tStat P-value 24.37 0.00 Intercept Period 3841.67 157.64 15.26 184.38 -12.08 0.00 01 02 1913.54 148.25 12.91 0.00 131.25 4.27 7.31 0.00 -948.96 141.82 6.69 0.00 quarter 2 of this year? 18. Refer to Exhibit 3. What is the demand forecast for А. 10109 B. 8152 C. 7385 D. 6554 19. Refer to Exhibit 3. What is the demand forecast for quarter 4 of this year? A. 6792 B. 2328 C. 3023 D. 5763

Answer 1

A) The dummy variables act as a binary variable and added to the intercept when we take any regression equation particular to some data label.

For example, when we are calculating the forecast for this year's Quarter 2 we should take only the Q2 dummy variable data and add to the intercept(all other dummy variables are treated as 0) and the slope remains the same(period). As the last three years period data labeled as 1-12 this years quarter 2 will be period 14

So the forecast will be = (3841.67+131.25)+14*184.38= 6554

The answer is option D

B) For quarter 4 there is no dummy variable so the forecast will be

=3841.67+(14*184.38)= 6792

The answer is option A

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