Q. Rubax, a U.S. manufacturer of athletic shoes, estimates the following linear trend model for show sales: Qt = a + bt + c1D1 + c2D2 + c3D3 Qt = sales of athletic shoes in the tth quarter t = 1, 2,..., 28 [2014(I), 2014(II),....,2020[IV)] D1 = 1 if t is quarter I (winter); 0 otherwise D2 = 1 if t is quarter II (spring); 0 otherwise D3 = 1 if t is quarter III (summer); 0 otherwise The regression analysis produces the following results: a. Is there sufficient statistical evidence of an upward trend in shoe sales? b. Do these data indicate a statistically significant seasonal pattern of sales of Rubax shoes? If so, what is the seasonal pattern exhibited by the data? c. Using the estimated forecast equation, forecast sales of Rubax shoes for 2021(III) and 2022(II). d. How might you improve this forecast equation?
Q. Rubax, a U.S. manufacturer of athletic shoes, estimates the following linear trend model for show sales:
Qt = a + bt + c1D1 + c2D2 + c3D3
Qt = sales of athletic shoes in the tth quarter
t = 1, 2,..., 28 [2014(I), 2014(II),....,2020[IV)]
D1 = 1 if t is quarter I (winter); 0 otherwise
D2 = 1 if t is quarter II (spring); 0 otherwise
D3 = 1 if t is quarter III (summer); 0 otherwise
The regression analysis produces the following results:
a. Is there sufficient statistical evidence of an upward trend in shoe sales?
b. Do these data indicate a statistically significant seasonal pattern of sales of Rubax shoes? If so, what is the seasonal pattern exhibited by the data?
c. Using the estimated
d. How might you improve this forecast equation?
Thank you!

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