(b) Use a multiple regression model with dummy variables as follows to develop a
ID: 3264319 • Letter: #
Question
(b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 1 if Quarter 3, 0 otherwise If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a sign before the blank. (Example: -300) Value = Qtrl + Qtr2:+ Qtr3 (c) Compute the quarterly forecasts for next year based on the model you developed in part (b) If required, round your answers to three decimal places. Quarter 1 forecast Quarter 2 forecast Quarter 3 forecast Quarter 4 forecast 6.333 4.333 4.667 6.667 (d) Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for Quarter 1 in Year 1, t = 2 for Quarter 2 in Year 1, t = 12 for Quarter 4 in Year 3 If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a sign before the blank. (Example: -300) Value =Explanation / Answer
only quarter
y = 6.6666 -Q1 -3Q2 -2Q3
c)
forecast
d) quarte + time
y = 3.416666 + 0.40625*t +0.21875 *Q1 -2.1875*Q2 -1.59375 *Q3
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SUMMARY OUTPUT Regression Statistics Multiple R 0.631054743 R Square 0.398230088 Adjusted R Square 0.172566372 Standard Error 1.683250823 Observations 12 ANOVA df SS MS F Regression 3 15 5 1.764706 Residual 8 22.66666667 2.833333 Total 11 37.66666667 Coefficients Standard Error t Stat P-value Intercept 6.666666667 0.971825316 6.859943 0.00013 Q1 -1 1.374368542 -0.72761 0.4876 Q2 -3 1.374368542 -2.18282 0.060595 Q3 -2 1.374368542 -1.45521 0.183698Related Questions
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