Question: SUMMARY OUTPUT Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables

SUMMARY OUTPUT 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. Regression Statistics Multiple R 0.979322 R Square 0.959071 Adjusted R 0.935683 Standard E 0.469295 Observatic 12 ANOVA RESIDUAL OUTPUT

SUMMARY OUTPUT Regression Statistics Multiple R 0.979322 R Square 0.959071 Adjusted R 0.935683 Standard E 0.469295 Residuals Observatic 12 ANOVA df SS MS F gnificance F Regression 4 36.125 9.03125 41.00676 6.04E-05 Residual 7 1.541667 0.220238 0.128472 Total 11 37.66667 Coefficientsandard Err tStat P-value Lower 95%Upper 95% ower 99.09/pper 99.0% Intercept 3.416667 0.428406 7.9753 9.3E-05 2.403647 4.429686 1.917467 4.915866 Qtr1 0.21875 0.402878 0.542968 0.604002 -0.73391 1.171406 -1.19112 1.628616 Qtr2 -2.1875 0.392056 -5.57956 0.000834 -3.11456 -1.26044 -3.55949 -0.81551 Qtr3 -1.59375 0.385417 -4.13514 0.004376 -2.50512 -0.68238 -2.94251 -0.24499 0.40625 0.04148 9.79382 2.45E-05 0.308165 0.504335 0.261091 0.551409 RESIDUAL OUTPUT Observation redicted Y. Residuals 1 4.041667 -0.04167 2 2.041667 -0.04167 3 3.041667 -0.04167 4 5.041667 -0.04167 5 5.666667 0.333333 6 3.666667 -0.66667 7 4.666667 0.333333 8 6.666667 0.333333 9 7.291667 -0.29167 10 5.291667 0.708333 11 6.291667 -0.29167 12 8.291667 -0.29167

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