Question: You are presented with two alternative estimates for a linear trend model with seasonality. The dependent variable is revenue for a very successful software startup

You are presented with two alternative estimates for a linear trend model with seasonality. The dependent variable is revenue for a very successful software startup (thousands of dollars) and independent variables include a trend variable and seasonal dummy variables. Regression Statistics Multiple R 0.991277 R Square 0.98263 Adjusted F 0.934017 Standard E 4831.617 Observation ANOVA SS MS ignificance F Regression 5 2.91E+10 5.81E+09 248.9031 6.04E-18 Residual 22 5.14E+08 23344519 Total 27 2.96E+10 Coefficientstandard Err t P-value Lower 95% Upper 95% ower 95.09/pper 95.0% Intercept 0 #N/A #NIA #N/A #N/A #N/A #N/A #N/A Quarter 1057 958 119.8946 8.824072 1.12E-08 809.312 1306.604 809.312 1306.604 91 16455.26 2400.887 6.853826 6.95E-07 11476.12 21434.39 11476.12 21434.39 92 14930.3 2480.398 6.019317 4.66E-06 9786.27 20074.33 9786.27 20074.33 93 13430.77 2563.053 5.240146 2.95E-05 8115.325 18746.22 8115.325 18746.22 23147 92 2590.018 8.937357 8.95E-09 17776.55 28519.29 17776.55 28519.29 SUMMARY OUTPUT Regression Statistics Multiple R 0.89783 R Square 0.806098 Adjusted F 0.770844 Standard E 4831.617 Observation 27 ANOVA dif SS MS gnificance F Regression 4 2. 14E+09 5.34E+08 22.8649 1.44E-07 Residual 22 5.14E+08 23344519 Total 26 2.65E+09 Coefficientstandard Err t Stat P-value Lower 95% Upper 95% ower 95.09/pper 95.0% Intercept 23147.92 2590.018 8.937357 8.95E-09 17776.55 28519.29 17776.55 28519.29 Quarter 1057 958 119.8946 8.824072 1.12E-08 809.312 1306.604 809.312 1306.604 q1 6692.66 2690.736 6 -2.4873 0.020939 -12272.9 -1112.42 -12272.9 -1112.42 92 -8217.62 2688.063 -3.05708 0.005776 -13792.3 -2 -2642.92 -13792.3 -2642.92 03 9717.15 2690.736 -3.61134 0.001549 -15297.4 -4136.9 -15297.4 -4136.9 How many observations were in the data set? Question 14 1 pts What are the model degrees of freedom for the regressions shown above

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