The Triberg Clock Company of Schonachbach, Germany, holds an annual antique clock auction. Data on the...

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The Triberg Clock Company of Schonachbach, Germany, holds an annual antique clock auction. Data on the age of the clock (years), the number of bidders (persons), and the price of the winning bid and, starting price at an action (Euro), were used to find out whether or not the final price depends on starting price. The results are given in the following table, in which some results are missing |. reg in price in_age number_of_bidders start_price Source | SS Model | 2.76280816 Residual | Total 2.81434375 in_age | .313786 number_of_bidders | .0154789 start_price | df _cons | 4.647861 in price | Coefficient Std. err. 920936053 .001840557 MS 31 090785282 0057665 .5971436 .5537673 Number of obs F (3, 28) Prob > F R-squared t p>|t| = 2.98 0.009 2.68 0.012 0.26 0.830 8.39 0.000 = Adj R-squared = Root MSE = 500.36 0.0000 0742045 0036668 -.982119 3.51352 0.9797 . 0429 [95% conf. interval] 5533675 .027291 1.072848 5.782202 a. Fill the gaps (pick any 6 gaps). Show your calculations. b. Interpret the R-square, state and do a relevant statistical hypothesis test. c. Interpret the coefficients. Do they seem to make sense? d. What are the dimensions (measures, units) of the coefficients? e. Which coefficients are statistically significant? What does that mean? State and test appropriate hypothesis f. Describe the hypothesis of primary interest for this study. What is your conclusion? g. What would be an estimated selling price of the antique Longines watches produced in 1920, if you decided to set a starting price 5K, and you expect at least 20 participants at the coming action. The Triberg Clock Company of Schonachbach, Germany, holds an annual antique clock auction. Data on the age of the clock (years), the number of bidders (persons), and the price of the winning bid and, starting price at an action (Euro), were used to find out whether or not the final price depends on starting price. The results are given in the following table, in which some results are missing |. reg in price in_age number_of_bidders start_price Source | SS Model | 2.76280816 Residual | Total 2.81434375 in_age | .313786 number_of_bidders | .0154789 start_price | df _cons | 4.647861 in price | Coefficient Std. err. 920936053 .001840557 MS 31 090785282 0057665 .5971436 .5537673 Number of obs F (3, 28) Prob > F R-squared t p>|t| = 2.98 0.009 2.68 0.012 0.26 0.830 8.39 0.000 = Adj R-squared = Root MSE = 500.36 0.0000 0742045 0036668 -.982119 3.51352 0.9797 . 0429 [95% conf. interval] 5533675 .027291 1.072848 5.782202 a. Fill the gaps (pick any 6 gaps). Show your calculations. b. Interpret the R-square, state and do a relevant statistical hypothesis test. c. Interpret the coefficients. Do they seem to make sense? d. What are the dimensions (measures, units) of the coefficients? e. Which coefficients are statistically significant? What does that mean? State and test appropriate hypothesis f. Describe the hypothesis of primary interest for this study. What is your conclusion? g. What would be an estimated selling price of the antique Longines watches produced in 1920, if you decided to set a starting price 5K, and you expect at least 20 participants at the coming action.

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From the regression output provided we need to fill in six gaps with missing values Lets find those gaps and fill them in based on the provided information a Starting with the gaps 1 Degrees of freedo... View the full answer