This exercise again draws the data set "grandfather clocks," available on Canvas and within R, under the help menu, sample data sets, in the Business and Demographic category. 1. In Excel or R, estimate a regression equation between price and age. Let price be the dependent variable, and age the independent variable. 2. Fully interpret the slope coefficient of the regression- what exactly does it tell you? 3. What is the value of the total sum of squares (SST) of the regression? Interpret this number: what does it tell you? 4. What is the value of the sum of squared errors (SSE) of the regression? Interpret this number: what does it tell you? 5. Calculate the standard error of the estimate, the sy.x, from the SSE of the regression and interpret this value. 6. What is the value of the sum of squares of the regression (SSR)? Interpret this number: what does it tell you? 7. Calculate the coefficient of determination, r-squared, from the SSR and SST. 8. Fully interpret the r-squared value: what exactly does it tell you? 9. Calculate the correlation coefficient, r, from the coefficient of determination. 10. The first observation in the data set is a 127 year-old clock, with a price of $1235. What is the predicted price of this clock? What is the residual for this observation (the amount by which the actual price differs from the predicted price)? 11. Use the regression equation to estimate the price of a 150 year-old clock. 12. Use the regression equation to estimate the change in price associated with 10 additional years of