Many countries, especially those in Europe, have significant gold holdings. But many of these countries also have massive debts. The following day to show the total value of gold holdings in billions of US dollars and the dirt as a percentage of the gross domestic product for nine countries (word press and treating economics websites, February 24, 2012). Click on the data file logo to reference the data.

Many countries, especially those in Europe, have significant gold holdings. But many of these countries also have massive debts. The following data show the total value of gold holdings in billions of U.S. dollars and the debt as a percentage of the gross domestic product for nine countries (WordPress and Trading Economics websites, February 24, 2012). Click on the datafile logo to reference the data. DATA file Gold Value Debt Country ($ billions) (% of GDP) China 63 17.7 France 146 81.7 Germany 203 83.2 Indonesia 33 69 2 Italy 147 119.0 Netherlands 36 63.7 Russia 50 9.9 Switzerland 62 55.0 United States 487 93.2 A b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? Positive relationship Does there appear to be any outliers and/or influential observations? Observation 9 (U.S.) appears to be an observation with |high leverage and may be very influential in terms of fitting a linear model to the data. c. Using the entire data set, develop the estimated regression equation that can be used to predict the debt of a country given the total value of its gold holdings. y = * (to 4 decimals)+ (to 4 decimals) Gold Value d. Suppose that after looking at the scatter diagram in part (a) that you were able to visually identify what appears to be an influential observation. Drop this observation from the data set and fit an estimated regression equation to the remaining data. (to 4 decimals)+ (to 4 decimals) Gold Value Compare the estimated slope for the new estimated regression equation to the estimated slope obtained in part (c). Does this approach confirm the conclusion you reached in part (d)? The slope of the estimated regression equation is now as compared to a value of when this observation is included. Thus, we see that this observation has a big impact on the value of the slope of the fitted line and hence we would say that it is an influential observation