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The file Corruption.xls contains country-level data on two variables. One of these is a measure of perceptions of political corruption. (This data is assembled by the organization Transparency.org.) [NOTE: I have modified the original corruption measure so that higher values mean higher corruption. The least corruption possible has a measure of 0, the highest a measure of 10.] The second variable is a measure of GDP per capita. a) (10) Before looking at any data, propose a simple economic story (model) for how these two variables might be related. Write a paragraph of no less than 50 words in which you explain your argument. Which is the independent, and which is the dependent variable in your model? (Plausible stories could be told in which either variable could be the independent (or dependent) variable.) b) (10) Whichever way you've chosen to model this problem, execute a linear regression. For one time in your life, you are going to be calculating the slope and intercept estimators in EXCEL (or Google Spreadsheet) using the "by hand" method that was demonstrated in the lectures. DO NOT USE THE BUILT IN EXCEL REGRESSION/VARIANCE/COVARIANCE FEATURES (except to check your work, if you desire). Report your Betahats. c) (10) What percentage of the variation of your dependent variable is explained by the independent? d) (10) Also "by hand," calculate the variance of B. Next, calculate the test statistic and p- value for a hypothesis test that the population slope is zero. (The p-value is going to be extremely small and not be on any t-table. Figure out how you can use Excel to calculate it. (Look up the t.dist functionality in the help menus.)