Coffee Exports Country Venezuela Sri_Lanka Nigeria Philippines Togo Paraguay Sierra_Leone Ghana Jamaica Angola Zimbabwe Zambia Dominican_Republic Rwanda Bolivia Haiti Guinea Malawi Central_African_Republic Cameroon Panama Cuba Congo Thailand Madagascar Burundi Papua_New_Guinea Ecuador Nicaragua Ethiopia Kenya Tanzania El_Salvador Costa_Rica India Peru Cote_d.Ivoire Honduras Uganda Indonesia Mexico Guatemala Dec Exports Total Exports 2000 2001 0.00 180.30 0.00 1.83 0.13 3.39 0.30 3.01 0.76 193.05 1.00 11.00 1.09 26.31 1.43 34.16 2.09 29.47 2.45 17.25 3.15 93.64 4.26 82.49 4.58 106.44 5.37 294.60 7.00 124.63 8.00 73.00 8.34 47.68 9.27 63.71 9.88 109.23 10.68 1169.71 15.87 56.93 20.10 99.53 23.37 199.33 29.77 1167.08 32.55 294.83 41.06 303.18 58.66 1079.98 70.49 754.29 72.83 1364.59 77.66 1390.99 97.65 982.44 102.36 866.16 118.90 1521.64 128.99 2054.66 140.78 3964.27 152.49 2396.00 175.98 3521.39 176.98 2391.61 232.43 3053.30 260.72 4439.59 318.31 3408.33 326.98 4110.38 Title: Demand Deposits at Commercial Banks Series ID: DEMDEPNS Source: Board of Governors of the Federal Reserve System Release: H.6 Money Stock Measures Seasonal Adjustment: Not Seasonally Adjusted Frequency: Monthly Units: Billions of Dollars Date Range: 1959-01-01 to 2004-03-01 Last Updated: 2004-04-23 9:05 AM CT Notes: Further information and definitions are available at: http://research.stlouisfed.org/publications/mt/. DATE 2002-01-01 2002-02-01 2002-03-01 2002-04-01 2002-05-01 2002-06-01 2002-07-01 2002-08-01 2002-09-01 2002-10-01 2002-11-01 2002-12-01 2003-01-01 2003-02-01 2003-03-01 2003-04-01 2003-05-01 2003-06-01 2003-07-01 2003-08-01 2003-09-01 2003-10-01 2003-11-01 2003-12-01 2004-01-01 2004-02-01 2004-03-01 Exponential Smoothing VALUE FORECAST ERROR 329.650 316.711 322.456 309.728 300.399 302.373 303.895 287.374 289.964 295.307 296.634 317.491 300.486 297.578 302.966 307.689 307.830 318.605 319.702 321.447 312.363 309.909 310.165 327.459 301.709 310.833 328.481 Excel Assignment 3 There are three data sets for you to analyze. Data Set 1 (Download Excel File \"Coffee Exports\") Data set 1 has coffee exports (thousands of 60-kilo bags) for December 2000 and total exports for 2001 for 42 coffee exporting countries excluding the three largest exporters (Brazil, Colombia, and Vietnam). A scatterplot is shown below. Commodity traders would like to use December export figures from a particular country to predict total exports from that country in the coming year. a) Use the data to come up with an equation for predicting total 2001 exports from December 2000 exports. b) Assume that the equation you found in part \"a\" will apply to any year. If the coffee export of a country in December is 100, within what range would we expect to find the total exports for that country to fall in the coming year. next page Data Set 2 (Download Excel File \"Salaries\") Data set 2 has gender, job class (1 through 8), and salary of 382 employees of a certain business. Below is a plot of average salaries for the males and females for the various job classifications. The plot suggest two things: (1) there appears to be a curved relationship between job class and salary, and (2) there appears to be differences between average salaries of males and females particularly at the upper job class levels. a) Fit a multiple regression model to the data to predict salary Y from the gender indicator variable X1, (1 = male, 0 = female), job class X2, and the square of the job class X3 = (X2)2. You will have to create X1 and X3 yourself. b) Based on your analysis, do you think gender has a significant effect on salary? What in the output tells you this? c) Based on you analysis, do you think that the quadratic term ought to be included in the equation? What in the analysis tells you this? next page Data Set 3 (Download Excel File \"Demand Deposits\") Data set 3 has demand deposits at commercial banks from Jan 2002 through Mar 2004. The plot is shown below. a. Compute the exponentially smoothed forecast and the forecast errors. Use the weight w = .5. b. Plot the data and the forecast on the same plot. c. Comment on how well the exponentially smoothed forecast actually forecasts the demand deposits. Gender F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F Job Class 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Salary 32000 34300 34300 28800 28400 33700 30800 30500 31300 26500 32400 28100 34900 29400 35000 29900 31500 31800 27500 27700 24400 36400 37800 33700 34300 39700 35200 34200 38800 37900 38400 36600 29000 34600 34000 33900 38400 33100 34300 33200 26000 30400 34900 27500 35400 35200 36500 39800 31500 33200 36500 35800 30100 28700 36500 34300 X1 X2 X3 Gender Indicator Job Class Job Class squared F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 39700 34400 39100 37600 36500 35400 41100 36400 39800 42900 39800 39300 32200 36400 38100 39300 47000 38700 36500 40200 40100 45800 28900 42000 41300 36700 39800 36100 43600 45900 46600 46300 42400 39700 40900 34200 53600 41500 37500 33200 43300 44200 44600 41300 37100 41200 32500 42000 40100 39300 36500 41400 37300 46400 40700 46000 38100 33400 F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 8 8 8 8 8 41500 32800 35300 65000 43000 54500 43600 45300 48500 38600 49700 51400 49900 47500 45300 48500 45300 48600 50800 44800 45300 47600 39700 47500 41100 45200 48000 42200 47500 40400 43600 50800 38700 50800 44200 61800 50700 53200 49700 48600 61300 62800 62900 54000 41100 65000 56300 53100 61200 66100 59300 59600 59400 78200 78300 78300 72800 62900 F M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 94800 36000 28700 28300 34100 28700 30100 28500 27700 30600 29000 32100 29900 31300 31600 32100 24300 39000 37400 37500 40200 35800 36900 38100 40500 37600 33300 40000 38700 34300 31500 32200 34600 31700 36500 34300 37300 29800 34300 38200 32400 38600 30900 33900 40000 36000 35400 37500 35700 44500 37600 35500 39500 42100 43000 36300 38700 35600 M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 35600 39700 43100 40900 43100 38600 48400 42600 37600 36500 28700 38700 39800 43100 28800 39700 37500 35700 39800 32100 39600 45200 49600 43300 49600 40200 39200 50600 49200 45200 45200 35600 38500 44700 38600 49600 38300 36500 36400 37300 56300 33900 43600 40900 42800 44900 33900 39000 39800 37500 54600 40600 32600 32600 43300 50800 47300 34300 M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 56800 50800 47400 49200 47500 48600 45300 50100 43000 39800 44400 44400 47500 48600 47500 45300 45200 52900 48600 45300 51900 48600 45300 49700 48600 48600 46300 51900 50200 45800 48000 48500 45300 43100 50000 52900 38700 50700 44800 46100 49900 48000 50800 38800 40700 51900 45400 43600 46300 44400 58200 50800 78300 52400 67200 60700 64000 58500 M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 56300 65600 62300 56800 56300 56300 63700 63900 56800 94700 59600 27600 34300 27800 89200 42000 69500 67800 65600 61700 67300 67200 55900 85900 61600 72700 85800 61800 118000 84100 78200 100200 102100 89200 68500 63900 Y Salary