Journal Project Part A: Please use the given information to answer the following questions. (Show your work) 1. According to the team marketing report quoted by Dallas Morning News, the average ticket price was up 4.7 percent for last year; show how they obtained a 4.7 % increase. 2. Further, is the 4.7 % increase accurate? If not, what should be the correct percentage increase? Part B: Use the given information to answer the following questions Sample Data for Exports. Period Foreign Income Relative Price 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 305 130 189 175 101 269 421 195 282 203 35 98 83 76 93 77 44 57 31 92 Exchange-rate Risk 35 22 27 16 28 46 56 12 40 32 Export Y 20 15 17 9 16 27 35 7 22 23 Title: Determinants of Exports 1. Run a multiple regression using the above data. (with Export as the dependent variable). 2. Compare the result of question (1) with another regression equation obtained without the Foreign Income variable. 3. Which of the models do you prefer? Why? 4. Interpret your results for (1) and (2). 5. In writing your paper, you should start by indicating the purpose of the study. Also, discuss the methodology and conclusions. Part B requirement: The layout and format of the paper should include the following sections: Title page, Abstract, Introduction, Method, Results, and Discussion. a) Abstract is a very concise summary of the paper. b) The Introduction tells the reader about the topic. Specifically, it should start with the purpose of this paper is to examine.... What the issue is, what is known about it, and the specific focus? Put a business context to it -- write the value added by your work or what businesses can gain from knowledge of the determinants of Exports. c) Empirical Results you should start with a brief discussion of the descriptive statistics for each variable. The measures of central tendency and variability will suffice -- of course, with some discussions. Interpretations of empirical results of the estimated parameters and other indicators such as 2 , 2 , standard error of regression equation, etc. d) Method you should be able to explain what method you are using for your work. For example, you can start with telling the reader that the ordinary least squares (OLS) method was used to obtain estimated coefficients... (Then write more). You should be able to write the equation from the Excel result. We expect to see you... 1. Using the excel result to generate the equation. 2. Find the standard error of each variable. 3. Find the t-stat of each variable. 4. Find the p-value of each variable. EXAMPLE: = 1.24 + 1.71x1 - 0.83x2 - 2.12x3 (6.79) (1.43) (0.22) (0.85) T-stat [0.18] [1.20] [3.78] [2.48] P-value 0.857 0.247 0.002 0.025 e) The results section should tell what was found from the computed data. Use Chapters 4, 10, and 11 to help you test various hypothesis equations. EXAMPLE: H0 = 1 = 2 = 3 = 0 Ha = 1 2 3 0, or all variables are 0 OR H0 i = 0 Ha i 0 (Note: Remember to mention what is used in your hypothesis testing) f) The Discussion describes rigorously what your findings mean or imply in the light of the information presented in the introduction. It is the interpretive segment of the paper and loops back to answer the issues raised in the introduction. Remember to answer questions 2 and 3. **The Project part B should contain 4-5 pages plus computer printouts** Due Date: Friday, April 22, 2016 by noon (Central Time) into the Dropbox at e-college. ** You can use EXCEL or any other software to do this work. ** You need to include your printout in your submissions. You should save you file as \"ECO578P_Lastname_Firstname\" **If you have multiple file, please put 1, 2, ... after your First name. Part A Team Arizona Atlanta Baltimore Buffalo Carolina Chicago Cincinnati Dallas Denver Detroit Green Bay Indianapolis Jacksonville Kansas City Miami Minnesota New England New Orleans NY Giants NY Jets Oakland Philadelphia Pittsburgh St. Louis San Diego San Francisco Seattle Tampa Bay Tennessee Washington NFL Average Calculated Average Average Growth Rate Average Growth Rate Part B 1998 39.65 32.15 42.93 37.26 55.47 38.18 37.77 43.48 35.83 35.86 36.51 34.15 56.71 41.98 42.16 33.05 39.45 34.47 40.91 35.5 52.84 37.59 35.76 33.98 53.87 50 33.95 64.58 45.11 74.28 42.86 42.51 1997 39.65 31.49 37.44 35.58 55.47 38.18 34.09 43.48 35.83 35.43 36.51 34.15 54.24 38.02 42.16 33.05 39.45 30.64 40.91 30.5 52.84 37.59 35.76 33.98 53.87 45 32.65 35.46 40.75 74.28 40.93 40.28 4.715368 % 4.715368 % Foreign Income Period X1 1970 305 1971 130 1972 189 1973 175 1974 101 1975 269 1976 421 1977 195 1978 282 1979 203 5.54% % change using log ExchangeRelative rate Risk Price X2 X3 Export Y 35 35 20 98 22 15 83 27 17 76 16 9 93 28 16 77 46 27 44 56 35 57 12 7 31 40 22 92 32 23 SUMMARY OUTPUT Regression Statistics Multiple R 0.9923987915 R Square 0.9848553615 Adjusted R Square 0.9772830422 Standard Error 1.249867779 Observations 10 ANOVA df Regression Residual Total Intercept Foreign Income X1 Relative Price X2 Exchange-rate Risk X3 SS 3 609.5269832094 6 9.3730167906 9 618.9 MS F 203.1756610698 130.05993627 1.5621694651 Coefficients Standard Error -10.1702433155 3.4731285927 0.0270381153 0.0120409964 0.097052345 0.0301471217 0.5246751681 0.0591582486 t Stat P-value -2.9282656959 0.0263464283 2.245504802 0.0658468117 3.2192905781 0.0181533886 8.8690111744 0.0001143334 RESIDUAL OUTPUT Observation 1 2 3 4 5 6 7 8 9 10 Predicted Export Y 19.8368448056 14.39869518 17.1615346474 10.3322077695 16.2773791201 28.7110979942 34.864915814 6.9302748479 21.4501346144 21.0369152069 Residuals Standard Residuals 0.1631551944 0.1598757012 0.60130482 0.5892183212 -0.1615346474 -0.158287728 -1.3322077695 -1.3054297908 -0.2773791201 -0.2718036743 -1.7110979942 -1.6767041506 0.135084186 0.1323689328 0.0697251521 0.068323645 0.5498653856 0.5388128426 1.9630847931 1.9236259009 Significance F 7.555161E-006 Lower 95% Upper 95% Lower 95.0% Upper 95.0% -18.66868283 -1.6718038014 -18.66868283 -1.6718038014 -0.0024251416 0.0565013721 -0.0024251416 0.0565013721 0.0232849955 0.1708196945 0.0232849955 0.1708196945 0.3799201484 0.6694301877 0.3799201484 0.6694301877 SUMMARY OUTPUT Regression Statistics Multiple R 0.9859655574 R Square 0.9721280803 Adjusted R Square 0.9641646747 Standard Error 1.5698012203 Observations 10 ANOVA df Regression Residual Total Intercept Relative Price X2 Exchange-rate Risk X3 SS 2 601.6500689004 7 17.2499310996 9 618.9 MS F 300.8250344502 122.07441462 2.4642758714 Coefficients Standard Error -3.7583583133 2.4832522808 0.0431137431 0.0228799281 0.6337820234 0.0423843528 t Stat P-value -1.5134822758 0.1739235067 1.8843478394 0.101515211 14.9532075202 1.43539E-006 RESIDUAL OUTPUT Observation 1 2 3 4 5 6 7 8 9 10 Predicted Export Y 19.9329935147 14.4099930285 16.9321969983 9.6587985392 17.9971164531 28.7153729836 33.6304396938 6.3045093261 22.929448659 20.4891308035 Residuals Standard Residuals 0.0670064853 0.048399894 0.5900069715 0.4261718066 0.0678030017 0.048975231 -0.6587985392 -0.4758610953 -1.9971164531 -1.4425502884 -1.7153729836 -1.2390423145 1.3695603062 0.9892560905 0.6954906739 0.5023644318 -0.929448659 -0.6713561591 2.5108691965 1.8136424033 Significance F 3.614798E-006 Lower 95% Upper 95% Lower 95.0% -9.6303168792 2.1136002526 -9.6303168792 -0.0109886898 0.0972161761 -0.0109886898 0.5335589548 0.7340050919 0.5335589548 Upper 95.0% 2.1136002526 0.0972161761 0.7340050919