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. Period 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 Foreign Income X1 305 130 189 175 101 269 421 195 282 203 Relative Price X2 35 98 83 76 93 77 44 57 31 92 Exchange-rate X3 35 22 27 16 28 46 56 12 40 32 Export Y (dependent variable) 20 15 17 9 16 27 35 7 22 23 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9923987915 0.9848553615 0.9772830422 1.249867779 10 ANOVA df 3 6 9 SS 609.5269832094 9.3730167906 618.9 MS 203.1756610698 1.5621694651 Coefficients -10.1702433155 0.0270381153 0.097052345 0.5246751681 Standard Error 3.4731285927 0.0120409964 0.0301471217 0.0591582486 t Stat -2.9282656959 2.245504802 3.2192905781 8.8690111744 2 7 9 SS 601.6500689004 17.2499310996 618.9 MS 300.8250344502 2.4642758714 Coefficients -3.7583583133 0.0431137431 0.6337820234 Standard Error 2.4832522808 0.0228799281 0.0423843528 Regression Residual Total Intercept Foreign Income X1 Relative Price X2 Exchange-rate X3 F Significance F 130.0599362677 7.5551613656E-006 P-value 0.0263464283 0.0658468117 0.0181533886 0.0001143334 Lower 95% -18.6686828296 -0.0024251416 0.0232849955 0.3799201484 Upper 95% -1.6718038014 0.0565013721 0.1708196945 0.6694301877 Lower 95.0% -18.6686828296 -0.0024251416 0.0232849955 0.3799201484 Upper 95.0% -1.6718038014 0.0565013721 0.1708196945 0.6694301877 Upper 95% 2.1136002526 0.0972161761 0.7340050919 Lower 95.0% -9.6303168792 -0.0109886898 0.5335589548 Upper 95.0% 2.1136002526 0.0972161761 0.7340050919 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9859655574 0.9721280803 0.9641646747 1.5698012203 10 ANOVA df Regression Residual Total Intercept Relative Price X2 Exchange-rate X3 F Significance F 122.0744146159 3.6147984480E-006 t Stat P-value -1.5134822758 0.1739235067 1.8843478394 0.101515211 14.9532075202 1.43539479484768E-006 Lower 95% -9.6303168792 -0.0109886898 0.5335589548