Name _______________________________________EDP 660 EXAM 2TAKE HOME COMPONENTDirections: You are being provided with examples of four data sets, each with a brief description. Fulldata sets will be posted on the webpage and sent to you via email. For each data set, complete thespecific tasks using Minitab or other statistical software (pre-approved by the instructor). All analysesshould be complete prior to the in-class exam, Thursday, October 26th at 9:30AM. Clean output (nowritten comments) should be brought to the in-class exam, as you will use your output to answeradditional in-class questions. Both components will be submitted at the end of the exam period. You areto work independently and any violation will result in a grade of 0 on the take-home portion. Contact mewith questions. Good luck!1. Detailed interviews were conducted with over 1,000 street vendors in the city of Puebla, Mexico, in order to study the factors influencing vendors’ incomes (World Development, Feb. 1998). Vendors were defined as individuals working in the street, and included vendors with carts and stands on wheels and excluded beggars, drug dealers, and prostitutes. The researchers collected data on gender,age, hours worked per day, annual earnings, and education level. A subset of these data appears in the table; the full data set is posted as Take Home Item 1. a. Compute the first order least-squares regression equation, R-square, coefficient estimates, and standardized residuals for estimating mean annual earnings as a function of age (x1) and hours worked (x2) (Include the ANOVA table.) {3 points}b. Compute the regression equation, R-square and coefficient estimates and standardized residuals for the complete second order model for estimating mean annual earnings as a function of age (x1) and hours worked (x2). Be sure to include both the interaction and quadratic terms in the model. (Include the ANOVA table) {5 points}c. Test the hypothesis that the interaction and quadratic terms in the model are not necessary forpredicting annual earnings. {5 points} (Write this on a separate sheet and attach to your output.)d. Create a dummy (indicator) variable for Gender. Compute the first order least-squares regression equation, R-square, coefficient estimates, and standardized residuals for estimating mean annual earnings as a function of hours worked (x2) and gender (x3 ). (Includethe ANOVA table.) {4 points}e. Based on the above analyses, select the most parsimonious model. For this model, produce diagnostic measures (Standardized Residuals, Leverages, Cook’s D, DFFITS). {4 points}2.VendorNumberAnnualEarningsAgeHours worked perdayGender21 2841 29 12 M53 1876 21 8 F60 2934 62 10 M184 1552 18 10 F263 3065 40 11 M281 3670 50 11 F3. In any production process in which one or more workers are engaged in a variety of tasks, the total time spent in production varies as a function of the size of the work pool and the level of output of the various activities. For example, in a large metropolitan department store, the number of hours worked y per day by the clerical staff may depend on the following variables:x1 = Number of pieces of mail processed (open, sort, etc.)x2 = Number of money orders and gift certificates soldx3 = Number of window payments (customer charge accounts) transactedx4 = Number of change order transactions processedx5= Number of checks cashedx6= Number of pieces of miscellaneous mail processed on an “as available” basisx7 = Number of bus tickets soldThe table below gives a portion of the output counts for these activities on each of 52 working days. Thefull data set is posted as Take Home Item 2.a. Conduct a stepwise regression analysis of the data. Set the alpha to enter and/or exit at .05 (inthe Methods menu.) {5 points}Observation Number Day of Week y x1 x2 x3 x4 x5 x6 x71 M 128.5 7781 100 886 235 644 56 7372 T 113.6 7004 110 962 388 589 57 10293 W 146.6 7267 61 1342 398 1081 59 8304 Th 124.3 2129 102 1153 457 891 57 14685 F 100.4 4878 45 803 577 537 49 3356 S 119.2 3999 144 1127 345 563 64 9187 M 109.5 11777 123 627 326 402 60 3358 T 128.5 5764 78 748 161 495 57 9629 W 131.2 7392 172 876 219 823 62