Summer 2008 K. Bradley, Ph.D.Name _______________________________________EDP/EPE 660 Exam 2Take-Home Component {34 points}Directions: You are being provided with an example of a data set, with a brief description. Thefull data set is posted on the website in an excel worksheet. Complete the specific tasks usingMinitab (remember to enable commands) or other statistical software (pre-approved by theinstructor). All analyses should be complete prior to the in-class exam, Monday, May 19th at9:30AM. Clean output (no written comments) should be brought to the in-class exam, as you willuse your output to answer additional in-class questions. Both components will be submitted atthe end of the exam period. You are to work independently and any violation will result in agrade of 0 on the take-home portion. Contact me with questions. Good luck!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 withcarts and stands on wheels and excluded beggars, drug dealers, and prostitutes. Theresearchers collected data on gender, age, hours worked per day, annual earnings, andeducation level. A subset of these data appears in the table; the data set (reduced) that youwill be working with is posted as Take Home 2.a. For each variable above, use Minitab to describe it. You may use descriptive statistics, agraphical summary or even a frequency table. Choose descriptive statistics with level ofmeasurement in mind. {5 points}b. Produce a matrix plot for the interval/ratio variables. {2 points}c. Compute a simple linear regression with the independent variable, hours worked per day,to estimate mean annual earnings (make sure to produce the ANOVA table). {2 points}d. Produce a fitted line plot for the equation produced in (c) with a 95% prediction interval.{3 points}1VendorNumberAnnualEarnings AgeHours workedper day Gender21 2841 29 12 M53 1876 21 8 F263 3065 40 11 M281 3670 50 11 FSummer 2008 K. Bradley, Ph.D.e. Run the analysis as a multiple regression, least-squares regression equation, R-square,and coefficient estimates for estimating mean annual earnings as a function of age (x1)and hours worked (x2). (Include the ANOVA table.) {3 points}f. Re-run the model in (e) so that it includes the interaction term (first create the necessaryinteraction in Minitab). (Include the ANOVA table.) {4 points}g. Run a regression analysis to fit the quadratic model for estimating mean annual earningsas a function of age (x1) and hours worked2 (x2)2 (first create the squared term inMinitab.) {4 points}h. Compute the regression equation, R-square and coefficient estimates for the completesecond order model, 221521423212110)( xxxxxxxyE, for estimatingmean annual earnings as a function of age (x1) and hours worked (x2). First create thenecessary interaction and squared terms in Minitab. (Include the ANOVA table) {6points}i. Create a dummy (indicator) variable for Gender. Compute the first order least-squaresregression equation, R-square and coefficient estimates for estimating mean annualearnings as a function of hours worked (x2) and gender (x3 ). (Include the ANOVA table.){5