EXAM 1



Name _______________________________________

EDP/EPE 660 Exam 2

Take-Home Component {34 points}

Directions: You are being provided with an example of a data set, with a brief description. The full data set is posted on the website in an excel worksheet. Complete the specific tasks using Minitab (remember to enable commands) or other statistical software (pre-approved by the instructor). All analyses should be complete prior to the in-class exam, Monday, May 19th at 9:30AM. Clean output (no written comments) should be brought to the in-class exam, as you will use your output to answer additional in-class questions. Both components will be submitted at the end of the exam period. You are to work independently and any violation will result in a grade 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 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 data set (reduced) that you will be working with is posted as Take Home 2.

|Vendor Number |Annual Earnings |Age |Hours worked per day |Gender |

|21 |2841 |29 |12 |M |

|53 |1876 |21 |8 |F |

|263 |3065 |40 |11 |M |

|281 |3670 |50 |11 |F |

a. For each variable above, use Minitab to describe it. You may use descriptive statistics, a graphical summary or even a frequency table. Choose descriptive statistics with level of measurement 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}

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 necessary interaction in Minitab). (Include the ANOVA table.) {4 points}

g. Run a regression analysis to fit the quadratic model for estimating mean annual earnings as a function of age (x1) and hours worked2 (x2)2 (first create the squared term in Minitab.) {4 points}

h. Compute the regression equation, R-square and coefficient estimates for the complete second order model, [pic], for estimating mean annual earnings as a function of age (x1) and hours worked (x2). First create the necessary interaction and squared terms in Minitab. (Include the ANOVA table) {6 points}

i. Create a dummy (indicator) variable for Gender. Compute the first order least-squares regression equation, R-square and coefficient estimates for estimating mean annual earnings as a function of hours worked (x2) and gender (x3 ). (Include the ANOVA table.) {5 points}[pic]

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