Data analysis in business Tutorial 10

Question A
A leading textile manufacturer wants to the productivity of its workers. The manager has observed
that inexperienced workers perform quite poorly, but they usually improve quickly. However, very
experienced workers do not perform as well as expected. He attributes this phenomenon to boredom—
more experienced workers grow weary of the monotonous work and become less productive. In an
attempt to learn more about productivity and experience, he took a random sample of workers with
varying years of experience (variable name: years) and counted the number of units each worker
produced in a day (variable name: units). The data are stored in the EViews workfile T10_textile.wf1.
The manage intends to use the following quadratic model to capture the non-linear relationship
between experience and productivity.
units = 0 + 1years + 2years2 + (1)

1. Given the managers observation, what sign do you think 2 will have ?
2. Estimate Equation (1) and write down the fitted model.
   Question B
   The number of car accidents on a particular stretch of highway seems to be related to the number of
   vehicles that travel over it and the speed at which they are traveling. A city councillor has decided to
   examine the data statistically so that he can (if possible) introduce new speed laws that will reduce
   traffic accidents. He randomly selects 60 days and for each day recorded the number of accidents (var.
   name: acc), the number of cars passing along a stretch of road (var. name: cars) and their average
   speeds in kilometers per hour (var. name: speed). The observations are in T10_accidents.wf1.
3. Estimate a first order model (without interaction) and interpret the results.
4. Estimate a model with interaction.
5. From Eviews output, is there any interaction effect between cars and speed?
6. Compare and interpret the regression output from Question B1 and B2.
   Question C
   An economist wants to analyze the effect of race on the salary of major league baseball players while
   controlling for other factors that measure aspects of player productivity and longevity. She obtained
   the data on players salary in thousands of dollars (variable name: salary), years in major leagues
   1
   (variable name: years), games per year in league (variable name: gamesyr), percentage of years an
   all-star (variable name: allstar). The data also includes a variable black that is a binary indicators
   for the individual players (the base group is white players). The variable percblck is the percentage
   of the team’s city that is black.
   Estimate the following model using the data for 270 players.
   salary=0 + 1years + 2gamesyr + 2allstar

• 2black + 2percblck + 3black percblck +

1. Write down the fitted model.
2. From the estimated model, what is the effect of two extra years in the league on the expected
   salary, holding all other factors constant?
3. From the estimated model, are black players paid less than the white players?
   2

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