Data analysis in business Tutorial 9

Question A
Retailers need their stores to be re-stocked on time so they never run out of products. A manager
wants to determine the factors that affect how long it takes to unload delivery trucks. He hypothesized
that how long it took to unload (variable name time measured in minutes) is affected by the total
number of boxes (variable name boxes), the total weight of those boxes in hundreds of kilos (variable
name weight), and the time of day when the truck arrives (variables name arrival). He had a random
sample taken of 50 deliveries to one of his stores and these data are stored in T9_delivery.wf1.

1. The variable arrival is qualitative with three categories coded as follows
   arrival =
   8><

:
1 if morning
2 if afternoon
3 if evening
Transform the variable to two dummies using evening (arrival = 3) as the reference category
(hint, type in group dum @expand(arrival,@droplast) in Eviews).

2. Estimate the population model
   dtime = 0 + 1 boxes + 2 weight + 3 morning + 4 afternoon
   and write down the fitted model.
3. Interpret the estimated value for coefficients 1 and 2.
4. Interpret the estimated value for coefficients 3 and 4.
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   Question B
   A real estate agent wanted to develop a model to predict the selling price of a home. The agent
   believed that the most important variables in determining the price of a house are its size, number of
   bedrooms and lot size and that house prices are linearly related to the three variables.
   He took a sample of 100 homes that were recently sold and recorded the selling price (Price),
   the number of bedrooms (Bedrooms), the house size in square meters (H_Size) and the lot size in
   square meters (L_Size).
   These data are stored in the EViews workfile T9_house.wf1.
5. Write down the population regression model for the response variable Price that reflects the
   belief of the real estate agent.
6. Use EViews to estimate the population model and write down the fitted model (round to 4
   significant digits).
7. From Eviews output, test the null that H_Size has no effect on Price against a two -sided
   alternative at 5% significance level. Write down the 5 steps of hypothesis testing.
8. From Eviews output, can you reject the null that Bedrooms has no effect on Price at the 5%
   level? How about L_size? You don’t need write down the 5 steps of hypothesis testing, just the
   conclusion.
9. Test the overall utility of the model. Write down the 5 steps of hypothesis testing.
10. Briefly comment on the results from B3-B5 (hint, compute the correlations between the three
    independent variables).
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