| Objective: Use the Gretl program to replicate
Example 3.2 on pp. 76-84 using the woody3.csv
data file. The data file contains information for 33 restaurants
on Y (sales), NN (# of nearby competitors), P (local population) and I
(average local family income).
I. Programming: Click here
for programming directions. Note: this data is cross-sectional data,
i.e., it's measured across restaurants at a point of time.
II. Assignment:
a. Generate descriptive statistics (means, standard deviations,
minimums and maximums) for each variable. Briefly discuss.
b. Run a regression of Y on NN, P and I. Print out
the regression results, including predicted values and residuals.
c. What regression "model" are you estimating? Specifically,
what kind of relationship are you assuming exists between which variables?
Describe verbally and algebraically.
d. Specifically, what do the observed Y, predicted Y, and residual
columns tell you? How are they computed?
e. Interpret the estimated coefficients.
f. How could the regression results be used to predict
restaurant sales? Give an example.
g. What does the R squared value measure? the adjusted
R squared? Which should be used?
h. Compute the standardized coefficients and elasticities.
What do they show? |