484 Statistical Methods
c. Create two line plots, one showing
the visitation at Exit Glacier plotted
against year with different lines for
different months, and the second
showing visitation plotted against
month with different lines for differ-
ent years (you will have to create a
two-way table for this). Are there any
unusual values? How might the June
1994 data infl uence future visitation
forecasts?
d. Calculate the seasonally adjusted
values for visits to the park. Is there
a particular month in which visits to
the park jump to a new and higher
level?
e. Smooth the visitation data using ex-
ponential smoothing. Use smoothing
constants of 0.15 for both the location
and the linear parameters, and use
0.05 for the seasonal parameter. Fore-
cast the visitation 12 months into the
future. What are the projected values
for the next 12 months?
f. A lot of weight of the projected visita-
tions for 1994–1995 is based on the
jump in visitation in June 1994.
Assume that this jump was an aber-
ration, and refi t two exponential
smoothing models with 0.05 and 0.01
for the location parameter (to reduce
the effect of the June 1994 increase),
0.15 for the linear parameter, and 0.05
for the seasonal parameter. Compare
your results with your fi rst forecasts.
How do the standard errors compare?
Which projections would you work
with and why? What further informa-
tion would you need to decide be-
tween these three projections?
g. What problems do you see with either
forecasted value? (Hint: Look at the
confi dence intervals for the forecasts.)
h. Save your changes to the workbook
and write a report summarizing your
observations.- The visitation data in the Visit workbook
cover a wide range of values. It might
be appropriate to analyze the log 10 of
the visitation counts instead of the raw
counts.
a. Open the Visit workbook from the
Chapter11 folder and save it as Visit
Log Analysis.
b. Create a new column in the workbook
of the log 10 counts of the Exit Glacier
data (use the Excel function log 10 ).
c. Create a line plot of log 10 (visitation)
for the Exit Glacier site from 1990 to
mid-1994. What seasonal values does
this chart reveal that were hidden
when you charted the raw counts?
d. Use exponential smoothing to smooth
the log 10 (visitation) data. Use a value
of 0.15 for the location and linear ef-
fects, and use 0.05 for the seasonal
effect. Project log 10 (visitation) 12
months into the future. Untransform
the projections and the prediction
intervals by raising 10 to the power
of log 10 (visitation) [that is, if log 10
(visitation) = 1.6, then visitation =
10 1.6 = 39.8]. What do you project for
the next year at Exit Glacier? What are
the 95% prediction intervals? Are the
upper and lower limits reasonable?
e. Redo your forecasts, using 0.01 and
then 0.05 for the location parameter,
0.15 for the linear parameter, and 0.05
for the seasonal parameter. Which of
the three projections results in the
smallest standard error?
f. Compare your chosen projections
from Exercise 8, using the raw counts,
with your chosen projections from
this exercise, using the log 10 trans-
formed counts. Which would you use
to project the 1994–1995 visitations?
Which would you use to determine
the amount of personnel you will
need in the winter months and why?