1. Question #2 - Page # 88, Use data P02_03.xlsx.
The file P02_03.xlsx contains data from a survey of 399
people regarding a government environmental policy.
a. Create a crosstabs and an associated column chart for
Gender versus Opinion. Express the counts as percentages
so that for either gender, the percentages add
to 100%. Discuss your findings. Specifically, do the
two genders tend to differ in their opinions about the
b. Repeat part a with Age versus Opinion.
c. Recode Salary to be categorical with categories “Less
than $40K,” “Between $40K and $70K,” “Between $70K
and $100K,” and “Greater than $100K” (where you can
treat the breakpoints however you like). Then repeat
part a with this new Salary variable versus Opinion.
2. Question #20 - Page #96, Use data P02_56.xlsx
The file P02_56.xlsx contains monthly values of indexes
that measure the amount of energy necessary to heat or cool
buildings due to outside temperatures. (See the explanation
in the Source sheet of the file.) These are reported for each
state in the United States and also for several regions, as
listed in the Locations sheet, from 1931 to 2000.
a. For each of the Heating Degree Days and Cooling
Degree Days sheets, create a new Season variable
with values “Winter,” “Spring,” “Summer,” and “Fall.”
Winter consists of December, January, and February;
Spring consists of March, April, and May; Summer
consists of June, July, and August; and Fall consists of
September, October, and November.
b. Find the mean, median, and standard deviation
of Heating Degree Days (HDD), broken down by
for the 48 contiguous states location (code
5999). (Ignore the first and last rows for the given location,
the ones that contain -9999, the code for missing
Also, create side-by-side box plots of HDD,
broken down by season. Comment on the results. Do they go in the direction you would expect? Do the
same for Cooling Degree Days (which has no missing data).
c. Repeat part b for California (code 0499).
d. Repeat part b for the New England group of states
3. Question #30 - Page #105, Use data P03_30.xlsx
rates of various currencies versus the U.S. dollar. It is of
interest to financial analysts and economists to see whether
exchange rates move together through time. You could find
the correlations between the exchange rates themselves,
but it is often more useful with time series data to check for
correlations between differences from day to day.
a. Create a column of differences for each currency.
b. Create a table of correlations between all of the original
variables. Then on the same sheet, create a second
table of correlations between the difference variables.
On this same sheet, enter two cutoff values, one positive
such as 0.6 and one negative such as 20.5, and
use conditional formatting to color all correlations (in
both tables) above the positive cutoff green and all
correlations below the negative cutoff red. Do it so
that the 1’s on the diagonal are not colored.
c. Based on the second table and your coloring, can you
conclude that these currencies tend to move together
in the same direction? If not, what can you conclude?
d. Repeat part c for Ratio2.
1. Question #49 - Page #125, Use data P03_22.xlsx
The file P03_22.xlsx lists financial data on movies
released from 1980 to 2011 with budgets of at least
a. Create three new variables, Ratio1, Ratio2, and
Decade. Ratio1 should be US Gross divided by
Budget, and Decade should list 1980s, 1990s, or
2000s, depending on the year of the release date.
If either US Gross or Worldwide Gross is listed as
“Unknown,” the corresponding ratio should be blank.
(Hint: For Decade, use the YEAR function to fill in a
new Year column. Then use a lookup table to populate
the Decade column.)
b. Use a pivot table to find counts of movies by various
distributors. Then go back to the data and create one
more column, Distributor New, which lists the distributor
for distributors with at least 30 movies and lists
Other for the rest. (Hint: Use a lookup table to populate
Distributor New, but also use an IF to fill in Other
where the distributor is missing.)
c. Create a pivot table and corresponding pivot chart
that shows average and standard deviation of Ratio1,
broken down by Distributor New, with Decade in the
Filters area. Comment on any
d. Repeat part c for Ratio2.
1. Question #63 - Page #129, Use data P03_63.xlsx
63. The file P03_63.xlsx contains financial data on 85
U.S. companies in the Computer and Electronic Product
Manufacturing sector (NAICS code 334) with 2009
earnings before taxes of at least $10,000. Each of these
companies listed R&D (research and development)
expenses on its income statement. Create a table of correlations
between all of the variables and use conditional
formatting to color green all correlations involving
R&D that are strongly positive or negative. (Use cutoff
values of your choice to define “strongly.”) Then create
scatterplots of R&D (Y axis) versus each of the other
most highly correlated variables. Comment on any patterns
you see in these scatterplots, including any obvious
outliers, and explain why (or if) it makes sense that
these variables are highly correlated with R&D. If there
are highly correlated variables with R&D, can you tell
which way the causality goes?