390 Statistical Methods
a. Open the Temperatures workbook
from the Chapter09 folder and save it
as Temperatures Regression.
b. Create a chart sheet containing a
scatterplot of latitude vs. longitude.
Modify the scales for the horizontal
and vertical axes to go from 60 to
120 degrees in longitude and from
20 to 50 degrees in latitude. Reverse
the orientation of the x-axis so that
it starts from 120 degrees on the left
and goes down to 60 degrees on the
right. Add labels to the points, show-
ing the temperature for each city.
c. Construct a regression model that re-
lates average temperature to latitude
and longitude.
d. Examine the results of the regression.
Are both predictor variables statis-
tically signifi cant at the 5% level?
What is the R^2 value? How much of
the variability in temperature is ex-
plained by longitude and latitude?
e. Format the regression values gener-
ated by the Analysis ToolPak to dis-
play residual values as integers. Copy
the map chart from part b to a new
chart sheet, and delete the tempera-
ture labels. Now label the points us-
ing the residual values.
f. Interpret your fi ndings. Where are the
negative values clustered? Where do
you usually fi nd positive residuals?
g. Write a report summarizing your
fi ndings, discussing where the linear
model fails and why.- The Housing Price workbook contains
information on home prices in
Albuquerque, New Mexico.
a. Open the Housing Price workbook
from the Chapter09 folder and save it
as Housing Price Regression.
b. Regress the price of the houses in the
sample on three predictor variables:
Square Feet, Age, and number of
features.
c. Examine the plot of residuals versus
predicted values. Is there any viola-
tion of the regression assumptions
evident in this plot?
d. Redo the regression analysis, this time
regressing the Log Price on the three
predictor variables. How does the plot
of residuals versus predicted values
appear in this model? Did the loga-
rithm correct the problem you noted
earlier?
e. There is an outlier in the plot. Iden-
tify the point and describe what this
tells us about the price of the house if
the model is correct.
f. Save your changes to the workbook
and write a report summarizing your
observations.- The Unemployment workbook contains
the U.S. unemployment rate, Federal
Reserve Board index of industrial
production, and year of the decade
1950–1959. Unemployment is the de-
pendent variable; Industrial Production
and Year of the Decade are the predictor
variables.
a. Open the Unemployment workbook
from the Chapter09 folder and save it
as Unemployment Regression.
b. Create a chart sheet showing the scat-
ter plot of Unemployment versus
FRB_Index. Add a linear trend line to
the chart. Does unemployment appear
to rise along with production?
c. Using the Analysis ToolPak, run a
simple linear regression of Unemploy-
ment versus FRB_Index. What is the
regression equation? What is the R^2
value? Does the regression explain
much of the variability in unemploy-
ment values during the 1950s?
d. Rerun the regression, adding Years to
the regression equation. How does the
R^2 value change with the addition of
the Years factor? What is the regres-
sion equation?