Select a realistic and interesting
data set consisting of a sample of approximately 50
observations (n = 50). Your data set should include a
quantitative dependent variable (Y), and two independent (X
 predictor) variables, one quantitative (QN) and one
qualitative (QL).
Example:
Y = Salary of USF
Professor
QN = Years of Experience
QL = Gender ( 1 = Male; 0 = Female)
Please select a qualitative variable
with exactly twolevels (male/female; inseason/offseason;
large cap fund/small cap fund). Also, try to select about
the same number of data points for each level, for example,
25 males and 25 females.
1. In an Excel Spreadsheet,
enter the QN data in a column, create a column for
curvature (QN^2, where "^" represents "squared") data,
enter the QL data in a column, create a column for QN*QL
interaction (where "*" indicates multiple respective QN
data times QL data, and enter Y data in a column. Note:
all independent variables should be contiguous or
adjacent to each other).
2. Using the data analysis tool,
build and test at least two of the following models, in
the process of determining your "best" model. For each
test, be able to state the null and alternate hypotheses,
the hypothesized regression equation associated with the
null and alternate hypotheses, the appropriate pvalue,
the null hypothesis decision (reject or do not reject the
null hypothesis), and the resulting conclusion (e.g.,
curvature is important, interaction is not important or
not significant). A flowchart to guide model testing is
provided in Item 4.
Model 1: E(Y) = B_{0} +
B_{1} QN + B_{2} QN^{2} +
B_{3} QL + B_{4} QN*QL
Model 2: E(Y) = B_{0} +
B_{1 }QN + B_{3} QL + B_{4
}QN*QL
Model 3: E(Y) = B_{0} +
B_{1} QN + B_{2} QN^{2} +
B_{3} QL
Model 4: E(Y) = B_{0} +
B_{1 }QN + B_{2} QN^{2
}
Model 5: E(Y) = B_{0} +
B_{1 }QN + B_{3} QL
Model 6: E(Y) = B_{0} +
B_{3} QL
Model 7: E(Y) = B_{0} +
B_{1} QN
3. Using the data analysis
regression tool, build Model 1.
4. Test curvature (Model 1 vs. 2).
A. If curvature is
significant, test interaction (Model 1 vs. 3).
(1). If interaction is
significant, stop. Model 1 is "best" model. Go to
Item 5.
(2). If interaction is not
significant, Build Model 3 and test QL (Model 3 vs.
4).
a. If QL is
significant, stop. Model 3 is "best" model. Go
to Item 5.
b. If QL is not
significant, build Model 4 and stop. Model 4 is
"best" model. Go to Item 5.
B. If curvature is not
significant, build Model 2 and test interaction (Model
2 vs. 5).
(1). If interaction is
significant, stop. Model 2 is "best" model. Go to
item 5.
(2). If interaction is not
significant, build Model 5 and test QL (Model 5 vs.
7).
a. If QL is
significant, test QN (Model 5 vs. 6).
1. If QN
is significant, stop. Model 5 is "best"
model. Go to Item 5.
2. If QN is not
significant, stop. Model 6 is "best model. Go
to Item 5.
b. If QL is not
significant, stop and select Model 7 as "best"
model, even if the Model is not significant. Go
to Item 5.
 5. Rerun the data analysis
regression tool for your "best" model, and include and be
able to describe or interpret the following printouts:
 Residual plot :
 QN (Model 4 or
7)
 QL Model 6)
 QN and QL (Models 1, 2, 3,
or 5)
 Normal probability plot (for
all Models)
 Fitted Line Plot:
 QN^{2} (Models 1, 3,
4)
 QN (Models 1, 2, 3, 4, 5,
7)
 QL (Models 1, 2, 3, 5, 6)
6. Be able to demonstrate your
knowledge of the learning objectives as applied to this
Assignment in Exam 3.
7. (Optional) Send Microsoft
Excel file, with Items 1  5, as an attachment in an
email to the instructor if you wish the instructor to
review/give feedback on your work. This should be done
not later than November 1, 2001 if you wish to
receive instructor feedback. You may send parts of the
assignment as you finish them, if you wish.
8. Notify the instructor, via
email, when you have completed Assignment 3, and wish to
take Exam 3. Include Assignment 3 as an attachment to
your email if you did not do item 7 above. This needs to
be done BEFORE Nov 3, 2001. Email or fax
completed Exam 3 back to the instructor NOT LATER THAN
NOV 3, 2001.

Anderson, D., Sweeney, D., &
Williams, T. (2001). Contemporary Business Statistics with
Microsoft Excel. Cincinnati, OH: SouthWestern.
Chapter 12 (Section 12.9).
